diff --git "a/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/dtufc_elic-featurecoding_sd35_individual.log" "b/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/dtufc_elic-featurecoding_sd35_individual.log" new file mode 100644--- /dev/null +++ "b/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/dtufc_elic-featurecoding_sd35_individual.log" @@ -0,0 +1,21862 @@ +Experiment: dtufc_elic-featurecoding_sd35_individual +Log file: output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/dtufc_elic-featurecoding_sd35_individual.log +DTUFCCodecConfig: + arch: elic-featurecoding + handler: sd35 + checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.01_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar + transform_type: kmeans + transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json + bit_depth: 8 + device: cuda:0 +Loading checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.01_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 506 +Loaded elic-featurecoding (1-channel) on cuda:0 +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item0_clip_pooled_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder-item0.clip_pooled_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item0_clip_pooled_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item0_clip_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder-item0.clip_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item0_clip_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item3_clip_pooled_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder-item3.clip_pooled_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item3_clip_pooled_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item3_clip_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder-item3.clip_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item3_clip_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item1_clip_pooled_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_2-item1.clip_pooled_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item1_clip_pooled_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item1_clip_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_2-item1.clip_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item1_clip_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item4_clip_pooled_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_2-item4.clip_pooled' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item4_clip_pooled_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item4_clip_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_2-item4.clip_prompt' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item4_clip_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_3-item2_t5_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_3-item2.t5_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_3-item2_t5_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_3-item5_t5_prompt_embeds.json: torch.Size([256]) +Loaded per-key quantization points for key 'text_encoder_3-item5.t5_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_3-item5_t5_prompt_embeds.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_encoder_vae_encoder_f0.json: torch.Size([256]) +Loaded per-key quantization points for key 'vae.encoder_f0' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_encoder_vae_encoder_f0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_encoder_vae_encoder_f1.json: torch.Size([256]) +Loaded per-key quantization points for key 'vae.encoder_f1' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_encoder_vae_encoder_f1.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_decoder.json: torch.Size([256]) +Loaded per-key quantization points for key 'vae.decoder' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_decoder.json +Loaded per-key mappings: model=sd35 + Keys: ['text_encoder-item0.clip_pooled_prompt_embeds', 'text_encoder-item0.clip_prompt_embeds', 'text_encoder-item3.clip_pooled_prompt_embeds', 'text_encoder-item3.clip_prompt_embeds', 'text_encoder_2-item1.clip_pooled_prompt_embeds', 'text_encoder_2-item1.clip_prompt_embeds', 'text_encoder_2-item4.clip_pooled', 'text_encoder_2-item4.clip_prompt', 'text_encoder_3-item2.t5_prompt_embeds', 'text_encoder_3-item5.t5_prompt_embeds', 'vae.encoder_f0', 'vae.encoder_f1', 'vae.decoder'] +---------------- ------------------------------------------------------------------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +Checkpoint codec_weights/elic_hybrid/elic2022-official_lambda0.01_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features +Output output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond +---------------- ------------------------------------------------------------------------------------------------------------------- +Files found: 100 +---------------------------------------------------------------------- + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002153.zst (1/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002153.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,196B, BPFP=12.4583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 10,408B, BPFP=1.4080 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,712B, BPFP=10.7000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 16,784B, BPFP=1.3623 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 56,760B, BPFP=1.4397 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 39,384B, BPFP=0.6010 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 39,380B, BPFP=0.6009 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 20,168B, BPFP=0.6155 +⌛️ [2/4] FRONTEND: Frontend time: 3.139s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.656s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017200 0.48561128 + text_encoder-item0.clip_prompt_embeds 0.00025464 34.77013367 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020464 0.46949377 + text_encoder_2-item1.clip_prompt_embeds 0.00016240 0.08880132 + text_encoder_3-item2.t5_prompt_embeds 0.00000839 0.00251825 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00635250 1.03789592 + vae.encoder_f1 0.00635834 1.03853095 + vae.decoder 0.00019940 0.03084335 + ------------------------------------------------------------------------------------- + TOTAL 0.00300073 3.63147591 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 246308 +BPFP 0.8715 bits/point +EBPFP 1.7430 equivalent bits/point +MSE 3.631476 +---------------------- -------------------------------------------------------- +Time: 4.802s Load: 0.007s, Pack+Encode: 3.139s, Decode+Unpack: 1.656s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.6315 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002153.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000002153.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002431.zst (2/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002431.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,172B, BPFP=12.2083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 10,100B, BPFP=1.3663 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 17,116B, BPFP=1.3893 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 53,436B, BPFP=1.3554 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 27,592B, BPFP=0.4210 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 27,588B, BPFP=0.4210 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 16,128B, BPFP=0.4922 +⌛️ [2/4] FRONTEND: Frontend time: 2.153s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.606s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020777 0.45190084 + text_encoder-item0.clip_prompt_embeds 0.00022609 34.75806827 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00019887 0.48146625 + text_encoder_2-item1.clip_prompt_embeds 0.00019493 0.12356106 + text_encoder_3-item2.t5_prompt_embeds 0.00000845 0.00343898 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.01130640 1.14544678 + vae.encoder_f1 0.01130902 1.14675188 + vae.decoder 0.00020860 0.02982000 + ------------------------------------------------------------------------------------- + TOTAL 0.00529919 3.68271472 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 215400 +BPFP 0.7621 bits/point +EBPFP 1.5243 equivalent bits/point +MSE 3.682715 +---------------------- -------------------------------------------------------- +Time: 3.768s Load: 0.008s, Pack+Encode: 2.153s, Decode+Unpack: 1.606s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.6827 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002431.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000002431.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000003661.zst (3/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000003661.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,256B, BPFP=13.0833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,428B, BPFP=1.1402 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,956B, BPFP=12.2250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,212B, BPFP=1.2347 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 48,916B, BPFP=1.2408 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 14,896B, BPFP=0.2273 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 14,896B, BPFP=0.2273 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 11,664B, BPFP=0.3560 +⌛️ [2/4] FRONTEND: Frontend time: 2.152s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.603s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020323 0.44006622 + text_encoder-item0.clip_prompt_embeds 0.00022402 360.09486607 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00024964 0.55094485 + text_encoder_2-item1.clip_prompt_embeds 0.00015987 0.08537365 + text_encoder_3-item2.t5_prompt_embeds 0.00000778 0.00320146 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 1.19630027 3.70893049 + vae.encoder_f1 1.19630098 3.71125603 + vae.decoder 0.00023596 0.02635288 + ------------------------------------------------------------------------------------- + TOTAL 0.55486265 13.37889749 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 177740 +BPFP 0.6289 bits/point +EBPFP 1.2578 equivalent bits/point +MSE 13.378897 +---------------------- -------------------------------------------------------- +Time: 3.763s Load: 0.007s, Pack+Encode: 2.152s, Decode+Unpack: 1.603s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 13.3789 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000003661.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000003661.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000011149.zst (4/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000011149.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,224B, BPFP=12.7500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,768B, BPFP=1.3214 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,764B, BPFP=11.0250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 17,308B, BPFP=1.4049 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 52,436B, BPFP=1.3301 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 34,348B, BPFP=0.5241 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 34,344B, BPFP=0.5240 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 25,740B, BPFP=0.7855 +⌛️ [2/4] FRONTEND: Frontend time: 2.153s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.608s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018694 0.45499655 + text_encoder-item0.clip_prompt_embeds 0.00030342 23.84586800 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00066702 0.46467714 + text_encoder_2-item1.clip_prompt_embeds 0.00020355 0.10296512 + text_encoder_3-item2.t5_prompt_embeds 0.00000815 0.00273535 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00586287 0.83930212 + vae.encoder_f1 0.00587438 0.83824712 + vae.decoder 0.00017677 0.04747233 + ------------------------------------------------------------------------------------- + TOTAL 0.00277565 3.25582214 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 237448 +BPFP 0.8402 bits/point +EBPFP 1.6803 equivalent bits/point +MSE 3.255822 +---------------------- -------------------------------------------------------- +Time: 3.770s Load: 0.008s, Pack+Encode: 2.153s, Decode+Unpack: 1.608s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.2558 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000011149.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000011149.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000023937.zst (5/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000023937.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,168B, BPFP=12.1667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,296B, BPFP=1.1223 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,824B, BPFP=11.4000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,856B, BPFP=1.2058 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 50,096B, BPFP=1.2707 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 24,868B, BPFP=0.3795 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 24,860B, BPFP=0.3793 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 13,896B, BPFP=0.4241 +⌛️ [2/4] FRONTEND: Frontend time: 2.160s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.601s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00027243 0.43047563 + text_encoder-item0.clip_prompt_embeds 0.00024120 34.76268263 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025189 0.49915867 + text_encoder_2-item1.clip_prompt_embeds 0.00017312 0.11430756 + text_encoder_3-item2.t5_prompt_embeds 0.00000806 0.00285828 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00779453 1.01944864 + vae.encoder_f1 0.00779802 1.01792669 + vae.decoder 0.00023829 0.02969345 + ------------------------------------------------------------------------------------- + TOTAL 0.00367359 3.62324963 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 200380 +BPFP 0.7090 bits/point +EBPFP 1.4180 equivalent bits/point +MSE 3.623250 +---------------------- -------------------------------------------------------- +Time: 3.769s Load: 0.008s, Pack+Encode: 2.160s, Decode+Unpack: 1.601s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.6232 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000023937.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000023937.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000027620.zst (6/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000027620.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,168B, BPFP=12.1667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,360B, BPFP=1.2662 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,828B, BPFP=11.4250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 16,380B, BPFP=1.3295 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 49,780B, BPFP=1.2627 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 37,828B, BPFP=0.5772 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 37,816B, BPFP=0.5770 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 17,360B, BPFP=0.5298 +⌛️ [2/4] FRONTEND: Frontend time: 2.161s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.602s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036702 0.46654900 + text_encoder-item0.clip_prompt_embeds 0.00025651 34.70652394 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023478 0.46773295 + text_encoder_2-item1.clip_prompt_embeds 0.00016148 0.10921366 + text_encoder_3-item2.t5_prompt_embeds 0.00000844 0.00317718 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00655775 1.07150722 + vae.encoder_f1 0.00656268 1.07048213 + vae.decoder 0.00020283 0.02923671 + ------------------------------------------------------------------------------------- + TOTAL 0.00309620 3.64580307 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 232036 +BPFP 0.8210 bits/point +EBPFP 1.6420 equivalent bits/point +MSE 3.645803 +---------------------- -------------------------------------------------------- +Time: 3.771s Load: 0.009s, Pack+Encode: 2.161s, Decode+Unpack: 1.602s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.6458 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000027620.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000027620.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000030504.zst (7/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000030504.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,216B, BPFP=12.6667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,812B, BPFP=1.0568 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,820B, BPFP=11.3750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,192B, BPFP=1.1519 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 45,248B, BPFP=1.1477 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 37,120B, BPFP=0.5664 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 37,124B, BPFP=0.5665 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 22,204B, BPFP=0.6776 +⌛️ [2/4] FRONTEND: Frontend time: 2.149s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.596s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036856 0.47031935 + text_encoder-item0.clip_prompt_embeds 0.00022242 34.68023708 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022710 0.50093136 + text_encoder_2-item1.clip_prompt_embeds 0.00016311 0.08834570 + text_encoder_3-item2.t5_prompt_embeds 0.00000924 0.00269741 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00593415 0.90964299 + vae.encoder_f1 0.00594307 0.90872890 + vae.decoder 0.00018992 0.04021293 + ------------------------------------------------------------------------------------- + TOTAL 0.00280571 3.57038990 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 227252 +BPFP 0.8041 bits/point +EBPFP 1.6082 equivalent bits/point +MSE 3.570390 +---------------------- -------------------------------------------------------- +Time: 3.753s Load: 0.007s, Pack+Encode: 2.149s, Decode+Unpack: 1.596s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.5704 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000030504.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000030504.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000031248.zst (8/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000031248.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,176B, BPFP=12.2500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,612B, BPFP=1.3003 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,804B, BPFP=11.2750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 17,468B, BPFP=1.4179 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 52,452B, BPFP=1.3305 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 32,400B, BPFP=0.4944 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 32,408B, BPFP=0.4945 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 16,348B, BPFP=0.4989 +⌛️ [2/4] FRONTEND: Frontend time: 2.158s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.604s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036736 0.45715479 + text_encoder-item0.clip_prompt_embeds 0.00022110 35.90558628 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00042957 0.48182459 + text_encoder_2-item1.clip_prompt_embeds 0.00091506 0.10724829 + text_encoder_3-item2.t5_prompt_embeds 0.00000774 0.00330016 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00641770 0.91627687 + vae.encoder_f1 0.00642053 0.91336274 + vae.decoder 0.00017498 0.02475019 + ------------------------------------------------------------------------------------- + TOTAL 0.00305947 3.60415158 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 224184 +BPFP 0.7932 bits/point +EBPFP 1.5864 equivalent bits/point +MSE 3.604152 +---------------------- -------------------------------------------------------- +Time: 3.769s Load: 0.008s, Pack+Encode: 2.158s, Decode+Unpack: 1.604s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.6042 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000031248.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000031248.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000055072.zst (9/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000055072.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,200B, BPFP=12.5000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,748B, BPFP=1.0482 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,844B, BPFP=11.5250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,828B, BPFP=1.2036 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 52,184B, BPFP=1.3237 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 27,868B, BPFP=0.4252 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 27,880B, BPFP=0.4254 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 20,720B, BPFP=0.6323 +⌛️ [2/4] FRONTEND: Frontend time: 2.147s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.601s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00030751 0.43914723 + text_encoder-item0.clip_prompt_embeds 0.00021654 34.77260256 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022548 0.47976217 + text_encoder_2-item1.clip_prompt_embeds 0.00022218 0.13476141 + text_encoder_3-item2.t5_prompt_embeds 0.00000780 0.00288607 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00577698 0.81280011 + vae.encoder_f1 0.00578348 0.81290418 + vae.decoder 0.00017559 0.03828102 + ------------------------------------------------------------------------------------- + TOTAL 0.00273280 3.52993225 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 214788 +BPFP 0.7600 bits/point +EBPFP 1.5200 equivalent bits/point +MSE 3.529932 +---------------------- -------------------------------------------------------- +Time: 3.755s Load: 0.007s, Pack+Encode: 2.147s, Decode+Unpack: 1.601s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.5299 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000055072.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000055072.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000060932.zst (10/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000060932.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,108B, BPFP=11.5417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,748B, BPFP=1.1834 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,712B, BPFP=10.7000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 16,080B, BPFP=1.3052 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 53,864B, BPFP=1.3663 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 32,508B, BPFP=0.4960 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 32,524B, BPFP=0.4963 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 16,420B, BPFP=0.5011 +⌛️ [2/4] FRONTEND: Frontend time: 2.142s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.604s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00030339 0.53595881 + text_encoder-item0.clip_prompt_embeds 0.00022160 35.03558112 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00041183 0.45290751 + text_encoder_2-item1.clip_prompt_embeds 0.00016827 0.18724619 + text_encoder_3-item2.t5_prompt_embeds 0.00000781 0.00258608 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00668450 0.88632262 + vae.encoder_f1 0.00668875 0.88568670 + vae.decoder 0.00023059 0.03116024 + ------------------------------------------------------------------------------------- + TOTAL 0.00315742 3.57217436 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 223480 +BPFP 0.7907 bits/point +EBPFP 1.5815 equivalent bits/point +MSE 3.572174 +---------------------- -------------------------------------------------------- +Time: 3.754s Load: 0.008s, Pack+Encode: 2.142s, Decode+Unpack: 1.604s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.5722 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000060932.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000060932.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000062025.zst (11/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000062025.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,188B, BPFP=12.3750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 10,200B, BPFP=1.3799 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,732B, BPFP=10.8250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 16,792B, BPFP=1.3630 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 54,456B, BPFP=1.3813 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 27,880B, BPFP=0.4254 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 27,888B, BPFP=0.4255 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 10,448B, BPFP=0.3188 +⌛️ [2/4] FRONTEND: Frontend time: 2.148s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.607s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017240 0.47899151 + text_encoder-item0.clip_prompt_embeds 0.00023190 105.03321158 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00016235 0.41476469 + text_encoder_2-item1.clip_prompt_embeds 0.00020162 0.10631112 + text_encoder_3-item2.t5_prompt_embeds 0.00000881 0.00259309 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.04018118 1.35694838 + vae.encoder_f1 0.04018488 1.35244203 + vae.decoder 0.00016201 0.02029706 + ------------------------------------------------------------------------------------- + TOTAL 0.01868571 5.61549096 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 211100 +BPFP 0.7469 bits/point +EBPFP 1.4939 equivalent bits/point +MSE 5.615491 +---------------------- -------------------------------------------------------- +Time: 3.764s Load: 0.009s, Pack+Encode: 2.148s, Decode+Unpack: 1.607s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.6155 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000062025.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000062025.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000064718.zst (12/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000064718.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,152B, BPFP=12.0000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,500B, BPFP=1.1499 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,736B, BPFP=10.8500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,720B, BPFP=1.1948 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 51,864B, BPFP=1.3155 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 32,548B, BPFP=0.4966 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 32,556B, BPFP=0.4968 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 17,296B, BPFP=0.5278 +⌛️ [2/4] FRONTEND: Frontend time: 2.139s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.606s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00038474 0.51148844 + text_encoder-item0.clip_prompt_embeds 0.00023140 45.75787169 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025605 0.48767986 + text_encoder_2-item1.clip_prompt_embeds 0.00016636 0.11076204 + text_encoder_3-item2.t5_prompt_embeds 0.00000797 0.00267181 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.04874706 1.46654081 + vae.encoder_f1 0.04875064 1.47603106 + vae.decoder 0.00019641 0.02380780 + ------------------------------------------------------------------------------------- + TOTAL 0.02266071 4.11988631 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 220888 +BPFP 0.7816 bits/point +EBPFP 1.5631 equivalent bits/point +MSE 4.119886 +---------------------- -------------------------------------------------------- +Time: 3.752s Load: 0.008s, Pack+Encode: 2.139s, Decode+Unpack: 1.606s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.1199 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000064718.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000064718.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000070739.zst (13/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000070739.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,220B, BPFP=12.7083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,092B, BPFP=1.2300 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,780B, BPFP=11.1250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,292B, BPFP=1.2412 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 48,300B, BPFP=1.2251 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 38,720B, BPFP=0.5908 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 38,720B, BPFP=0.5908 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 10,664B, BPFP=0.3254 +⌛️ [2/4] FRONTEND: Frontend time: 2.146s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.602s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017774 0.45355173 + text_encoder-item0.clip_prompt_embeds 0.00030893 34.75774275 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00035783 0.46145267 + text_encoder_2-item1.clip_prompt_embeds 0.00024047 0.14164274 + text_encoder_3-item2.t5_prompt_embeds 0.00000770 0.00343728 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.01360236 1.38856089 + vae.encoder_f1 0.01360807 1.38590598 + vae.decoder 0.00023006 0.02627112 + ------------------------------------------------------------------------------------- + TOTAL 0.00637132 3.79490225 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 224304 +BPFP 0.7936 bits/point +EBPFP 1.5873 equivalent bits/point +MSE 3.794902 +---------------------- -------------------------------------------------------- +Time: 3.757s Load: 0.008s, Pack+Encode: 2.146s, Decode+Unpack: 1.602s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.7949 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000070739.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000070739.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000074646.zst (14/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000074646.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,156B, BPFP=12.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,908B, BPFP=1.3404 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,796B, BPFP=11.2250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 16,476B, BPFP=1.3373 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 53,948B, BPFP=1.3684 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 14,344B, BPFP=0.2189 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 14,340B, BPFP=0.2188 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 5,736B, BPFP=0.1750 +⌛️ [2/4] FRONTEND: Frontend time: 2.149s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.601s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00059206 0.47424376 + text_encoder-item0.clip_prompt_embeds 0.00024198 166.95594900 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023989 0.46597896 + text_encoder_2-item1.clip_prompt_embeds 0.00015983 0.09524406 + text_encoder_3-item2.t5_prompt_embeds 0.00000786 0.00282476 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 1.67190456 3.89380527 + vae.encoder_f1 1.67190480 3.89380431 + vae.decoder 0.00017417 0.01227962 + ------------------------------------------------------------------------------------- + TOTAL 0.77542609 8.41127920 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 178220 +BPFP 0.6306 bits/point +EBPFP 1.2612 equivalent bits/point +MSE 8.411279 +---------------------- -------------------------------------------------------- +Time: 3.757s Load: 0.007s, Pack+Encode: 2.149s, Decode+Unpack: 1.601s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 8.4113 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000074646.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000074646.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000085157.zst (15/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000085157.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,212B, BPFP=12.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,040B, BPFP=1.2229 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,772B, BPFP=11.0750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,948B, BPFP=1.2945 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 56,256B, BPFP=1.4269 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 39,384B, BPFP=0.6010 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 39,368B, BPFP=0.6007 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 19,780B, BPFP=0.6036 +⌛️ [2/4] FRONTEND: Frontend time: 2.138s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.606s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00021898 0.43624159 + text_encoder-item0.clip_prompt_embeds 0.00025129 34.77436545 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023862 0.48833709 + text_encoder_2-item1.clip_prompt_embeds 0.00021627 0.14161825 + text_encoder_3-item2.t5_prompt_embeds 0.00000880 0.00291780 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00621760 1.02001333 + vae.encoder_f1 0.00622505 1.01812887 + vae.decoder 0.00025114 0.03496560 + ------------------------------------------------------------------------------------- + TOTAL 0.00294689 3.62553893 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 243276 +BPFP 0.8608 bits/point +EBPFP 1.7216 equivalent bits/point +MSE 3.625539 +---------------------- -------------------------------------------------------- +Time: 3.752s Load: 0.008s, Pack+Encode: 2.138s, Decode+Unpack: 1.606s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.6255 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000085157.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000085157.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000089648.zst (16/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000089648.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,164B, BPFP=12.1250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,052B, BPFP=1.0893 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,800B, BPFP=11.2500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,248B, BPFP=1.2377 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 48,276B, BPFP=1.2245 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 42,156B, BPFP=0.6432 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 42,160B, BPFP=0.6433 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 20,252B, BPFP=0.6180 +⌛️ [2/4] FRONTEND: Frontend time: 2.136s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.600s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00241962 0.48843129 + text_encoder-item0.clip_prompt_embeds 0.00020838 56.53838187 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021520 0.47051573 + text_encoder_2-item1.clip_prompt_embeds 0.00018543 0.12467978 + text_encoder_3-item2.t5_prompt_embeds 0.00000844 0.00338976 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00675961 1.20674467 + vae.encoder_f1 0.00676652 1.20804262 + vae.decoder 0.00021373 0.03650633 + ------------------------------------------------------------------------------------- + TOTAL 0.00319201 4.28162614 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 239624 +BPFP 0.8479 bits/point +EBPFP 1.6957 equivalent bits/point +MSE 4.281626 +---------------------- -------------------------------------------------------- +Time: 3.743s Load: 0.007s, Pack+Encode: 2.136s, Decode+Unpack: 1.600s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.2816 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000089648.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000089648.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000093965.zst (17/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000093965.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,172B, BPFP=12.2083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,864B, BPFP=1.1991 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,760B, BPFP=11.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 16,076B, BPFP=1.3049 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 56,008B, BPFP=1.4207 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 23,676B, BPFP=0.3613 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 23,672B, BPFP=0.3612 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 30,256B, BPFP=0.9233 +⌛️ [2/4] FRONTEND: Frontend time: 2.140s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.604s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020005 0.41308081 + text_encoder-item0.clip_prompt_embeds 0.00021387 144.34924581 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00028145 0.46680384 + text_encoder_2-item1.clip_prompt_embeds 0.00018115 0.10196477 + text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00226559 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00596338 0.65007436 + vae.encoder_f1 0.00596322 0.65012431 + vae.decoder 0.00018207 0.05546455 + ------------------------------------------------------------------------------------- + TOTAL 0.00281657 6.32087778 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 222000 +BPFP 0.7855 bits/point +EBPFP 1.5710 equivalent bits/point +MSE 6.320878 +---------------------- -------------------------------------------------------- +Time: 3.751s Load: 0.008s, Pack+Encode: 2.140s, Decode+Unpack: 1.604s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.3209 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000093965.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000093965.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000094852.zst (18/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000094852.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,184B, BPFP=12.3333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,936B, BPFP=1.2089 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,744B, BPFP=10.9000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,356B, BPFP=1.2464 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 49,004B, BPFP=1.2430 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 17,148B, BPFP=0.2617 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 17,144B, BPFP=0.2616 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 23,104B, BPFP=0.7051 +⌛️ [2/4] FRONTEND: Frontend time: 2.159s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.607s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022632 0.42952943 + text_encoder-item0.clip_prompt_embeds 0.00022138 34.73861734 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00034234 0.45657630 + text_encoder_2-item1.clip_prompt_embeds 0.00019942 0.16979065 + text_encoder_3-item2.t5_prompt_embeds 0.00000807 0.00314001 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00552804 0.54357040 + vae.encoder_f1 0.00552758 0.54345965 + vae.decoder 0.00018040 0.04462553 + ------------------------------------------------------------------------------------- + TOTAL 0.00261550 3.40641500 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 194136 +BPFP 0.6869 bits/point +EBPFP 1.3738 equivalent bits/point +MSE 3.406415 +---------------------- -------------------------------------------------------- +Time: 3.774s Load: 0.008s, Pack+Encode: 2.159s, Decode+Unpack: 1.607s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.4064 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000094852.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000094852.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000117914.zst (19/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000117914.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,196B, BPFP=12.4583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,960B, BPFP=1.0768 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,840B, BPFP=11.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,940B, BPFP=1.2127 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 47,892B, BPFP=1.2148 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 20,792B, BPFP=0.3173 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 20,784B, BPFP=0.3171 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 13,696B, BPFP=0.4180 +⌛️ [2/4] FRONTEND: Frontend time: 2.145s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.600s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019161 0.49806333 + text_encoder-item0.clip_prompt_embeds 0.00024507 131.25826062 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020802 0.48155394 + text_encoder_2-item1.clip_prompt_embeds 0.00034897 0.10683109 + text_encoder_3-item2.t5_prompt_embeds 0.00000820 0.00308736 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00721525 0.80921811 + vae.encoder_f1 0.00721777 0.81056005 + vae.decoder 0.00018707 0.02556613 + ------------------------------------------------------------------------------------- + TOTAL 0.00340651 6.04948736 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 189616 +BPFP 0.6709 bits/point +EBPFP 1.3418 equivalent bits/point +MSE 6.049487 +---------------------- -------------------------------------------------------- +Time: 3.752s Load: 0.007s, Pack+Encode: 2.145s, Decode+Unpack: 1.600s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.0495 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000117914.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000117914.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000123321.zst (20/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000123321.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,196B, BPFP=12.4583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,824B, BPFP=1.0584 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,872B, BPFP=11.7000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,956B, BPFP=1.1328 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 45,440B, BPFP=1.1526 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 31,356B, BPFP=0.4785 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 31,360B, BPFP=0.4785 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 17,156B, BPFP=0.5236 +⌛️ [2/4] FRONTEND: Frontend time: 2.132s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.603s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018740 0.43983893 + text_encoder-item0.clip_prompt_embeds 0.00046272 336.19653003 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022428 0.47908430 + text_encoder_2-item1.clip_prompt_embeds 0.00014574 0.09667564 + text_encoder_3-item2.t5_prompt_embeds 0.00000853 0.00513437 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.01999603 1.28922582 + vae.encoder_f1 0.01999529 1.29137456 + vae.decoder 0.00024882 0.03272383 + ------------------------------------------------------------------------------------- + TOTAL 0.00933711 11.63307644 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 210676 +BPFP 0.7454 bits/point +EBPFP 1.4909 equivalent bits/point +MSE 11.633076 +---------------------- -------------------------------------------------------- +Time: 3.742s Load: 0.008s, Pack+Encode: 2.132s, Decode+Unpack: 1.603s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 11.6331 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000123321.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000123321.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127182.zst (21/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127182.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,168B, BPFP=12.1667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,212B, BPFP=1.1109 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,820B, BPFP=11.3750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,008B, BPFP=1.2182 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 45,636B, BPFP=1.1576 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 33,840B, BPFP=0.5164 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 33,820B, BPFP=0.5161 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 11,096B, BPFP=0.3386 +⌛️ [2/4] FRONTEND: Frontend time: 2.146s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.604s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00062140 0.48991072 + text_encoder-item0.clip_prompt_embeds 0.00020334 35.08229970 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00017433 0.42083349 + text_encoder_2-item1.clip_prompt_embeds 0.00020202 0.08843431 + text_encoder_3-item2.t5_prompt_embeds 0.00000787 0.00317343 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.01341345 1.16567910 + vae.encoder_f1 0.01341645 1.16399813 + vae.decoder 0.00018350 0.01995397 + ------------------------------------------------------------------------------------- + TOTAL 0.00627332 3.69715207 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 211116 +BPFP 0.7470 bits/point +EBPFP 1.4940 equivalent bits/point +MSE 3.697152 +---------------------- -------------------------------------------------------- +Time: 3.758s Load: 0.009s, Pack+Encode: 2.146s, Decode+Unpack: 1.604s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.6972 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127182.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000127182.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127394.zst (22/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127394.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,164B, BPFP=12.1250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,404B, BPFP=1.1369 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,780B, BPFP=11.1250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,840B, BPFP=1.2857 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 53,468B, BPFP=1.3562 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 36,116B, BPFP=0.5511 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 36,120B, BPFP=0.5511 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 24,260B, BPFP=0.7404 +⌛️ [2/4] FRONTEND: Frontend time: 2.135s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.602s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00063926 0.46381307 + text_encoder-item0.clip_prompt_embeds 0.00022316 23.60588939 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00045791 0.49840260 + text_encoder_2-item1.clip_prompt_embeds 0.00022852 0.09470883 + text_encoder_3-item2.t5_prompt_embeds 0.00000822 0.00271978 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00606298 0.88847500 + vae.encoder_f1 0.00607096 0.88867062 + vae.decoder 0.00023408 0.04086396 + ------------------------------------------------------------------------------------- + TOTAL 0.00287331 3.27153417 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 237668 +BPFP 0.8409 bits/point +EBPFP 1.6819 equivalent bits/point +MSE 3.271534 +---------------------- -------------------------------------------------------- +Time: 3.744s Load: 0.008s, Pack+Encode: 2.135s, Decode+Unpack: 1.602s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.2715 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127394.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000127394.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000133969.zst (23/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000133969.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,168B, BPFP=12.1667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,284B, BPFP=1.1207 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,808B, BPFP=11.3000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,276B, BPFP=1.1588 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 50,836B, BPFP=1.2895 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 33,388B, BPFP=0.5095 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 33,416B, BPFP=0.5099 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 21,428B, BPFP=0.6539 +⌛️ [2/4] FRONTEND: Frontend time: 2.144s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.590s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00054317 0.46824153 + text_encoder-item0.clip_prompt_embeds 0.00023597 34.81999755 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00026316 0.47321715 + text_encoder_2-item1.clip_prompt_embeds 0.00018757 0.11854504 + text_encoder_3-item2.t5_prompt_embeds 0.00000828 0.00274981 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00653100 0.94888037 + vae.encoder_f1 0.00653745 0.94669819 + vae.decoder 0.00020026 0.03457212 + ------------------------------------------------------------------------------------- + TOTAL 0.00308450 3.59260166 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 225120 +BPFP 0.7965 bits/point +EBPFP 1.5931 equivalent bits/point +MSE 3.592602 +---------------------- -------------------------------------------------------- +Time: 3.742s Load: 0.008s, Pack+Encode: 2.144s, Decode+Unpack: 1.590s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.5926 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000133969.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000133969.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000140270.zst (24/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000140270.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,200B, BPFP=12.5000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,076B, BPFP=1.2278 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,828B, BPFP=11.4250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,600B, BPFP=1.2662 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 48,368B, BPFP=1.2269 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 33,848B, BPFP=0.5165 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 33,856B, BPFP=0.5166 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 13,788B, BPFP=0.4208 +⌛️ [2/4] FRONTEND: Frontend time: 2.141s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.595s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018905 0.44582136 + text_encoder-item0.clip_prompt_embeds 0.00022433 34.72551618 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00107168 0.49489455 + text_encoder_2-item1.clip_prompt_embeds 0.00016492 0.08942841 + text_encoder_3-item2.t5_prompt_embeds 0.00000806 0.00343056 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00869686 1.24400592 + vae.encoder_f1 0.00870063 1.24112296 + vae.decoder 0.00021246 0.02777013 + ------------------------------------------------------------------------------------- + TOTAL 0.00408877 3.72487957 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 218080 +BPFP 0.7716 bits/point +EBPFP 1.5433 equivalent bits/point +MSE 3.724880 +---------------------- -------------------------------------------------------- +Time: 3.743s Load: 0.008s, Pack+Encode: 2.141s, Decode+Unpack: 1.595s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.7249 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000140270.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000140270.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000146358.zst (25/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000146358.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,188B, BPFP=12.3750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,384B, BPFP=1.2695 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,760B, BPFP=11.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 16,768B, BPFP=1.3610 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 52,536B, BPFP=1.3326 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 38,608B, BPFP=0.5891 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 38,616B, BPFP=0.5892 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 16,504B, BPFP=0.5037 +⌛️ [2/4] FRONTEND: Frontend time: 2.148s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.595s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020560 0.43785910 + text_encoder-item0.clip_prompt_embeds 0.00022433 179.89366883 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020112 0.46862249 + text_encoder_2-item1.clip_prompt_embeds 0.00017331 0.29325982 + text_encoder_3-item2.t5_prompt_embeds 0.00000752 0.00250485 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00626512 1.13314390 + vae.encoder_f1 0.00626949 1.13413537 + vae.decoder 0.00018936 0.02926093 + ------------------------------------------------------------------------------------- + TOTAL 0.00295827 7.48013243 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 235880 +BPFP 0.8346 bits/point +EBPFP 1.6692 equivalent bits/point +MSE 7.480132 +---------------------- -------------------------------------------------------- +Time: 3.751s Load: 0.008s, Pack+Encode: 2.148s, Decode+Unpack: 1.595s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 7.4801 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000146358.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000146358.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000148662.zst (26/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000148662.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,184B, BPFP=12.3333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,588B, BPFP=1.2971 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,712B, BPFP=10.7000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 17,148B, BPFP=1.3919 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 53,028B, BPFP=1.3451 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 27,256B, BPFP=0.4159 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 27,256B, BPFP=0.4159 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 11,688B, BPFP=0.3567 +⌛️ [2/4] FRONTEND: Frontend time: 2.139s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.594s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.01261352 0.53210437 + text_encoder-item0.clip_prompt_embeds 0.00026137 59.39752858 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00138553 0.47233434 + text_encoder_2-item1.clip_prompt_embeds 0.00019680 0.10789314 + text_encoder_3-item2.t5_prompt_embeds 0.00000808 0.00282507 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.35915655 2.19322610 + vae.encoder_f1 0.35915723 2.22092509 + vae.decoder 0.00024181 0.02469490 + ------------------------------------------------------------------------------------- + TOTAL 0.16663024 4.81786333 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 209376 +BPFP 0.7408 bits/point +EBPFP 1.4817 equivalent bits/point +MSE 4.817863 +---------------------- -------------------------------------------------------- +Time: 3.741s Load: 0.008s, Pack+Encode: 2.139s, Decode+Unpack: 1.594s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.8179 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000148662.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000148662.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000151051.zst (27/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000151051.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,184B, BPFP=12.3333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,812B, BPFP=1.0568 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,812B, BPFP=11.3250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,976B, BPFP=1.1344 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 45,428B, BPFP=1.1523 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 11,968B, BPFP=0.1826 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 11,968B, BPFP=0.1826 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 13,108B, BPFP=0.4000 +⌛️ [2/4] FRONTEND: Frontend time: 2.128s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.586s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00032602 0.48218707 + text_encoder-item0.clip_prompt_embeds 0.00021656 34.75330594 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00019988 0.49520044 + text_encoder_2-item1.clip_prompt_embeds 0.00016555 0.08540009 + text_encoder_3-item2.t5_prompt_embeds 0.00000783 0.00352582 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.29031765 1.73599136 + vae.encoder_f1 0.29031771 1.73514104 + vae.decoder 0.00019965 0.03380304 + ------------------------------------------------------------------------------------- + TOTAL 0.13469251 3.95479459 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 167772 +BPFP 0.5936 bits/point +EBPFP 1.1872 equivalent bits/point +MSE 3.954795 +---------------------- -------------------------------------------------------- +Time: 3.721s Load: 0.007s, Pack+Encode: 2.128s, Decode+Unpack: 1.586s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.9548 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000151051.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000151051.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000155443.zst (28/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000155443.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,184B, BPFP=12.3333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,356B, BPFP=0.9951 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,728B, BPFP=10.8000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,924B, BPFP=1.1302 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 45,428B, BPFP=1.1523 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 28,780B, BPFP=0.4391 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 28,776B, BPFP=0.4391 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 26,924B, BPFP=0.8217 +⌛️ [2/4] FRONTEND: Frontend time: 2.138s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.595s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00199158 0.48201636 + text_encoder-item0.clip_prompt_embeds 0.00025451 23.73525433 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023552 0.49632621 + text_encoder_2-item1.clip_prompt_embeds 0.00017758 0.09935313 + text_encoder_3-item2.t5_prompt_embeds 0.00000816 0.00311966 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00595764 0.69722354 + vae.encoder_f1 0.00596395 0.69603091 + vae.decoder 0.00019845 0.04389266 + ------------------------------------------------------------------------------------- + TOTAL 0.00281886 3.18651384 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 214616 +BPFP 0.7594 bits/point +EBPFP 1.5187 equivalent bits/point +MSE 3.186514 +---------------------- -------------------------------------------------------- +Time: 3.741s Load: 0.008s, Pack+Encode: 2.138s, Decode+Unpack: 1.595s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.1865 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000155443.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000155443.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000159458.zst (29/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000159458.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,168B, BPFP=12.1667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,288B, BPFP=1.2565 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,748B, BPFP=10.9250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 17,156B, BPFP=1.3925 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 52,760B, BPFP=1.3383 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 21,760B, BPFP=0.3320 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 21,764B, BPFP=0.3321 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 11,224B, BPFP=0.3425 +⌛️ [2/4] FRONTEND: Frontend time: 2.128s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.594s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00029967 0.46952617 + text_encoder-item0.clip_prompt_embeds 0.00026157 35.95517536 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022221 0.48033566 + text_encoder_2-item1.clip_prompt_embeds 0.00022582 0.09779166 + text_encoder_3-item2.t5_prompt_embeds 0.00000776 0.00272213 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.40456498 2.31902456 + vae.encoder_f1 0.40456539 2.31709075 + vae.decoder 0.00020503 0.02396019 + ------------------------------------------------------------------------------------- + TOTAL 0.18768128 4.25564446 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 197384 +BPFP 0.6984 bits/point +EBPFP 1.3968 equivalent bits/point +MSE 4.255644 +---------------------- -------------------------------------------------------- +Time: 3.730s Load: 0.007s, Pack+Encode: 2.128s, Decode+Unpack: 1.594s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.2556 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000159458.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000159458.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000161128.zst (30/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000161128.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,172B, BPFP=12.2083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,244B, BPFP=1.2505 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,764B, BPFP=11.0250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,148B, BPFP=1.1484 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 48,248B, BPFP=1.2238 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 34,376B, BPFP=0.5245 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 34,364B, BPFP=0.5244 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 23,032B, BPFP=0.7029 +⌛️ [2/4] FRONTEND: Frontend time: 2.134s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.598s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00063306 0.46091505 + text_encoder-item0.clip_prompt_embeds 0.00027179 34.77481991 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025795 0.51314864 + text_encoder_2-item1.clip_prompt_embeds 0.00015124 0.08976622 + text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00327656 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00673531 1.21197367 + vae.encoder_f1 0.00673732 1.20977080 + vae.decoder 0.00020129 0.03848381 + ------------------------------------------------------------------------------------- + TOTAL 0.00317768 3.71272214 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 226864 +BPFP 0.8027 bits/point +EBPFP 1.6054 equivalent bits/point +MSE 3.712722 +---------------------- -------------------------------------------------------- +Time: 3.740s Load: 0.008s, Pack+Encode: 2.134s, Decode+Unpack: 1.598s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.7127 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000161128.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000161128.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000168458.zst (31/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000168458.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,200B, BPFP=12.5000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,524B, BPFP=1.2884 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,776B, BPFP=11.1000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,944B, BPFP=1.2942 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 54,864B, BPFP=1.3916 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 27,920B, BPFP=0.4260 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 27,912B, BPFP=0.4259 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 9,928B, BPFP=0.3030 +⌛️ [2/4] FRONTEND: Frontend time: 2.130s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.593s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00023681 0.48261885 + text_encoder-item0.clip_prompt_embeds 0.00023057 59.80302185 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023879 0.51540279 + text_encoder_2-item1.clip_prompt_embeds 0.00123217 0.10542613 + text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00299536 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00881784 1.15259266 + vae.encoder_f1 0.00882136 1.15527296 + vae.decoder 0.00017598 0.02095719 + ------------------------------------------------------------------------------------- + TOTAL 0.00418676 4.33954534 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 209584 +BPFP 0.7416 bits/point +EBPFP 1.4831 equivalent bits/point +MSE 4.339545 +---------------------- -------------------------------------------------------- +Time: 3.730s Load: 0.007s, Pack+Encode: 2.130s, Decode+Unpack: 1.593s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.3395 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000168458.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000168458.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000171788.zst (32/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000171788.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,208B, BPFP=12.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,748B, BPFP=1.1834 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,688B, BPFP=10.5500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,628B, BPFP=1.2685 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 50,880B, BPFP=1.2906 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 30,512B, BPFP=0.4656 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 30,508B, BPFP=0.4655 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 24,348B, BPFP=0.7430 +⌛️ [2/4] FRONTEND: Frontend time: 2.134s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.588s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00038174 0.45275712 + text_encoder-item0.clip_prompt_embeds 0.00025208 23.93406512 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00047028 0.52303133 + text_encoder_2-item1.clip_prompt_embeds 0.00113921 0.10992198 + text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00270962 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00582247 0.73408622 + vae.encoder_f1 0.00582996 0.73356199 + vae.decoder 0.00016099 0.04238294 + ------------------------------------------------------------------------------------- + TOTAL 0.00279351 3.20919810 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 224036 +BPFP 0.7927 bits/point +EBPFP 1.5854 equivalent bits/point +MSE 3.209198 +---------------------- -------------------------------------------------------- +Time: 3.730s Load: 0.008s, Pack+Encode: 2.134s, Decode+Unpack: 1.588s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.2092 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000171788.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000171788.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000179265.zst (33/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000179265.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,228B, BPFP=12.7917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,264B, BPFP=1.1180 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,720B, BPFP=10.7500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,328B, BPFP=1.2442 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 48,584B, BPFP=1.2323 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 38,144B, BPFP=0.5820 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 38,156B, BPFP=0.5822 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 18,456B, BPFP=0.5632 +⌛️ [2/4] FRONTEND: Frontend time: 2.151s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.595s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017989 0.42088727 + text_encoder-item0.clip_prompt_embeds 0.00020809 23.86758278 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00035925 0.50382748 + text_encoder_2-item1.clip_prompt_embeds 0.00112984 0.11414728 + text_encoder_3-item2.t5_prompt_embeds 0.00000832 0.00243520 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00602745 1.08295894 + vae.encoder_f1 0.00603159 1.08574879 + vae.decoder 0.00017526 0.03373282 + ------------------------------------------------------------------------------------- + TOTAL 0.00288782 3.36914508 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 230396 +BPFP 0.8152 bits/point +EBPFP 1.6304 equivalent bits/point +MSE 3.369145 +---------------------- -------------------------------------------------------- +Time: 3.754s Load: 0.008s, Pack+Encode: 2.151s, Decode+Unpack: 1.595s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.3691 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000179265.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000179265.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000189752.zst (34/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000189752.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,192B, BPFP=12.4167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,312B, BPFP=1.1245 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,768B, BPFP=11.0500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,764B, BPFP=1.1984 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 48,440B, BPFP=1.2287 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 39,748B, BPFP=0.6065 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 39,724B, BPFP=0.6061 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 21,268B, BPFP=0.6490 +⌛️ [2/4] FRONTEND: Frontend time: 2.134s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.595s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019078 0.44002930 + text_encoder-item0.clip_prompt_embeds 0.00020908 23.85288149 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00048701 0.50306969 + text_encoder_2-item1.clip_prompt_embeds 0.00016227 0.10367761 + text_encoder_3-item2.t5_prompt_embeds 0.00000845 0.00257393 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00634616 1.06853509 + vae.encoder_f1 0.00635208 1.07024515 + vae.decoder 0.00022721 0.03391566 + ------------------------------------------------------------------------------------- + TOTAL 0.00300000 3.36141110 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 235732 +BPFP 0.8341 bits/point +EBPFP 1.6682 equivalent bits/point +MSE 3.361411 +---------------------- -------------------------------------------------------- +Time: 3.737s Load: 0.008s, Pack+Encode: 2.134s, Decode+Unpack: 1.595s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.3614 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000189752.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000189752.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222118.zst (35/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222118.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,208B, BPFP=12.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,316B, BPFP=1.1250 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,836B, BPFP=11.4750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,968B, BPFP=1.1338 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 45,812B, BPFP=1.1620 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 17,720B, BPFP=0.2704 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 17,704B, BPFP=0.2701 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 7,992B, BPFP=0.2439 +⌛️ [2/4] FRONTEND: Frontend time: 2.134s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.595s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020745 0.47290079 + text_encoder-item0.clip_prompt_embeds 0.00022947 24.10580907 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00031292 0.52258101 + text_encoder_2-item1.clip_prompt_embeds 0.00017460 0.15278383 + text_encoder_3-item2.t5_prompt_embeds 0.00000789 0.00496201 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.05448642 1.19574833 + vae.encoder_f1 0.05448771 1.19766414 + vae.decoder 0.00017748 0.01979459 + ------------------------------------------------------------------------------------- + TOTAL 0.02531999 3.42793027 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 175072 +BPFP 0.6195 bits/point +EBPFP 1.2389 equivalent bits/point +MSE 3.427930 +---------------------- -------------------------------------------------------- +Time: 3.737s Load: 0.007s, Pack+Encode: 2.134s, Decode+Unpack: 1.595s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.4279 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222118.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000222118.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222825.zst (36/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222825.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,180B, BPFP=12.2917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,152B, BPFP=1.1028 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,812B, BPFP=11.3250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,368B, BPFP=1.2474 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 50,192B, BPFP=1.2731 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 26,784B, BPFP=0.4087 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 26,796B, BPFP=0.4089 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 9,404B, BPFP=0.2870 +⌛️ [2/4] FRONTEND: Frontend time: 2.130s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.600s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00026664 0.47195554 + text_encoder-item0.clip_prompt_embeds 0.00020169 33.86523226 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00017591 0.46855578 + text_encoder_2-item1.clip_prompt_embeds 0.00015739 0.09440441 + text_encoder_3-item2.t5_prompt_embeds 0.00000811 0.00279612 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.06876971 1.42154908 + vae.encoder_f1 0.06877109 1.43146312 + vae.decoder 0.00023999 0.01746751 + ------------------------------------------------------------------------------------- + TOTAL 0.03194988 3.78661309 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 200204 +BPFP 0.7084 bits/point +EBPFP 1.4168 equivalent bits/point +MSE 3.786613 +---------------------- -------------------------------------------------------- +Time: 3.738s Load: 0.008s, Pack+Encode: 2.130s, Decode+Unpack: 1.600s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.7866 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222825.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000222825.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000227478.zst (37/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000227478.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,172B, BPFP=12.2083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,772B, BPFP=1.1867 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,804B, BPFP=11.2750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,332B, BPFP=1.2445 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 47,756B, BPFP=1.2113 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 28,396B, BPFP=0.4333 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 28,376B, BPFP=0.4330 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 25,272B, BPFP=0.7712 +⌛️ [2/4] FRONTEND: Frontend time: 2.146s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.595s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00028326 0.46611456 + text_encoder-item0.clip_prompt_embeds 0.00025253 23.79389035 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00041073 0.50022287 + text_encoder_2-item1.clip_prompt_embeds 0.00018825 0.11025610 + text_encoder_3-item2.t5_prompt_embeds 0.00000859 0.00290557 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00595097 0.70629799 + vae.encoder_f1 0.00595882 0.70663691 + vae.decoder 0.00020134 0.04784479 + ------------------------------------------------------------------------------------- + TOTAL 0.00281645 3.19351148 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 217396 +BPFP 0.7692 bits/point +EBPFP 1.5384 equivalent bits/point +MSE 3.193511 +---------------------- -------------------------------------------------------- +Time: 3.749s Load: 0.008s, Pack+Encode: 2.146s, Decode+Unpack: 1.595s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.1935 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000227478.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000227478.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000239843.zst (38/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000239843.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,132B, BPFP=11.7917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,804B, BPFP=1.1910 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,824B, BPFP=11.4000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 16,196B, BPFP=1.3146 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 49,196B, BPFP=1.2479 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 22,252B, BPFP=0.3395 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 22,236B, BPFP=0.3393 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 18,232B, BPFP=0.5564 +⌛️ [2/4] FRONTEND: Frontend time: 2.143s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.601s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00029404 0.48759758 + text_encoder-item0.clip_prompt_embeds 0.00022201 34.77963508 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00030500 0.47859817 + text_encoder_2-item1.clip_prompt_embeds 0.00020541 0.09734143 + text_encoder_3-item2.t5_prompt_embeds 0.00000847 0.00302842 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00831743 0.93090308 + vae.encoder_f1 0.00831926 0.93169475 + vae.decoder 0.00028593 0.02980683 + ------------------------------------------------------------------------------------- + TOTAL 0.00392223 3.58246997 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 200388 +BPFP 0.7090 bits/point +EBPFP 1.4181 equivalent bits/point +MSE 3.582470 +---------------------- -------------------------------------------------------- +Time: 3.752s Load: 0.008s, Pack+Encode: 2.143s, Decode+Unpack: 1.601s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.5825 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000239843.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000239843.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000240250.zst (39/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000240250.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,196B, BPFP=12.4583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,876B, BPFP=1.3360 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,796B, BPFP=11.2250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 16,260B, BPFP=1.3198 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 53,508B, BPFP=1.3572 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 38,332B, BPFP=0.5849 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 38,352B, BPFP=0.5852 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 17,312B, BPFP=0.5283 +⌛️ [2/4] FRONTEND: Frontend time: 2.148s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.611s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00025874 0.45374699 + text_encoder-item0.clip_prompt_embeds 0.00026808 23.82098679 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00033998 0.48830533 + text_encoder_2-item1.clip_prompt_embeds 0.00021475 0.09407694 + text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00280950 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00606586 1.10265100 + vae.encoder_f1 0.00607066 1.10094726 + vae.decoder 0.00019664 0.03193163 + ------------------------------------------------------------------------------------- + TOTAL 0.00286987 3.37498779 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 237148 +BPFP 0.8391 bits/point +EBPFP 1.6782 equivalent bits/point +MSE 3.374988 +---------------------- -------------------------------------------------------- +Time: 3.767s Load: 0.008s, Pack+Encode: 2.148s, Decode+Unpack: 1.611s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.3750 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000240250.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000240250.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000258793.zst (40/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000258793.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,132B, BPFP=11.7917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,508B, BPFP=1.2863 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,776B, BPFP=11.1000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 17,244B, BPFP=1.3997 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 54,404B, BPFP=1.3800 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 29,020B, BPFP=0.4428 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 29,020B, BPFP=0.4428 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 17,380B, BPFP=0.5304 +⌛️ [2/4] FRONTEND: Frontend time: 2.141s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.607s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00028013 0.46789145 + text_encoder-item0.clip_prompt_embeds 0.00023198 23.84586166 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00035192 0.54819193 + text_encoder_2-item1.clip_prompt_embeds 0.00017676 0.10022220 + text_encoder_3-item2.t5_prompt_embeds 0.00000830 0.00270782 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.05216765 1.44677329 + vae.encoder_f1 0.05216896 1.44530165 + vae.decoder 0.00017960 0.02846958 + ------------------------------------------------------------------------------------- + TOTAL 0.02424513 3.53517616 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 220000 +BPFP 0.7784 bits/point +EBPFP 1.5568 equivalent bits/point +MSE 3.535176 +---------------------- -------------------------------------------------------- +Time: 3.756s Load: 0.008s, Pack+Encode: 2.141s, Decode+Unpack: 1.607s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.5352 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000258793.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000258793.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000270402.zst (41/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000270402.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,160B, BPFP=12.0833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,896B, BPFP=1.2035 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,840B, BPFP=11.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,208B, BPFP=1.2344 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 47,600B, BPFP=1.2074 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 40,396B, BPFP=0.6164 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 40,416B, BPFP=0.6167 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 19,264B, BPFP=0.5879 +⌛️ [2/4] FRONTEND: Frontend time: 2.149s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.613s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00048242 0.49481277 + text_encoder-item0.clip_prompt_embeds 0.00023125 34.66390397 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00024484 0.48770928 + text_encoder_2-item1.clip_prompt_embeds 0.00020589 0.14747149 + text_encoder_3-item2.t5_prompt_embeds 0.00000799 0.00422772 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00620361 0.98234069 + vae.encoder_f1 0.00620966 0.97926474 + vae.decoder 0.00020748 0.03399219 + ------------------------------------------------------------------------------------- + TOTAL 0.00293402 3.60524672 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 235296 +BPFP 0.8325 bits/point +EBPFP 1.6651 equivalent bits/point +MSE 3.605247 +---------------------- -------------------------------------------------------- +Time: 3.770s Load: 0.008s, Pack+Encode: 2.149s, Decode+Unpack: 1.613s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.6052 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000270402.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000270402.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000274272.zst (42/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000274272.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,192B, BPFP=12.4167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,556B, BPFP=1.1575 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,680B, BPFP=10.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,888B, BPFP=1.2896 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 49,880B, BPFP=1.2652 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 34,064B, BPFP=0.5198 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 34,052B, BPFP=0.5196 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 19,776B, BPFP=0.6035 +⌛️ [2/4] FRONTEND: Frontend time: 2.163s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.594s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022540 0.43530357 + text_encoder-item0.clip_prompt_embeds 0.00023066 23.82797069 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00044687 0.50706682 + text_encoder_2-item1.clip_prompt_embeds 0.00018171 0.13544794 + text_encoder_3-item2.t5_prompt_embeds 0.00000793 0.00416433 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.03159856 1.13474727 + vae.encoder_f1 0.03160188 1.13063097 + vae.decoder 0.00018417 0.02997318 + ------------------------------------------------------------------------------------- + TOTAL 0.01470700 3.39126594 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 225604 +BPFP 0.7982 bits/point +EBPFP 1.5965 equivalent bits/point +MSE 3.391266 +---------------------- -------------------------------------------------------- +Time: 3.765s Load: 0.008s, Pack+Encode: 2.163s, Decode+Unpack: 1.594s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.3913 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000274272.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000274272.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000280891.zst (43/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000280891.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,196B, BPFP=12.4583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,828B, BPFP=1.1943 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,720B, BPFP=10.7500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,788B, BPFP=1.2815 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 52,224B, BPFP=1.3247 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 40,568B, BPFP=0.6190 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 40,564B, BPFP=0.6190 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 17,996B, BPFP=0.5492 +⌛️ [2/4] FRONTEND: Frontend time: 2.145s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.599s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017642 0.41214633 + text_encoder-item0.clip_prompt_embeds 0.00024948 35.02747269 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00032352 0.48484340 + text_encoder_2-item1.clip_prompt_embeds 0.00019749 0.09196978 + text_encoder_3-item2.t5_prompt_embeds 0.00000781 0.00335758 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.03490865 1.60861421 + vae.encoder_f1 0.03491008 1.61987841 + vae.decoder 0.00028462 0.03679348 + ------------------------------------------------------------------------------------- + TOTAL 0.01625440 3.90628107 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 239400 +BPFP 0.8471 bits/point +EBPFP 1.6941 equivalent bits/point +MSE 3.906281 +---------------------- -------------------------------------------------------- +Time: 3.752s Load: 0.008s, Pack+Encode: 2.145s, Decode+Unpack: 1.599s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.9063 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000280891.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000280891.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000285788.zst (44/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000285788.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,208B, BPFP=12.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,932B, BPFP=1.0731 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,724B, BPFP=10.7750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,740B, BPFP=1.2776 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 52,856B, BPFP=1.3407 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 20,348B, BPFP=0.3105 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 20,352B, BPFP=0.3105 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 24,680B, BPFP=0.7532 +⌛️ [2/4] FRONTEND: Frontend time: 2.142s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.596s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017474 0.42381056 + text_encoder-item0.clip_prompt_embeds 0.00021560 349.68892045 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023980 0.47387180 + text_encoder_2-item1.clip_prompt_embeds 0.00021108 0.09475118 + text_encoder_3-item2.t5_prompt_embeds 0.00000804 0.00244395 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00544735 0.55522126 + vae.encoder_f1 0.00544843 0.55595660 + vae.decoder 0.00018632 0.04377482 + ------------------------------------------------------------------------------------- + TOTAL 0.00257940 11.64604648 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 205356 +BPFP 0.7266 bits/point +EBPFP 1.4532 equivalent bits/point +MSE 11.646046 +---------------------- -------------------------------------------------------- +Time: 3.746s Load: 0.008s, Pack+Encode: 2.142s, Decode+Unpack: 1.596s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 11.6460 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000285788.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000285788.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000287291.zst (45/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000287291.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,140B, BPFP=11.8750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,192B, BPFP=1.1082 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,780B, BPFP=11.1250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,540B, BPFP=1.2614 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 47,780B, BPFP=1.2120 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 35,740B, BPFP=0.5453 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 35,740B, BPFP=0.5453 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 19,728B, BPFP=0.6021 +⌛️ [2/4] FRONTEND: Frontend time: 2.152s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.605s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00241107 0.47792077 + text_encoder-item0.clip_prompt_embeds 0.00022698 45.64932951 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00024914 0.51886582 + text_encoder_2-item1.clip_prompt_embeds 0.00021102 0.13199488 + text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00404060 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00630479 0.88657945 + vae.encoder_f1 0.00631430 0.88421100 + vae.decoder 0.00018596 0.03012466 + ------------------------------------------------------------------------------------- + TOTAL 0.00298001 3.84718512 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 226156 +BPFP 0.8002 bits/point +EBPFP 1.6004 equivalent bits/point +MSE 3.847185 +---------------------- -------------------------------------------------------- +Time: 3.765s Load: 0.008s, Pack+Encode: 2.152s, Decode+Unpack: 1.605s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.8472 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000287291.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000287291.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000289343.zst (46/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000289343.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,228B, BPFP=12.7917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,740B, BPFP=1.1824 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,856B, BPFP=11.6000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,912B, BPFP=1.2104 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 45,408B, BPFP=1.1518 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 33,880B, BPFP=0.5170 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 33,876B, BPFP=0.5169 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 22,620B, BPFP=0.6903 +⌛️ [2/4] FRONTEND: Frontend time: 2.144s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.597s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00074171 0.48479108 + text_encoder-item0.clip_prompt_embeds 0.00024643 34.77988873 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022451 0.51410813 + text_encoder_2-item1.clip_prompt_embeds 0.00018967 0.12929769 + text_encoder_3-item2.t5_prompt_embeds 0.00000778 0.00270532 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00612578 0.83138579 + vae.encoder_f1 0.00613243 0.83244944 + vae.decoder 0.00018179 0.03444801 + ------------------------------------------------------------------------------------- + TOTAL 0.00289482 3.53829193 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 223036 +BPFP 0.7892 bits/point +EBPFP 1.5783 equivalent bits/point +MSE 3.538292 +---------------------- -------------------------------------------------------- +Time: 3.749s Load: 0.008s, Pack+Encode: 2.144s, Decode+Unpack: 1.597s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.5383 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000289343.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000289343.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000304545.zst (47/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000304545.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,208B, BPFP=12.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 10,176B, BPFP=1.3766 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,788B, BPFP=11.1750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 18,776B, BPFP=1.5240 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 54,952B, BPFP=1.3939 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 9,836B, BPFP=0.1501 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 9,836B, BPFP=0.1501 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 24,980B, BPFP=0.7623 +⌛️ [2/4] FRONTEND: Frontend time: 2.137s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.597s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018845 0.47955263 + text_encoder-item0.clip_prompt_embeds 0.00024049 23.67842135 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023104 0.50438967 + text_encoder_2-item1.clip_prompt_embeds 0.00016878 0.11093798 + text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00280559 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00526071 0.31135285 + vae.encoder_f1 0.00526072 0.31135055 + vae.decoder 0.00016981 0.04128557 + ------------------------------------------------------------------------------------- + TOTAL 0.00248947 3.00651153 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 192068 +BPFP 0.6796 bits/point +EBPFP 1.3592 equivalent bits/point +MSE 3.006512 +---------------------- -------------------------------------------------------- +Time: 3.743s Load: 0.008s, Pack+Encode: 2.137s, Decode+Unpack: 1.597s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.0065 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000304545.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000304545.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000310622.zst (48/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000310622.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,124B, BPFP=11.7083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,324B, BPFP=1.1261 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,764B, BPFP=11.0250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,676B, BPFP=1.1912 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 49,076B, BPFP=1.2448 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 36,032B, BPFP=0.5498 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 36,020B, BPFP=0.5496 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 21,864B, BPFP=0.6672 +⌛️ [2/4] FRONTEND: Frontend time: 2.139s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.595s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00063331 0.44734629 + text_encoder-item0.clip_prompt_embeds 0.00022843 23.83870443 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00086038 0.47309227 + text_encoder_2-item1.clip_prompt_embeds 0.00016207 0.10807995 + text_encoder_3-item2.t5_prompt_embeds 0.00000746 0.00303183 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00622977 0.91028476 + vae.encoder_f1 0.00623684 0.90973032 + vae.decoder 0.00019755 0.03305928 + ------------------------------------------------------------------------------------- + TOTAL 0.00294358 3.28726574 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 229396 +BPFP 0.8117 bits/point +EBPFP 1.6233 equivalent bits/point +MSE 3.287266 +---------------------- -------------------------------------------------------- +Time: 3.742s Load: 0.008s, Pack+Encode: 2.139s, Decode+Unpack: 1.595s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.2873 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000310622.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000310622.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000311394.zst (49/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000311394.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,208B, BPFP=12.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,392B, BPFP=1.2706 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,880B, BPFP=11.7500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,704B, BPFP=1.2747 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 48,404B, BPFP=1.2278 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 24,392B, BPFP=0.3722 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 24,384B, BPFP=0.3721 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 14,584B, BPFP=0.4451 +⌛️ [2/4] FRONTEND: Frontend time: 2.148s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.597s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019653 0.50727562 + text_encoder-item0.clip_prompt_embeds 0.00026004 34.70789156 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025016 0.51953983 + text_encoder_2-item1.clip_prompt_embeds 0.00015074 0.11134584 + text_encoder_3-item2.t5_prompt_embeds 0.00000873 0.00351177 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00725303 0.78999346 + vae.encoder_f1 0.00725507 0.78997922 + vae.decoder 0.00017991 0.02579440 + ------------------------------------------------------------------------------------- + TOTAL 0.00341494 3.51529981 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 200464 +BPFP 0.7093 bits/point +EBPFP 1.4186 equivalent bits/point +MSE 3.515300 +---------------------- -------------------------------------------------------- +Time: 3.752s Load: 0.008s, Pack+Encode: 2.148s, Decode+Unpack: 1.597s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.5153 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000311394.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000311394.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000316015.zst (50/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000316015.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,220B, BPFP=12.7083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,380B, BPFP=1.2689 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,816B, BPFP=11.3500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,960B, BPFP=1.2955 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 50,940B, BPFP=1.2921 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 26,052B, BPFP=0.3975 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 26,044B, BPFP=0.3974 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 11,100B, BPFP=0.3387 +⌛️ [2/4] FRONTEND: Frontend time: 2.147s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.589s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00056072 0.47115993 + text_encoder-item0.clip_prompt_embeds 0.00031748 23.72513993 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022063 0.50960083 + text_encoder_2-item1.clip_prompt_embeds 0.00019717 0.11187674 + text_encoder_3-item2.t5_prompt_embeds 0.00000812 0.00315772 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.42111695 2.27037644 + vae.encoder_f1 0.42111716 2.26497936 + vae.decoder 0.00019827 0.02308819 + ------------------------------------------------------------------------------------- + TOTAL 0.19535708 3.91299546 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 203028 +BPFP 0.7184 bits/point +EBPFP 1.4367 equivalent bits/point +MSE 3.912995 +---------------------- -------------------------------------------------------- +Time: 3.743s Load: 0.008s, Pack+Encode: 2.147s, Decode+Unpack: 1.589s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.9130 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000316015.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000316015.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000323571.zst (51/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000323571.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,184B, BPFP=12.3333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,344B, BPFP=1.2641 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,712B, BPFP=10.7000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 16,152B, BPFP=1.3110 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 55,328B, BPFP=1.4034 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 29,996B, BPFP=0.4577 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 29,996B, BPFP=0.4577 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 13,596B, BPFP=0.4149 +⌛️ [2/4] FRONTEND: Frontend time: 2.129s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.593s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020408 0.41948994 + text_encoder-item0.clip_prompt_embeds 0.00024951 72.18056683 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020437 0.47420254 + text_encoder_2-item1.clip_prompt_embeds 0.00016387 0.09158004 + text_encoder_3-item2.t5_prompt_embeds 0.00000772 0.00290676 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.10376993 2.15533853 + vae.encoder_f1 0.10377157 2.15394902 + vae.decoder 0.00019787 0.02561623 + ------------------------------------------------------------------------------------- + TOTAL 0.04817852 5.12725600 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 217824 +BPFP 0.7707 bits/point +EBPFP 1.5414 equivalent bits/point +MSE 5.127256 +---------------------- -------------------------------------------------------- +Time: 3.730s Load: 0.008s, Pack+Encode: 2.129s, Decode+Unpack: 1.593s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.1273 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000323571.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000323571.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325483.zst (52/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325483.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,204B, BPFP=12.5417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,500B, BPFP=1.1499 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,688B, BPFP=10.5500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,880B, BPFP=1.2890 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 49,236B, BPFP=1.2489 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 31,552B, BPFP=0.4814 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 31,552B, BPFP=0.4814 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 13,104B, BPFP=0.3999 +⌛️ [2/4] FRONTEND: Frontend time: 2.142s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.590s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00035723 0.44094225 + text_encoder-item0.clip_prompt_embeds 0.00022350 106.33575149 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00046887 0.49575438 + text_encoder_2-item1.clip_prompt_embeds 0.00019271 0.10747732 + text_encoder_3-item2.t5_prompt_embeds 0.00000799 0.00325336 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.01346414 1.25223064 + vae.encoder_f1 0.01346933 1.24898028 + vae.decoder 0.00019243 0.02397664 + ------------------------------------------------------------------------------------- + TOTAL 0.00629858 5.60188773 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 213232 +BPFP 0.7545 bits/point +EBPFP 1.5089 equivalent bits/point +MSE 5.601888 +---------------------- -------------------------------------------------------- +Time: 3.740s Load: 0.008s, Pack+Encode: 2.142s, Decode+Unpack: 1.590s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 5.6019 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325483.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000325483.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325991.zst (53/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325991.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,188B, BPFP=12.3750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,664B, BPFP=1.1721 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,844B, BPFP=11.5250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,616B, BPFP=1.1864 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 55,008B, BPFP=1.3953 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 27,360B, BPFP=0.4175 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 27,372B, BPFP=0.4177 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 12,164B, BPFP=0.3712 +⌛️ [2/4] FRONTEND: Frontend time: 2.144s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.599s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00021921 0.44787546 + text_encoder-item0.clip_prompt_embeds 0.00024958 34.73244090 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00135081 0.54004292 + text_encoder_2-item1.clip_prompt_embeds 0.00018030 0.09536556 + text_encoder_3-item2.t5_prompt_embeds 0.00000823 0.00350623 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.11196710 1.72493839 + vae.encoder_f1 0.11196851 1.72006488 + vae.decoder 0.00023459 0.02788842 + ------------------------------------------------------------------------------------- + TOTAL 0.05198575 3.94794959 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 208732 +BPFP 0.7386 bits/point +EBPFP 1.4771 equivalent bits/point +MSE 3.947950 +---------------------- -------------------------------------------------------- +Time: 3.751s Load: 0.008s, Pack+Encode: 2.144s, Decode+Unpack: 1.599s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.9479 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325991.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000325991.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000329319.zst (54/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000329319.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,192B, BPFP=12.4167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,724B, BPFP=1.3155 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,768B, BPFP=11.0500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 16,592B, BPFP=1.3468 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 48,368B, BPFP=1.2269 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 38,248B, BPFP=0.5836 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 38,244B, BPFP=0.5836 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 16,528B, BPFP=0.5044 +⌛️ [2/4] FRONTEND: Frontend time: 2.141s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.593s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00021756 0.47760789 + text_encoder-item0.clip_prompt_embeds 0.00025929 34.71396019 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021916 0.51595564 + text_encoder_2-item1.clip_prompt_embeds 0.00042246 0.17787680 + text_encoder_3-item2.t5_prompt_embeds 0.00000781 0.00330897 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00675017 1.11712241 + vae.encoder_f1 0.00675421 1.11655557 + vae.decoder 0.00023635 0.03419824 + ------------------------------------------------------------------------------------- + TOTAL 0.00320042 3.67087652 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 231180 +BPFP 0.8180 bits/point +EBPFP 1.6360 equivalent bits/point +MSE 3.670877 +---------------------- -------------------------------------------------------- +Time: 3.742s Load: 0.008s, Pack+Encode: 2.141s, Decode+Unpack: 1.593s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.6709 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000329319.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000329319.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000335081.zst (55/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000335081.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,204B, BPFP=12.5417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,348B, BPFP=1.1293 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,824B, BPFP=11.4000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,112B, BPFP=1.2266 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 53,020B, BPFP=1.3449 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 43,980B, BPFP=0.6711 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 43,996B, BPFP=0.6713 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 16,508B, BPFP=0.5038 +⌛️ [2/4] FRONTEND: Frontend time: 2.133s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.599s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017133 0.42586303 + text_encoder-item0.clip_prompt_embeds 0.00064775 192.95038555 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00128483 0.49222889 + text_encoder_2-item1.clip_prompt_embeds 0.00019620 0.09326132 + text_encoder_3-item2.t5_prompt_embeds 0.00000792 0.00272250 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00728993 1.37458038 + vae.encoder_f1 0.00729572 1.37443697 + vae.decoder 0.00026488 0.03423963 + ------------------------------------------------------------------------------------- + TOTAL 0.00345536 7.92523547 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 244508 +BPFP 0.8651 bits/point +EBPFP 1.7303 equivalent bits/point +MSE 7.925235 +---------------------- -------------------------------------------------------- +Time: 3.741s Load: 0.009s, Pack+Encode: 2.133s, Decode+Unpack: 1.599s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 7.9252 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000335081.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000335081.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000342186.zst (56/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000342186.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,208B, BPFP=12.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,120B, BPFP=1.2338 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,800B, BPFP=11.2500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,288B, BPFP=1.2409 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 51,928B, BPFP=1.3172 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 37,148B, BPFP=0.5668 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 37,160B, BPFP=0.5670 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 17,588B, BPFP=0.5367 +⌛️ [2/4] FRONTEND: Frontend time: 2.134s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.593s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00042462 0.44859211 + text_encoder-item0.clip_prompt_embeds 0.00023188 23.78874332 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022679 0.47017231 + text_encoder_2-item1.clip_prompt_embeds 0.00015622 0.09324383 + text_encoder_3-item2.t5_prompt_embeds 0.00000782 0.00286183 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00613207 1.03075445 + vae.encoder_f1 0.00613899 1.02907562 + vae.decoder 0.00023812 0.03482801 + ------------------------------------------------------------------------------------- + TOTAL 0.00290239 3.34110169 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 231756 +BPFP 0.8200 bits/point +EBPFP 1.6400 equivalent bits/point +MSE 3.341102 +---------------------- -------------------------------------------------------- +Time: 3.735s Load: 0.009s, Pack+Encode: 2.134s, Decode+Unpack: 1.593s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.3411 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000342186.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000342186.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000343976.zst (57/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000343976.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,200B, BPFP=12.5000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,560B, BPFP=1.2933 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,788B, BPFP=11.1750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 16,012B, BPFP=1.2997 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 50,200B, BPFP=1.2733 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 32,044B, BPFP=0.4890 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 32,044B, BPFP=0.4890 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 16,632B, BPFP=0.5076 +⌛️ [2/4] FRONTEND: Frontend time: 2.143s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.590s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019246 0.41998323 + text_encoder-item0.clip_prompt_embeds 0.00023678 34.71757897 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00028948 0.52507277 + text_encoder_2-item1.clip_prompt_embeds 0.00019061 0.12367758 + text_encoder_3-item2.t5_prompt_embeds 0.00000767 0.00346832 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00636537 1.06179225 + vae.encoder_f1 0.00636991 1.05755377 + vae.decoder 0.00025538 0.03123634 + ------------------------------------------------------------------------------------- + TOTAL 0.00301360 3.64176119 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 219996 +BPFP 0.7784 bits/point +EBPFP 1.5568 equivalent bits/point +MSE 3.641761 +---------------------- -------------------------------------------------------- +Time: 3.740s Load: 0.007s, Pack+Encode: 2.143s, Decode+Unpack: 1.590s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.6418 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000343976.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000343976.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000351362.zst (58/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000351362.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,144B, BPFP=11.9167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,916B, BPFP=1.2062 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,772B, BPFP=11.0750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,072B, BPFP=1.1422 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 46,748B, BPFP=1.1858 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 30,424B, BPFP=0.4642 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 30,428B, BPFP=0.4643 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 11,500B, BPFP=0.3510 +⌛️ [2/4] FRONTEND: Frontend time: 2.148s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.590s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036983 0.50685195 + text_encoder-item0.clip_prompt_embeds 0.00023432 240.53578193 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018703 0.44241829 + text_encoder_2-item1.clip_prompt_embeds 0.00017889 0.10296890 + text_encoder_3-item2.t5_prompt_embeds 0.00000811 0.00366683 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.23155926 2.34172797 + vae.encoder_f1 0.23156048 2.34168983 + vae.decoder 0.00018572 0.02446380 + ------------------------------------------------------------------------------------- + TOTAL 0.10744199 9.61780374 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 205520 +BPFP 0.7272 bits/point +EBPFP 1.4544 equivalent bits/point +MSE 9.617804 +---------------------- -------------------------------------------------------- +Time: 3.746s Load: 0.008s, Pack+Encode: 2.148s, Decode+Unpack: 1.590s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 9.6178 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000351362.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000351362.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000357816.zst (59/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000357816.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,172B, BPFP=12.2083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,316B, BPFP=1.1250 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,796B, BPFP=11.2250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,540B, BPFP=1.1802 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 48,628B, BPFP=1.2335 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 39,040B, BPFP=0.5957 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 39,028B, BPFP=0.5955 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 20,264B, BPFP=0.6184 +⌛️ [2/4] FRONTEND: Frontend time: 2.132s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.597s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020740 0.48705522 + text_encoder-item0.clip_prompt_embeds 0.00022528 23.73543823 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022839 0.48944898 + text_encoder_2-item1.clip_prompt_embeds 0.00016484 0.10558053 + text_encoder_3-item2.t5_prompt_embeds 0.00000786 0.00340376 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00729824 1.09825361 + vae.encoder_f1 0.00730369 1.09894931 + vae.decoder 0.00019938 0.03905215 + ------------------------------------------------------------------------------------- + TOTAL 0.00343853 3.37268918 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 233300 +BPFP 0.8255 bits/point +EBPFP 1.6510 equivalent bits/point +MSE 3.372689 +---------------------- -------------------------------------------------------- +Time: 3.737s Load: 0.008s, Pack+Encode: 2.132s, Decode+Unpack: 1.597s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.3727 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000357816.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000357816.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361180.zst (60/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361180.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,184B, BPFP=12.3333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,192B, BPFP=1.1082 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,784B, BPFP=11.1500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,204B, BPFP=1.2341 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 47,852B, BPFP=1.2138 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 25,632B, BPFP=0.3911 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 25,628B, BPFP=0.3911 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 24,156B, BPFP=0.7372 +⌛️ [2/4] FRONTEND: Frontend time: 2.129s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.595s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00021207 0.44232086 + text_encoder-item0.clip_prompt_embeds 0.00022149 59.09413048 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018477 0.49665632 + text_encoder_2-item1.clip_prompt_embeds 0.00103146 0.12555184 + text_encoder_3-item2.t5_prompt_embeds 0.00000778 0.00276202 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00564371 0.76466203 + vae.encoder_f1 0.00565042 0.76463389 + vae.decoder 0.00019980 0.04302636 + ------------------------------------------------------------------------------------- + TOTAL 0.00270919 4.14384567 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 210148 +BPFP 0.7436 bits/point +EBPFP 1.4871 equivalent bits/point +MSE 4.143846 +---------------------- -------------------------------------------------------- +Time: 3.732s Load: 0.008s, Pack+Encode: 2.129s, Decode+Unpack: 1.595s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.1438 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361180.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000361180.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361268.zst (61/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361268.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,160B, BPFP=12.0833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,992B, BPFP=1.2165 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,788B, BPFP=11.1750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 16,000B, BPFP=1.2987 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 48,924B, BPFP=1.2410 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 28,176B, BPFP=0.4299 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 28,168B, BPFP=0.4298 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 22,984B, BPFP=0.7014 +⌛️ [2/4] FRONTEND: Frontend time: 2.128s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.620s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017717 0.46130832 + text_encoder-item0.clip_prompt_embeds 0.00022173 23.75782730 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022739 0.51225328 + text_encoder_2-item1.clip_prompt_embeds 0.00103962 0.10191623 + text_encoder_3-item2.t5_prompt_embeds 0.00000788 0.00282104 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00576096 0.69161808 + vae.encoder_f1 0.00576981 0.69123167 + vae.decoder 0.00019592 0.03937647 + ------------------------------------------------------------------------------------- + TOTAL 0.00276400 3.18423999 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 216708 +BPFP 0.7668 bits/point +EBPFP 1.5335 equivalent bits/point +MSE 3.184240 +---------------------- -------------------------------------------------------- +Time: 3.756s Load: 0.008s, Pack+Encode: 2.128s, Decode+Unpack: 1.620s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.1842 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361268.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000361268.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000367228.zst (62/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000367228.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,200B, BPFP=12.5000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 10,104B, BPFP=1.3669 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,760B, BPFP=11.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 17,052B, BPFP=1.3841 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 55,708B, BPFP=1.4130 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 22,984B, BPFP=0.3507 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 22,988B, BPFP=0.3508 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 14,476B, BPFP=0.4418 +⌛️ [2/4] FRONTEND: Frontend time: 2.167s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.603s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00024069 0.46090110 + text_encoder-item0.clip_prompt_embeds 0.00025917 34.81185741 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023350 0.52453694 + text_encoder_2-item1.clip_prompt_embeds 0.00019057 0.11115699 + text_encoder_3-item2.t5_prompt_embeds 0.00000791 0.00279740 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00594818 0.76600564 + vae.encoder_f1 0.00595328 0.76770079 + vae.decoder 0.00023462 0.02941078 + ------------------------------------------------------------------------------------- + TOTAL 0.00281845 3.50758910 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 206788 +BPFP 0.7317 bits/point +EBPFP 1.4633 equivalent bits/point +MSE 3.507589 +---------------------- -------------------------------------------------------- +Time: 3.778s Load: 0.008s, Pack+Encode: 2.167s, Decode+Unpack: 1.603s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.5076 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000367228.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000367228.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000369503.zst (63/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000369503.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,188B, BPFP=12.3750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 10,428B, BPFP=1.4107 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,824B, BPFP=11.4000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 18,868B, BPFP=1.5315 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 54,248B, BPFP=1.3760 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 20,400B, BPFP=0.3113 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 20,396B, BPFP=0.3112 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 7,696B, BPFP=0.2349 +⌛️ [2/4] FRONTEND: Frontend time: 2.138s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.592s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022245 0.43409773 + text_encoder-item0.clip_prompt_embeds 0.00022579 45.75819721 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020263 0.47870159 + text_encoder_2-item1.clip_prompt_embeds 0.00017578 0.10026481 + text_encoder_3-item2.t5_prompt_embeds 0.00000800 0.00242116 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.85445058 2.92618895 + vae.encoder_f1 0.85445166 2.93005276 + vae.decoder 0.00025257 0.01451966 + ------------------------------------------------------------------------------------- + TOTAL 0.39632643 4.79392760 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 195564 +BPFP 0.6920 bits/point +EBPFP 1.3839 equivalent bits/point +MSE 4.793928 +---------------------- -------------------------------------------------------- +Time: 3.738s Load: 0.008s, Pack+Encode: 2.138s, Decode+Unpack: 1.592s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.7939 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000369503.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000369503.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000370486.zst (64/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000370486.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,208B, BPFP=12.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,412B, BPFP=1.1380 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,800B, BPFP=11.2500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,288B, BPFP=1.2409 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 49,468B, BPFP=1.2548 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 42,300B, BPFP=0.6454 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 42,284B, BPFP=0.6452 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 23,744B, BPFP=0.7246 +⌛️ [2/4] FRONTEND: Frontend time: 2.152s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.588s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00057152 0.44768270 + text_encoder-item0.clip_prompt_embeds 0.00025458 34.74035275 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00158787 0.49796829 + text_encoder_2-item1.clip_prompt_embeds 0.00016969 0.15242668 + text_encoder_3-item2.t5_prompt_embeds 0.00000826 0.00287954 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00628510 1.03222215 + vae.encoder_f1 0.00629234 1.03380632 + vae.decoder 0.00023521 0.04193567 + ------------------------------------------------------------------------------------- + TOTAL 0.00297516 3.63239900 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 245020 +BPFP 0.8669 bits/point +EBPFP 1.7339 equivalent bits/point +MSE 3.632399 +---------------------- -------------------------------------------------------- +Time: 3.748s Load: 0.008s, Pack+Encode: 2.152s, Decode+Unpack: 1.588s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.6324 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000370486.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000370486.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377635.zst (65/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377635.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,184B, BPFP=12.3333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,652B, BPFP=1.1705 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,840B, BPFP=11.5000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,028B, BPFP=1.2198 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 51,652B, BPFP=1.3102 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 29,296B, BPFP=0.4470 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 29,312B, BPFP=0.4473 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 20,140B, BPFP=0.6146 +⌛️ [2/4] FRONTEND: Frontend time: 2.142s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.586s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00037564 0.49119564 + text_encoder-item0.clip_prompt_embeds 0.00022807 34.79762116 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00029471 0.52520227 + text_encoder_2-item1.clip_prompt_embeds 0.00018746 0.10594854 + text_encoder_3-item2.t5_prompt_embeds 0.00000782 0.00327070 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00573429 0.73479140 + vae.encoder_f1 0.00574192 0.73415607 + vae.decoder 0.00017875 0.03202675 + ------------------------------------------------------------------------------------- + TOTAL 0.00271248 3.49235313 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 217620 +BPFP 0.7700 bits/point +EBPFP 1.5400 equivalent bits/point +MSE 3.492353 +---------------------- -------------------------------------------------------- +Time: 3.736s Load: 0.008s, Pack+Encode: 2.142s, Decode+Unpack: 1.586s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.4924 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377635.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000377635.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377814.zst (66/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377814.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,244B, BPFP=12.9583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 11,096B, BPFP=1.5011 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,792B, BPFP=11.2000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 19,180B, BPFP=1.5568 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 67,388B, BPFP=1.7093 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 36,980B, BPFP=0.5643 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 36,976B, BPFP=0.5642 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 19,988B, BPFP=0.6100 +⌛️ [2/4] FRONTEND: Frontend time: 2.144s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.597s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017150 0.45057066 + text_encoder-item0.clip_prompt_embeds 0.00027120 34.72550984 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023509 0.47388053 + text_encoder_2-item1.clip_prompt_embeds 0.00019567 0.10800773 + text_encoder_3-item2.t5_prompt_embeds 0.00000829 0.00238023 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00781570 1.24690151 + vae.encoder_f1 0.00781878 1.25082362 + vae.decoder 0.00029724 0.03901247 + ------------------------------------------------------------------------------------- + TOTAL 0.00369190 3.72975684 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 255160 +BPFP 0.9028 bits/point +EBPFP 1.8056 equivalent bits/point +MSE 3.729757 +---------------------- -------------------------------------------------------- +Time: 3.750s Load: 0.008s, Pack+Encode: 2.144s, Decode+Unpack: 1.597s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.7298 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377814.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000377814.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000379800.zst (67/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000379800.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,236B, BPFP=12.8750 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,904B, BPFP=1.2045 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,720B, BPFP=10.7500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 16,316B, BPFP=1.3244 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 49,792B, BPFP=1.2630 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 34,612B, BPFP=0.5281 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 34,624B, BPFP=0.5283 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 26,040B, BPFP=0.7947 +⌛️ [2/4] FRONTEND: Frontend time: 2.140s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.594s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018216 0.48237896 + text_encoder-item0.clip_prompt_embeds 0.00022930 23.77835625 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00047978 0.46506863 + text_encoder_2-item1.clip_prompt_embeds 0.00018160 0.10602206 + text_encoder_3-item2.t5_prompt_embeds 0.00000828 0.00317679 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00577752 0.76808870 + vae.encoder_f1 0.00578475 0.76799005 + vae.decoder 0.00024190 0.04097613 + ------------------------------------------------------------------------------------- + TOTAL 0.00273964 3.22070280 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 233760 +BPFP 0.8271 bits/point +EBPFP 1.6542 equivalent bits/point +MSE 3.220703 +---------------------- -------------------------------------------------------- +Time: 3.741s Load: 0.008s, Pack+Encode: 2.140s, Decode+Unpack: 1.594s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.2207 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000379800.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000379800.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000384808.zst (68/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000384808.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,228B, BPFP=12.7917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 10,516B, BPFP=1.4226 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,928B, BPFP=12.0500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 17,548B, BPFP=1.4244 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 56,432B, BPFP=1.4314 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 35,760B, BPFP=0.5457 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 35,748B, BPFP=0.5455 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 9,568B, BPFP=0.2920 +⌛️ [2/4] FRONTEND: Frontend time: 2.130s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.599s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00047293 0.51752468 + text_encoder-item0.clip_prompt_embeds 0.00028764 34.80634047 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021081 0.49496937 + text_encoder_2-item1.clip_prompt_embeds 0.00018283 0.10231559 + text_encoder_3-item2.t5_prompt_embeds 0.00000777 0.00274062 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.03343784 1.58568323 + vae.encoder_f1 0.03344063 1.59815967 + vae.decoder 0.00016139 0.01906416 + ------------------------------------------------------------------------------------- + TOTAL 0.01555870 3.88849470 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 229244 +BPFP 0.8111 bits/point +EBPFP 1.6223 equivalent bits/point +MSE 3.888495 +---------------------- -------------------------------------------------------- +Time: 3.738s Load: 0.009s, Pack+Encode: 2.130s, Decode+Unpack: 1.599s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.8885 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000384808.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000384808.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000396338.zst (69/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000396338.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,156B, BPFP=12.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,016B, BPFP=1.0844 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,756B, BPFP=10.9750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,004B, BPFP=1.2179 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 50,028B, BPFP=1.2690 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 42,124B, BPFP=0.6428 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 42,112B, BPFP=0.6426 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 22,500B, BPFP=0.6866 +⌛️ [2/4] FRONTEND: Frontend time: 2.125s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.596s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00559742 0.41618721 + text_encoder-item0.clip_prompt_embeds 0.00023094 36.38594511 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00027942 0.47194624 + text_encoder_2-item1.clip_prompt_embeds 0.00018965 0.11565992 + text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00362324 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00637455 1.05876386 + vae.encoder_f1 0.00637988 1.05708015 + vae.decoder 0.00020059 0.03687311 + ------------------------------------------------------------------------------------- + TOTAL 0.00301333 3.68487935 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 243212 +BPFP 0.8605 bits/point +EBPFP 1.7211 equivalent bits/point +MSE 3.684879 +---------------------- -------------------------------------------------------- +Time: 3.729s Load: 0.008s, Pack+Encode: 2.125s, Decode+Unpack: 1.596s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.6849 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000396338.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000396338.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000397303.zst (70/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000397303.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,200B, BPFP=12.5000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,192B, BPFP=1.2435 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,852B, BPFP=11.5750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,920B, BPFP=1.2922 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 52,060B, BPFP=1.3205 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 33,468B, BPFP=0.5107 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 33,472B, BPFP=0.5107 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 21,924B, BPFP=0.6691 +⌛️ [2/4] FRONTEND: Frontend time: 2.130s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.596s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00036729 0.48624551 + text_encoder-item0.clip_prompt_embeds 0.00025217 23.78151634 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00026091 0.50754614 + text_encoder_2-item1.clip_prompt_embeds 0.00018200 0.08762162 + text_encoder_3-item2.t5_prompt_embeds 0.00000809 0.00271142 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00581597 0.80003428 + vae.encoder_f1 0.00582356 0.80029607 + vae.decoder 0.00019494 0.03627559 + ------------------------------------------------------------------------------------- + TOTAL 0.00275264 3.23429773 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 229604 +BPFP 0.8124 bits/point +EBPFP 1.6248 equivalent bits/point +MSE 3.234298 +---------------------- -------------------------------------------------------- +Time: 3.734s Load: 0.008s, Pack+Encode: 2.130s, Decode+Unpack: 1.596s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.2343 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000397303.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000397303.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000402473.zst (71/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000402473.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,144B, BPFP=11.9167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,612B, BPFP=1.0298 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,832B, BPFP=11.4500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,296B, BPFP=1.0792 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 46,960B, BPFP=1.1912 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 18,632B, BPFP=0.2843 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 18,636B, BPFP=0.2844 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 16,012B, BPFP=0.4886 +⌛️ [2/4] FRONTEND: Frontend time: 2.138s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.601s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00799810 0.52283780 + text_encoder-item0.clip_prompt_embeds 0.00026975 45.69621719 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022593 0.48835773 + text_encoder_2-item1.clip_prompt_embeds 0.00015480 0.08718614 + text_encoder_3-item2.t5_prompt_embeds 0.00000862 0.00401413 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 1.11695218 3.16196227 + vae.encoder_f1 1.11695278 3.15613699 + vae.decoder 0.00019720 0.03228044 + ------------------------------------------------------------------------------------- + TOTAL 0.51806274 4.90115084 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 184640 +BPFP 0.6533 bits/point +EBPFP 1.3066 equivalent bits/point +MSE 4.901151 +---------------------- -------------------------------------------------------- +Time: 3.746s Load: 0.007s, Pack+Encode: 2.138s, Decode+Unpack: 1.601s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.9012 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000402473.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000402473.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000409211.zst (72/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000409211.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,196B, BPFP=12.4583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,100B, BPFP=1.2311 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,720B, BPFP=10.7500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,160B, BPFP=1.2305 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 52,108B, BPFP=1.3217 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 33,348B, BPFP=0.5089 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 33,348B, BPFP=0.5089 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 18,324B, BPFP=0.5592 +⌛️ [2/4] FRONTEND: Frontend time: 2.139s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.597s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00023525 0.43361056 + text_encoder-item0.clip_prompt_embeds 0.00025545 34.75243295 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018422 0.44552965 + text_encoder_2-item1.clip_prompt_embeds 0.00016916 0.08970812 + text_encoder_3-item2.t5_prompt_embeds 0.00000823 0.00368267 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.01535016 1.31031406 + vae.encoder_f1 0.01535382 1.30726457 + vae.decoder 0.00021460 0.03396961 + ------------------------------------------------------------------------------------- + TOTAL 0.00717511 3.75703061 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 224820 +BPFP 0.7955 bits/point +EBPFP 1.5909 equivalent bits/point +MSE 3.757031 +---------------------- -------------------------------------------------------- +Time: 3.746s Load: 0.009s, Pack+Encode: 2.139s, Decode+Unpack: 1.597s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.7570 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000409211.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000409211.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000427500.zst (73/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000427500.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,224B, BPFP=12.7500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,776B, BPFP=1.3225 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,796B, BPFP=11.2250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 17,072B, BPFP=1.3857 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 54,704B, BPFP=1.3876 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 28,024B, BPFP=0.4276 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 28,028B, BPFP=0.4277 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 26,460B, BPFP=0.8075 +⌛️ [2/4] FRONTEND: Frontend time: 2.169s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.602s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020648 0.46799791 + text_encoder-item0.clip_prompt_embeds 0.00022628 34.79392840 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00027089 0.49829984 + text_encoder_2-item1.clip_prompt_embeds 0.00017658 0.09784645 + text_encoder_3-item2.t5_prompt_embeds 0.00000761 0.00271137 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00589589 0.71368605 + vae.encoder_f1 0.00590398 0.71116972 + vae.decoder 0.00017838 0.04934765 + ------------------------------------------------------------------------------------- + TOTAL 0.00278687 3.48358629 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 227600 +BPFP 0.8053 bits/point +EBPFP 1.6106 equivalent bits/point +MSE 3.483586 +---------------------- -------------------------------------------------------- +Time: 3.780s Load: 0.009s, Pack+Encode: 2.169s, Decode+Unpack: 1.602s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.4836 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000427500.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000427500.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435208.zst (74/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435208.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,208B, BPFP=12.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,640B, BPFP=1.1688 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,776B, BPFP=11.1000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,428B, BPFP=1.2523 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 46,844B, BPFP=1.1882 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 40,256B, BPFP=0.6143 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 40,260B, BPFP=0.6143 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 13,556B, BPFP=0.4137 +⌛️ [2/4] FRONTEND: Frontend time: 2.166s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.600s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00045802 0.41781183 + text_encoder-item0.clip_prompt_embeds 0.00031548 34.71815180 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020720 0.50863209 + text_encoder_2-item1.clip_prompt_embeds 0.00018318 0.11009823 + text_encoder_3-item2.t5_prompt_embeds 0.00000772 0.00517467 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00725484 1.28478658 + vae.encoder_f1 0.00725992 1.28485584 + vae.decoder 0.00019960 0.02746098 + ------------------------------------------------------------------------------------- + TOTAL 0.00342155 3.74539104 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 228484 +BPFP 0.8084 bits/point +EBPFP 1.6169 equivalent bits/point +MSE 3.745391 +---------------------- -------------------------------------------------------- +Time: 3.775s Load: 0.009s, Pack+Encode: 2.166s, Decode+Unpack: 1.600s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.7454 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435208.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000435208.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435880.zst (75/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435880.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,200B, BPFP=12.5000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,828B, BPFP=1.0590 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,956B, BPFP=12.2250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 13,384B, BPFP=1.0864 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 45,252B, BPFP=1.1478 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 34,392B, BPFP=0.5248 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 34,372B, BPFP=0.5245 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 13,476B, BPFP=0.4113 +⌛️ [2/4] FRONTEND: Frontend time: 2.153s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.591s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00061068 0.47611145 + text_encoder-item0.clip_prompt_embeds 0.00021831 23.77034294 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025602 0.52192235 + text_encoder_2-item1.clip_prompt_embeds 0.00016110 0.09446406 + text_encoder_3-item2.t5_prompt_embeds 0.00000740 0.00374355 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00923516 1.15983546 + vae.encoder_f1 0.00923823 1.16510224 + vae.decoder 0.00019521 0.02282491 + ------------------------------------------------------------------------------------- + TOTAL 0.00433552 3.40091783 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 212376 +BPFP 0.7514 bits/point +EBPFP 1.5029 equivalent bits/point +MSE 3.400918 +---------------------- -------------------------------------------------------- +Time: 3.753s Load: 0.008s, Pack+Encode: 2.153s, Decode+Unpack: 1.591s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.4009 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435880.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000435880.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000439593.zst (76/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000439593.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,192B, BPFP=12.4167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,160B, BPFP=1.1039 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,776B, BPFP=11.1000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,784B, BPFP=1.2812 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 50,988B, BPFP=1.2933 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 36,060B, BPFP=0.5502 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 36,072B, BPFP=0.5504 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 16,356B, BPFP=0.4991 +⌛️ [2/4] FRONTEND: Frontend time: 2.143s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.598s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00028585 0.46165419 + text_encoder-item0.clip_prompt_embeds 0.00062166 46.99147727 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00050487 0.48568587 + text_encoder_2-item1.clip_prompt_embeds 0.00018638 0.09483017 + text_encoder_3-item2.t5_prompt_embeds 0.00000762 0.00451263 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00831779 1.18807089 + vae.encoder_f1 0.00832197 1.18809283 + vae.decoder 0.00023271 0.02911749 + ------------------------------------------------------------------------------------- + TOTAL 0.00392639 4.02096996 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 226904 +BPFP 0.8028 bits/point +EBPFP 1.6057 equivalent bits/point +MSE 4.020970 +---------------------- -------------------------------------------------------- +Time: 3.749s Load: 0.009s, Pack+Encode: 2.143s, Decode+Unpack: 1.598s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.0210 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000439593.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000439593.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000441286.zst (77/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000441286.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,232B, BPFP=12.8333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,108B, BPFP=1.2321 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,764B, BPFP=11.0250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,620B, BPFP=1.1867 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 51,948B, BPFP=1.3177 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 30,144B, BPFP=0.4600 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 30,152B, BPFP=0.4601 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 16,100B, BPFP=0.4913 +⌛️ [2/4] FRONTEND: Frontend time: 2.147s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.596s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019770 0.45685740 + text_encoder-item0.clip_prompt_embeds 0.00022938 23.84818469 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00028331 0.49875827 + text_encoder_2-item1.clip_prompt_embeds 0.00016501 0.08870876 + text_encoder_3-item2.t5_prompt_embeds 0.00000786 0.00385352 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00626977 0.88590831 + vae.encoder_f1 0.00627489 0.88549459 + vae.decoder 0.00017842 0.03323586 + ------------------------------------------------------------------------------------- + TOTAL 0.00295919 3.27554974 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 215584 +BPFP 0.7628 bits/point +EBPFP 1.5256 equivalent bits/point +MSE 3.275550 +---------------------- -------------------------------------------------------- +Time: 3.751s Load: 0.008s, Pack+Encode: 2.147s, Decode+Unpack: 1.596s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.2755 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000441286.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000441286.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000445365.zst (78/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000445365.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,172B, BPFP=12.2083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,972B, BPFP=1.2137 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,772B, BPFP=11.0750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 16,220B, BPFP=1.3166 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 50,872B, BPFP=1.2904 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 33,016B, BPFP=0.5038 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 33,012B, BPFP=0.5037 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 24,896B, BPFP=0.7598 +⌛️ [2/4] FRONTEND: Frontend time: 2.149s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.600s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022406 0.42580831 + text_encoder-item0.clip_prompt_embeds 0.00022180 23.78970086 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00120074 0.46295729 + text_encoder_2-item1.clip_prompt_embeds 0.00017918 0.14772949 + text_encoder_3-item2.t5_prompt_embeds 0.00000774 0.00369611 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00585720 0.81665552 + vae.encoder_f1 0.00586586 0.81700784 + vae.decoder 0.00016520 0.04339989 + ------------------------------------------------------------------------------------- + TOTAL 0.00276807 3.24577897 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 230448 +BPFP 0.8154 bits/point +EBPFP 1.6308 equivalent bits/point +MSE 3.245779 +---------------------- -------------------------------------------------------- +Time: 3.756s Load: 0.008s, Pack+Encode: 2.149s, Decode+Unpack: 1.600s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.2458 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000445365.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000445365.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000449996.zst (79/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000449996.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,176B, BPFP=12.2500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,592B, BPFP=1.1623 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,628B, BPFP=10.1750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,352B, BPFP=1.2461 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 49,496B, BPFP=1.2555 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 25,112B, BPFP=0.3832 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 25,116B, BPFP=0.3832 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 12,860B, BPFP=0.3925 +⌛️ [2/4] FRONTEND: Frontend time: 2.142s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.591s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00265765 0.47419707 + text_encoder-item0.clip_prompt_embeds 0.00025784 47.11869251 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00017733 0.42954121 + text_encoder_2-item1.clip_prompt_embeds 0.00015430 0.09391621 + text_encoder_3-item2.t5_prompt_embeds 0.00000823 0.00309843 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00734802 0.98697448 + vae.encoder_f1 0.00734987 0.98773235 + vae.decoder 0.00018093 0.01959931 + ------------------------------------------------------------------------------------- + TOTAL 0.00345989 3.92983761 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 199848 +BPFP 0.7071 bits/point +EBPFP 1.4142 equivalent bits/point +MSE 3.929838 +---------------------- -------------------------------------------------------- +Time: 3.742s Load: 0.008s, Pack+Encode: 2.142s, Decode+Unpack: 1.591s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.9298 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000449996.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000449996.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000451714.zst (80/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000451714.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,172B, BPFP=12.2083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,348B, BPFP=1.2646 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,752B, BPFP=10.9500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,948B, BPFP=1.2945 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 49,552B, BPFP=1.2569 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 40,292B, BPFP=0.6148 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 40,300B, BPFP=0.6149 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 19,648B, BPFP=0.5996 +⌛️ [2/4] FRONTEND: Frontend time: 2.152s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.600s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00019649 0.47649972 + text_encoder-item0.clip_prompt_embeds 0.00023510 24.40224229 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023039 0.46485271 + text_encoder_2-item1.clip_prompt_embeds 0.00019044 0.10343945 + text_encoder_3-item2.t5_prompt_embeds 0.00000826 0.00238746 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00637359 1.06608331 + vae.encoder_f1 0.00637830 1.06628251 + vae.decoder 0.00018566 0.03637500 + ------------------------------------------------------------------------------------- + TOTAL 0.00300937 3.37453167 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 238528 +BPFP 0.8440 bits/point +EBPFP 1.6880 equivalent bits/point +MSE 3.374532 +---------------------- -------------------------------------------------------- +Time: 3.761s Load: 0.008s, Pack+Encode: 2.152s, Decode+Unpack: 1.600s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.3745 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000451714.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000451714.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000464358.zst (81/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000464358.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,200B, BPFP=12.5000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,556B, BPFP=1.1575 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,780B, BPFP=11.1250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,952B, BPFP=1.2948 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 50,916B, BPFP=1.2915 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 29,000B, BPFP=0.4425 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 28,992B, BPFP=0.4424 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 12,352B, BPFP=0.3770 +⌛️ [2/4] FRONTEND: Frontend time: 2.150s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.594s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018476 0.45591418 + text_encoder-item0.clip_prompt_embeds 0.00026418 46.82802777 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018200 0.47615943 + text_encoder_2-item1.clip_prompt_embeds 0.00017999 0.10602628 + text_encoder_3-item2.t5_prompt_embeds 0.00000755 0.00321681 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.01530954 1.19483137 + vae.encoder_f1 0.01531230 1.19441748 + vae.decoder 0.00017892 0.02313200 + ------------------------------------------------------------------------------------- + TOTAL 0.00715252 4.01933517 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 209264 +BPFP 0.7404 bits/point +EBPFP 1.4809 equivalent bits/point +MSE 4.019335 +---------------------- -------------------------------------------------------- +Time: 3.752s Load: 0.008s, Pack+Encode: 2.150s, Decode+Unpack: 1.594s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.0193 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000464358.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000464358.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000466256.zst (82/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000466256.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,208B, BPFP=12.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,476B, BPFP=1.2819 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,744B, BPFP=10.9000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 16,384B, BPFP=1.3299 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 54,028B, BPFP=1.3704 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 36,852B, BPFP=0.5623 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 36,840B, BPFP=0.5621 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 19,464B, BPFP=0.5940 +⌛️ [2/4] FRONTEND: Frontend time: 2.148s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.597s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018183 0.41863004 + text_encoder-item0.clip_prompt_embeds 0.00021481 34.71803977 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00019636 0.44132261 + text_encoder_2-item1.clip_prompt_embeds 0.00020983 0.09789161 + text_encoder_3-item2.t5_prompt_embeds 0.00000831 0.00296890 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00591154 1.10813034 + vae.encoder_f1 0.00591973 1.11055601 + vae.decoder 0.00025286 0.03590653 + ------------------------------------------------------------------------------------- + TOTAL 0.00280398 3.66410856 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 236512 +BPFP 0.8368 bits/point +EBPFP 1.6737 equivalent bits/point +MSE 3.664109 +---------------------- -------------------------------------------------------- +Time: 3.753s Load: 0.009s, Pack+Encode: 2.148s, Decode+Unpack: 1.597s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.6641 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000466256.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000466256.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000467848.zst (83/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000467848.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,204B, BPFP=12.5417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,828B, BPFP=1.3295 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,728B, BPFP=10.8000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 17,076B, BPFP=1.3860 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 56,740B, BPFP=1.4392 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 23,384B, BPFP=0.3568 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 23,400B, BPFP=0.3571 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 21,336B, BPFP=0.6511 +⌛️ [2/4] FRONTEND: Frontend time: 2.142s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.594s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017556 0.46257889 + text_encoder-item0.clip_prompt_embeds 0.00023458 23.78372945 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00219611 0.53026948 + text_encoder_2-item1.clip_prompt_embeds 0.00186620 0.11905746 + text_encoder_3-item2.t5_prompt_embeds 0.00000775 0.00247829 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00588703 0.62546855 + vae.encoder_f1 0.00589573 0.62680340 + vae.decoder 0.00053402 0.03788324 + ------------------------------------------------------------------------------------- + TOTAL 0.00289910 3.15517545 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 215212 +BPFP 0.7615 bits/point +EBPFP 1.5230 equivalent bits/point +MSE 3.155175 +---------------------- -------------------------------------------------------- +Time: 3.744s Load: 0.007s, Pack+Encode: 2.142s, Decode+Unpack: 1.594s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.1552 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000467848.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000467848.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468501.zst (84/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468501.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,184B, BPFP=12.3333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,596B, BPFP=1.2982 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,760B, BPFP=11.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 16,628B, BPFP=1.3497 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 53,068B, BPFP=1.3461 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 33,660B, BPFP=0.5136 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 33,668B, BPFP=0.5137 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 13,156B, BPFP=0.4015 +⌛️ [2/4] FRONTEND: Frontend time: 2.150s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.600s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00027559 0.44462450 + text_encoder-item0.clip_prompt_embeds 0.00022882 278.63230519 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00110871 0.49260716 + text_encoder_2-item1.clip_prompt_embeds 0.00019473 0.09785789 + text_encoder_3-item2.t5_prompt_embeds 0.00000770 0.00286367 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00659691 1.06577623 + vae.encoder_f1 0.00660300 1.06645405 + vae.decoder 0.00023739 0.02606568 + ------------------------------------------------------------------------------------- + TOTAL 0.00311972 10.02249263 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 223236 +BPFP 0.7899 bits/point +EBPFP 1.5797 equivalent bits/point +MSE 10.022493 +---------------------- -------------------------------------------------------- +Time: 3.758s Load: 0.008s, Pack+Encode: 2.150s, Decode+Unpack: 1.600s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 10.0225 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468501.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000468501.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468632.zst (85/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468632.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,180B, BPFP=12.2917 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,972B, BPFP=1.2137 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,776B, BPFP=11.1000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,968B, BPFP=1.2961 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 49,164B, BPFP=1.2471 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 26,780B, BPFP=0.4086 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 26,780B, BPFP=0.4086 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 25,708B, BPFP=0.7845 +⌛️ [2/4] FRONTEND: Frontend time: 2.167s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.602s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00098754 0.47913905 + text_encoder-item0.clip_prompt_embeds 0.00023928 34.75040584 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022734 0.51477423 + text_encoder_2-item1.clip_prompt_embeds 0.00018899 0.10694828 + text_encoder_3-item2.t5_prompt_embeds 0.00000832 0.00302770 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00583864 0.67657036 + vae.encoder_f1 0.00583800 0.67657936 + vae.decoder 0.00018889 0.04320321 + ------------------------------------------------------------------------------------- + TOTAL 0.00276073 3.46556207 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 216844 +BPFP 0.7673 bits/point +EBPFP 1.5345 equivalent bits/point +MSE 3.465562 +---------------------- -------------------------------------------------------- +Time: 3.777s Load: 0.008s, Pack+Encode: 2.167s, Decode+Unpack: 1.602s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.4656 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468632.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000468632.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000471087.zst (86/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000471087.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,184B, BPFP=12.3333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,584B, BPFP=1.2965 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,720B, BPFP=10.7500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 16,612B, BPFP=1.3484 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 58,776B, BPFP=1.4909 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 21,748B, BPFP=0.3318 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 21,728B, BPFP=0.3315 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 21,324B, BPFP=0.6508 +⌛️ [2/4] FRONTEND: Frontend time: 2.170s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.596s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00032508 0.48489928 + text_encoder-item0.clip_prompt_embeds 0.00024821 48.50260417 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00060829 0.39562666 + text_encoder_2-item1.clip_prompt_embeds 0.00018297 0.10803685 + text_encoder_3-item2.t5_prompt_embeds 0.00002546 0.00241751 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00570467 0.59137046 + vae.encoder_f1 0.00570488 0.59181213 + vae.decoder 0.00017302 0.03520211 + ------------------------------------------------------------------------------------- + TOTAL 0.00269931 3.78480583 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 213192 +BPFP 0.7543 bits/point +EBPFP 1.5087 equivalent bits/point +MSE 3.784806 +---------------------- -------------------------------------------------------- +Time: 3.775s Load: 0.009s, Pack+Encode: 2.170s, Decode+Unpack: 1.596s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.7848 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000471087.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000471087.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000482477.zst (87/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000482477.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.007s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,212B, BPFP=12.6250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,216B, BPFP=1.2468 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,760B, BPFP=11.0000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,888B, BPFP=1.2896 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 50,564B, BPFP=1.2826 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 20,524B, BPFP=0.3132 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 20,532B, BPFP=0.3133 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 10,100B, BPFP=0.3082 +⌛️ [2/4] FRONTEND: Frontend time: 2.141s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.595s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00022393 0.45486259 + text_encoder-item0.clip_prompt_embeds 0.00021458 191.71054857 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020115 0.41207881 + text_encoder_2-item1.clip_prompt_embeds 0.00017334 0.11936844 + text_encoder_3-item2.t5_prompt_embeds 0.00000867 0.00501951 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00914783 1.10332310 + vae.encoder_f1 0.00914958 1.10326159 + vae.decoder 0.00017527 0.02260429 + ------------------------------------------------------------------------------------- + TOTAL 0.00429285 7.76710010 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 190312 +BPFP 0.6734 bits/point +EBPFP 1.3468 equivalent bits/point +MSE 7.767100 +---------------------- -------------------------------------------------------- +Time: 3.743s Load: 0.007s, Pack+Encode: 2.141s, Decode+Unpack: 1.595s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 7.7671 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000482477.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000482477.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499768.zst (88/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499768.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,156B, BPFP=12.0417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 10,628B, BPFP=1.4378 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,848B, BPFP=11.5500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 17,628B, BPFP=1.4308 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 56,300B, BPFP=1.4281 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 26,456B, BPFP=0.4037 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 26,456B, BPFP=0.4037 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 26,032B, BPFP=0.7944 +⌛️ [2/4] FRONTEND: Frontend time: 2.151s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.597s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00029464 0.45554968 + text_encoder-item0.clip_prompt_embeds 0.00022150 34.79223950 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00048959 0.49971581 + text_encoder_2-item1.clip_prompt_embeds 0.00016852 0.09341963 + text_encoder_3-item2.t5_prompt_embeds 0.00000767 0.00286682 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00578482 0.65561140 + vae.encoder_f1 0.00579739 0.65547901 + vae.decoder 0.00017668 0.04466816 + ------------------------------------------------------------------------------------- + TOTAL 0.00273588 3.45644448 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 227020 +BPFP 0.8033 bits/point +EBPFP 1.6065 equivalent bits/point +MSE 3.456444 +---------------------- -------------------------------------------------------- +Time: 3.757s Load: 0.008s, Pack+Encode: 2.151s, Decode+Unpack: 1.597s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.4564 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499768.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000499768.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499775.zst (89/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499775.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,160B, BPFP=12.0833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,192B, BPFP=1.1082 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,772B, BPFP=11.0750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,084B, BPFP=1.2244 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 48,552B, BPFP=1.2315 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 25,632B, BPFP=0.3911 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 25,620B, BPFP=0.3909 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 18,412B, BPFP=0.5619 +⌛️ [2/4] FRONTEND: Frontend time: 2.138s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.595s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00085811 0.49651615 + text_encoder-item0.clip_prompt_embeds 0.00023894 23.84522753 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00033417 0.51571798 + text_encoder_2-item1.clip_prompt_embeds 0.00016768 0.09949506 + text_encoder_3-item2.t5_prompt_embeds 0.00000784 0.00321781 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00958025 0.97719777 + vae.encoder_f1 0.00958229 0.97782356 + vae.decoder 0.00019995 0.03510182 + ------------------------------------------------------------------------------------- + TOTAL 0.00449688 3.31867152 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 204940 +BPFP 0.7251 bits/point +EBPFP 1.4503 equivalent bits/point +MSE 3.318672 +---------------------- -------------------------------------------------------- +Time: 3.741s Load: 0.008s, Pack+Encode: 2.138s, Decode+Unpack: 1.595s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.3187 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499775.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000499775.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000506454.zst (90/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000506454.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,164B, BPFP=12.1250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 10,320B, BPFP=1.3961 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,832B, BPFP=11.4500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 19,260B, BPFP=1.5633 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 56,524B, BPFP=1.4337 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 20,856B, BPFP=0.3182 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 20,856B, BPFP=0.3182 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 26,336B, BPFP=0.8037 +⌛️ [2/4] FRONTEND: Frontend time: 2.145s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.604s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017781 0.46653974 + text_encoder-item0.clip_prompt_embeds 0.00023387 311.56486742 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00060859 0.46180024 + text_encoder_2-item1.clip_prompt_embeds 0.00021718 0.10085756 + text_encoder_3-item2.t5_prompt_embeds 0.00000840 0.00260697 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00567713 0.60480934 + vae.encoder_f1 0.00567905 0.60489225 + vae.decoder 0.00019376 0.04953445 + ------------------------------------------------------------------------------------- + TOTAL 0.00268802 10.67272649 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 217664 +BPFP 0.7702 bits/point +EBPFP 1.5403 equivalent bits/point +MSE 10.672726 +---------------------- -------------------------------------------------------- +Time: 3.758s Load: 0.009s, Pack+Encode: 2.145s, Decode+Unpack: 1.604s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 10.6727 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000506454.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000506454.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000515828.zst (91/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000515828.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,208B, BPFP=12.5833 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,952B, BPFP=1.2110 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,768B, BPFP=11.0500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,692B, BPFP=1.2737 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 56,344B, BPFP=1.4292 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 26,508B, BPFP=0.4045 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 26,508B, BPFP=0.4045 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 15,860B, BPFP=0.4840 +⌛️ [2/4] FRONTEND: Frontend time: 2.146s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.595s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020194 0.42045033 + text_encoder-item0.clip_prompt_embeds 0.00024281 34.81082167 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020758 0.46979480 + text_encoder_2-item1.clip_prompt_embeds 0.00017819 0.09043293 + text_encoder_3-item2.t5_prompt_embeds 0.00000960 0.00239926 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.02387581 0.99439889 + vae.encoder_f1 0.02387858 0.99163628 + vae.decoder 0.00018648 0.02448732 + ------------------------------------------------------------------------------------- + TOTAL 0.01112583 3.61087534 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 213356 +BPFP 0.7549 bits/point +EBPFP 1.5098 equivalent bits/point +MSE 3.610875 +---------------------- -------------------------------------------------------- +Time: 3.749s Load: 0.008s, Pack+Encode: 2.146s, Decode+Unpack: 1.595s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.6109 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000515828.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000515828.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000517056.zst (92/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000517056.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,224B, BPFP=12.7500 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,104B, BPFP=1.2316 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,728B, BPFP=10.8000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,680B, BPFP=1.2727 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 50,448B, BPFP=1.2796 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 40,152B, BPFP=0.6127 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 40,132B, BPFP=0.6124 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 11,856B, BPFP=0.3618 +⌛️ [2/4] FRONTEND: Frontend time: 2.152s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.602s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018118 0.46035035 + text_encoder-item0.clip_prompt_embeds 0.00022399 34.80298803 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00031391 0.47728047 + text_encoder_2-item1.clip_prompt_embeds 0.00020480 0.10038954 + text_encoder_3-item2.t5_prompt_embeds 0.00000727 0.00353762 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.01169517 1.41546988 + vae.encoder_f1 0.01169969 1.41704273 + vae.decoder 0.00021186 0.02274050 + ------------------------------------------------------------------------------------- + TOTAL 0.00548058 3.80736315 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 230840 +BPFP 0.8168 bits/point +EBPFP 1.6335 equivalent bits/point +MSE 3.807363 +---------------------- -------------------------------------------------------- +Time: 3.763s Load: 0.009s, Pack+Encode: 2.152s, Decode+Unpack: 1.602s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.8074 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000517056.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000517056.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000523100.zst (93/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000523100.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,192B, BPFP=12.4167 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,892B, BPFP=1.2029 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,692B, BPFP=10.5750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,732B, BPFP=1.2769 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 56,172B, BPFP=1.4248 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 31,500B, BPFP=0.4807 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 31,496B, BPFP=0.4806 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 20,668B, BPFP=0.6307 +⌛️ [2/4] FRONTEND: Frontend time: 2.142s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.590s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00018108 0.44455918 + text_encoder-item0.clip_prompt_embeds 0.00022123 408.72920049 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020346 0.47382030 + text_encoder_2-item1.clip_prompt_embeds 0.00016509 0.09494475 + text_encoder_3-item2.t5_prompt_embeds 0.00000793 0.00282377 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.32749966 2.92837834 + vae.encoder_f1 0.32750070 2.92625999 + vae.decoder 0.00039956 0.03305019 + ------------------------------------------------------------------------------------- + TOTAL 0.15195981 14.28899645 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 227860 +BPFP 0.8062 bits/point +EBPFP 1.6125 equivalent bits/point +MSE 14.288996 +---------------------- -------------------------------------------------------- +Time: 3.740s Load: 0.008s, Pack+Encode: 2.142s, Decode+Unpack: 1.590s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 14.2890 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000523100.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000523100.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000526751.zst (94/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000526751.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,184B, BPFP=12.3333 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,332B, BPFP=1.2624 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,736B, BPFP=10.8500 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 16,080B, BPFP=1.3052 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 48,180B, BPFP=1.2221 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 26,772B, BPFP=0.4085 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 26,788B, BPFP=0.4088 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 20,368B, BPFP=0.6216 +⌛️ [2/4] FRONTEND: Frontend time: 2.137s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.583s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00109564 0.44690617 + text_encoder-item0.clip_prompt_embeds 0.00024675 34.76230849 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00084628 0.47967234 + text_encoder_2-item1.clip_prompt_embeds 0.00016730 0.09793231 + text_encoder_3-item2.t5_prompt_embeds 0.00000841 0.00272321 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00566967 0.70395315 + vae.encoder_f1 0.00567867 0.70340043 + vae.decoder 0.00017839 0.03391865 + ------------------------------------------------------------------------------------- + TOTAL 0.00268303 3.47689961 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 210956 +BPFP 0.7464 bits/point +EBPFP 1.4928 equivalent bits/point +MSE 3.476900 +---------------------- -------------------------------------------------------- +Time: 3.727s Load: 0.008s, Pack+Encode: 2.137s, Decode+Unpack: 1.583s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.4769 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000526751.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000526751.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000535578.zst (95/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000535578.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,196B, BPFP=12.4583 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 7,936B, BPFP=1.0736 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,836B, BPFP=11.4750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,108B, BPFP=1.1451 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 50,428B, BPFP=1.2791 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 25,668B, BPFP=0.3917 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 25,668B, BPFP=0.3917 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 27,784B, BPFP=0.8479 +⌛️ [2/4] FRONTEND: Frontend time: 2.139s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.591s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017308 0.39983439 + text_encoder-item0.clip_prompt_embeds 0.00022364 394.31892587 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00036756 0.53581328 + text_encoder_2-item1.clip_prompt_embeds 0.00015289 0.12576789 + text_encoder_3-item2.t5_prompt_embeds 0.00000784 0.00315060 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00580750 0.64619899 + vae.encoder_f1 0.00580664 0.64619195 + vae.decoder 0.00018044 0.05039147 + ------------------------------------------------------------------------------------- + TOTAL 0.00274301 12.85760455 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 215140 +BPFP 0.7612 bits/point +EBPFP 1.5224 equivalent bits/point +MSE 12.857605 +---------------------- -------------------------------------------------------- +Time: 3.738s Load: 0.008s, Pack+Encode: 2.139s, Decode+Unpack: 1.591s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 12.8576 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000535578.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000535578.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000546325.zst (96/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000546325.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,172B, BPFP=12.2083 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,608B, BPFP=1.1645 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,748B, BPFP=10.9250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,208B, BPFP=1.2344 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 47,808B, BPFP=1.2127 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 28,584B, BPFP=0.4362 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 28,568B, BPFP=0.4359 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 14,904B, BPFP=0.4548 +⌛️ [2/4] FRONTEND: Frontend time: 2.161s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.593s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00038620 0.46860909 + text_encoder-item0.clip_prompt_embeds 0.00030118 130.46824269 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020381 0.43329935 + text_encoder_2-item1.clip_prompt_embeds 0.00019649 0.09710002 + text_encoder_3-item2.t5_prompt_embeds 0.00000770 0.00369448 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.03869025 1.45730972 + vae.encoder_f1 0.03869358 1.45887792 + vae.decoder 0.00021614 0.02939026 + ------------------------------------------------------------------------------------- + TOTAL 0.01800198 6.32950778 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 207116 +BPFP 0.7328 bits/point +EBPFP 1.4657 equivalent bits/point +MSE 6.329508 +---------------------- -------------------------------------------------------- +Time: 3.762s Load: 0.009s, Pack+Encode: 2.161s, Decode+Unpack: 1.593s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.3295 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000546325.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000546325.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000551780.zst (97/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000551780.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,168B, BPFP=12.1667 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 8,464B, BPFP=1.1450 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,788B, BPFP=11.1750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 14,876B, BPFP=1.2075 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 48,256B, BPFP=1.2240 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 39,756B, BPFP=0.6066 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 39,752B, BPFP=0.6066 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 13,344B, BPFP=0.4072 +⌛️ [2/4] FRONTEND: Frontend time: 2.149s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.606s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00084877 0.47789145 + text_encoder-item0.clip_prompt_embeds 0.00023260 322.23390152 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00026409 0.51643081 + text_encoder_2-item1.clip_prompt_embeds 0.00016683 0.12122218 + text_encoder_3-item2.t5_prompt_embeds 0.00000828 0.00537749 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00839879 1.29318142 + vae.encoder_f1 0.00840224 1.29159129 + vae.decoder 0.00019463 0.02794579 + ------------------------------------------------------------------------------------- + TOTAL 0.00394849 11.26943693 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 227920 +BPFP 0.8064 bits/point +EBPFP 1.6129 equivalent bits/point +MSE 11.269437 +---------------------- -------------------------------------------------------- +Time: 3.763s Load: 0.008s, Pack+Encode: 2.149s, Decode+Unpack: 1.606s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 11.2694 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000551780.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000551780.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000555009.zst (98/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000555009.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.009s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,200B, BPFP=12.5000 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 10,612B, BPFP=1.4356 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,824B, BPFP=11.4000 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 17,828B, BPFP=1.4471 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 52,240B, BPFP=1.3251 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 36,784B, BPFP=0.5613 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 36,776B, BPFP=0.5612 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 13,620B, BPFP=0.4156 +⌛️ [2/4] FRONTEND: Frontend time: 2.155s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.604s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017723 0.44490616 + text_encoder-item0.clip_prompt_embeds 0.00023544 34.72738476 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022156 0.48182340 + text_encoder_2-item1.clip_prompt_embeds 0.00018986 0.10074099 + text_encoder_3-item2.t5_prompt_embeds 0.00000832 0.00310316 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.01160815 1.24669755 + vae.encoder_f1 0.01161249 1.24407470 + vae.decoder 0.00021720 0.02532651 + ------------------------------------------------------------------------------------- + TOTAL 0.00544054 3.72639348 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 231400 +BPFP 0.8188 bits/point +EBPFP 1.6375 equivalent bits/point +MSE 3.726393 +---------------------- -------------------------------------------------------- +Time: 3.768s Load: 0.009s, Pack+Encode: 2.155s, Decode+Unpack: 1.604s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 3.7264 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000555009.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000555009.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000565469.zst (99/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000565469.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,260B, BPFP=13.1250 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,440B, BPFP=1.2771 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,724B, BPFP=10.7750 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,876B, BPFP=1.2886 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 51,052B, BPFP=1.2949 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 23,204B, BPFP=0.3541 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 23,216B, BPFP=0.3542 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 11,844B, BPFP=0.3615 +⌛️ [2/4] FRONTEND: Frontend time: 2.139s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.601s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00017755 0.41759515 + text_encoder-item0.clip_prompt_embeds 0.00022923 57.78017620 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021530 0.49131541 + text_encoder_2-item1.clip_prompt_embeds 0.00015521 0.11124294 + text_encoder_3-item2.t5_prompt_embeds 0.00000740 0.00339015 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.02989292 1.41650891 + vae.encoder_f1 0.02989391 1.42024755 + vae.decoder 0.00034944 0.02332539 + ------------------------------------------------------------------------------------- + TOTAL 0.01393319 4.40982687 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 198132 +BPFP 0.7010 bits/point +EBPFP 1.4021 equivalent bits/point +MSE 4.409827 +---------------------- -------------------------------------------------------- +Time: 3.748s Load: 0.008s, Pack+Encode: 2.139s, Decode+Unpack: 1.601s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 4.4098 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000565469.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000565469.zst + + 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000575243.zst (100/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000575243.zst... + +Original data structure: +root: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.encoder-item6']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu +⌛️ [1/4] FRONTEND: Load time: 0.008s + +------------------------------------------------------------ +SD3.5 Features Summary +------------------------------------------------------------ +Number of text encoders: 3 +Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] +Has VAE latents: True +Has VAE encoder features: True +Data type: torch.float16 + text_encoder-item0: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item1: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item2: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + text_encoder-item3: + clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 + clip_prompt_embeds: torch.Size([77, 768]), torch.float16 + text_encoder_2-item4: + clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 + clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 + text_encoder_3-item5: + t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 + vae.decoder latents: torch.Size([16, 128, 128]) + vae.encoder features: + vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 + vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) + vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) + vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + text_encoder-item0.clip_pooled_prompt_embeds: 1,204B, BPFP=12.5417 + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + text_encoder-item0.clip_prompt_embeds: 9,800B, BPFP=1.3258 + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + text_encoder_2-item1.clip_pooled_prompt_embeds: 1,860B, BPFP=11.6250 + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + text_encoder_2-item1.clip_prompt_embeds: 15,896B, BPFP=1.2903 + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + text_encoder_3-item2.t5_prompt_embeds: 51,432B, BPFP=1.3046 + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + text_encoder-item3.clip_pooled_prompt_embeds: 1,396B, BPFP=14.5417 + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + text_encoder-item3.clip_prompt_embeds: 6,812B, BPFP=0.9215 + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + text_encoder_2-item4.clip_pooled_prompt_embeds: 2,272B, BPFP=14.2000 + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + text_encoder_2-item4.clip_prompt_embeds: 12,588B, BPFP=1.0218 + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + text_encoder_3-item5.t5_prompt_embeds: 37,448B, BPFP=0.9499 + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + vae.encoder_f0: 37,392B, BPFP=0.5706 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + vae.encoder_f1: 37,396B, BPFP=0.5706 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + vae.decoder: 19,044B, BPFP=0.5812 +⌛️ [2/4] FRONTEND: Frontend time: 2.148s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) +⌛️ [3/4] BACKEND: Backend time: 1.587s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds + Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds + Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds + Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 + Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 + Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder + IndividualUnPacker: + text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) + text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) + text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) + text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) + text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) + text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) + vae.decoder: torch.Size([16, 128, 128]) + vae.encoder_f0: torch.Size([32, 128, 128]) + vae.encoder_f1: torch.Size([32, 128, 128]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + text_encoder-item0.clip_pooled_prompt_embeds 0.00020076 0.48736111 + text_encoder-item0.clip_prompt_embeds 0.00024627 131.25132745 + text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020424 0.50625057 + text_encoder_2-item1.clip_prompt_embeds 0.00017521 0.09157033 + text_encoder_3-item2.t5_prompt_embeds 0.00000803 0.00460281 + text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.50219798 + text_encoder-item3.clip_prompt_embeds 0.00023247 85.14440442 + text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.28295259 + text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.10986713 + text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00204191 + vae.encoder_f0 0.00613025 0.90441656 + vae.encoder_f1 0.00613536 0.90338576 + vae.decoder 0.00018697 0.03413341 + ------------------------------------------------------------------------------------- + TOTAL 0.00289634 6.09345564 + (elements=2,260,992) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler sd35 +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 2260992 +Total Bytes 234540 +BPFP 0.8299 bits/point +EBPFP 1.6597 equivalent bits/point +MSE 6.093456 +---------------------- -------------------------------------------------------- +Time: 3.743s Load: 0.008s, Pack+Encode: 2.148s, Decode+Unpack: 1.587s +---------------------- -------------------------------------------------------- +Restored Feature Format: [Dict] with 8 keys + key['text_encoder-item0']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item1']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item2']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['text_encoder-item3']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu + key['text_encoder_2-item4']: [Dict] with 2 keys + key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu + key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu + key['text_encoder_3-item5']: [Dict] with 1 keys + key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu + key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder']: [Dict] with 2 keys + key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu + key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu +💾 Converting with 6.0935 MSE: + from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000575243.zst + to output-fixed/sd35/lambda0.01/elic-featurecoding-8bit-individual/sd35cond/000000575243.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 0.7726 bits/point +Avg EBPFP 1.5452 equivalent bits/point +Avg MSE 4.703740 +Avg Time 3.761s +------------------------ ----------------------------