Experiment: dtufc_hyperprior-featurecoding_sd35_individual Log file: output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/dtufc_hyperprior-featurecoding_sd35_individual.log DTUFCCodecConfig: arch: hyperprior-featurecoding handler: sd35 checkpoint: codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.02_epochs600_lr0.0001_bs180_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/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.02_epochs600_lr0.0001_bs180_patch256-256_checkpoint_best.pth.tar Checkpoint epoch: 598 Loaded hyperprior-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 hyperprior-featurecoding Checkpoint codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.02_epochs600_lr0.0001_bs180_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/lambda0.02/hyperprior-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: 0.009s ------------------------------------------------------------ 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: 2,340B, BPFP=24.3750 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: 13,680B, BPFP=1.8506 Using 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: 3,544B, BPFP=22.1500 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: 23,356B, BPFP=1.8958 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: 76,104B, BPFP=1.9304 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 65,512B, BPFP=0.9996 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 65,512B, BPFP=0.9996 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 36,588B, BPFP=1.1166 ⌛️ [2/4] FRONTEND: Frontend time: 0.701s (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: 0.523s [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 0.50440510 text_encoder-item0.clip_prompt_embeds 0.00025464 23.74437314 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020464 0.46889367 text_encoder_2-item1.clip_prompt_embeds 0.00016240 0.08079600 text_encoder_3-item2.t5_prompt_embeds 0.00000839 0.00144651 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00635250 0.72601670 vae.encoder_f1 0.00635834 0.72614378 vae.decoder 0.00019940 0.01955165 ------------------------------------------------------------------------------------- TOTAL 0.00300073 2.54451194 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 373700 BPFP 1.3223 bits/point EBPFP 2.6445 equivalent bits/point MSE 2.544512 ---------------------- -------------------------------------------------------- Time: 1.233s Load: 0.009s, Pack+Encode: 0.701s, Decode+Unpack: 0.523s ---------------------- -------------------------------------------------------- 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 2.5445 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002153.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 2,436B, BPFP=25.3750 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: 13,600B, BPFP=1.8398 Using 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: 3,752B, BPFP=23.4500 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: 23,376B, BPFP=1.8974 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: 74,032B, BPFP=1.8778 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 51,180B, BPFP=0.7809 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 51,180B, BPFP=0.7809 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 32,572B, BPFP=0.9940 ⌛️ [2/4] FRONTEND: Frontend time: 0.294s (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: 0.456s [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 0.47676484 text_encoder-item0.clip_prompt_embeds 0.00022609 48.18833705 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00019887 0.52637906 text_encoder_2-item1.clip_prompt_embeds 0.00019493 0.08576198 text_encoder_3-item2.t5_prompt_embeds 0.00000845 0.00130585 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.01130640 1.16958404 vae.encoder_f1 0.01130902 1.16797936 vae.decoder 0.00020860 0.01948095 ------------------------------------------------------------------------------------- TOTAL 0.00529919 3.38936379 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 339192 BPFP 1.2002 bits/point EBPFP 2.4003 equivalent bits/point MSE 3.389364 ---------------------- -------------------------------------------------------- Time: 0.758s Load: 0.008s, Pack+Encode: 0.294s, Decode+Unpack: 0.456s ---------------------- -------------------------------------------------------- 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 3.3894 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002431.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 0.008s ------------------------------------------------------------ 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: 2,408B, BPFP=25.0833 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: 12,228B, BPFP=1.6542 Using 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: 3,924B, BPFP=24.5250 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: 21,828B, BPFP=1.7718 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: 67,820B, BPFP=1.7203 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 27,792B, BPFP=0.4241 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 27,796B, BPFP=0.4241 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 25,664B, BPFP=0.7832 ⌛️ [2/4] FRONTEND: Frontend time: 0.294s (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: 0.455s [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 0.50668462 text_encoder-item0.clip_prompt_embeds 0.00022402 131.20713271 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00024964 0.63358088 text_encoder_2-item1.clip_prompt_embeds 0.00015987 0.07568091 text_encoder_3-item2.t5_prompt_embeds 0.00000778 0.00111101 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 1.19630027 4.54559326 vae.encoder_f1 1.19630098 4.54593372 vae.decoder 0.00023596 0.01884164 ------------------------------------------------------------------------------------- TOTAL 0.55486265 7.12637795 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 276524 BPFP 0.9784 bits/point EBPFP 1.9568 equivalent bits/point MSE 7.126378 ---------------------- -------------------------------------------------------- Time: 0.757s Load: 0.008s, Pack+Encode: 0.294s, Decode+Unpack: 0.455s ---------------------- -------------------------------------------------------- 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 7.1264 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000003661.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 2,344B, BPFP=24.4167 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: 13,580B, BPFP=1.8371 Using 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: 3,604B, BPFP=22.5250 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: 23,408B, BPFP=1.9000 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: 72,128B, BPFP=1.8295 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 60,936B, BPFP=0.9298 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 60,940B, BPFP=0.9299 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 47,840B, BPFP=1.4600 ⌛️ [2/4] FRONTEND: Frontend time: 0.294s (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: 0.458s [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 0.48439340 text_encoder-item0.clip_prompt_embeds 0.00030342 84.40882035 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00066702 0.49871812 text_encoder_2-item1.clip_prompt_embeds 0.00020355 0.08258602 text_encoder_3-item2.t5_prompt_embeds 0.00000815 0.00136273 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00586287 0.57886183 vae.encoder_f1 0.00587438 0.57883632 vae.decoder 0.00017677 0.02621743 ------------------------------------------------------------------------------------- TOTAL 0.00277565 4.06375212 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 371844 BPFP 1.3157 bits/point EBPFP 2.6314 equivalent bits/point MSE 4.063752 ---------------------- -------------------------------------------------------- Time: 0.760s Load: 0.008s, Pack+Encode: 0.294s, Decode+Unpack: 0.458s ---------------------- -------------------------------------------------------- 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 4.0638 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000011149.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 2,416B, BPFP=25.1667 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: 12,244B, BPFP=1.6564 Using 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: 3,816B, BPFP=23.8500 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: 22,068B, BPFP=1.7912 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: 67,352B, BPFP=1.7084 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 43,688B, BPFP=0.6666 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 43,688B, BPFP=0.6666 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 28,492B, BPFP=0.8695 ⌛️ [2/4] FRONTEND: Frontend time: 0.297s (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: 0.453s [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 0.50218093 text_encoder-item0.clip_prompt_embeds 0.00024120 191.22353558 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025189 0.52736516 text_encoder_2-item1.clip_prompt_embeds 0.00017312 0.07966633 text_encoder_3-item2.t5_prompt_embeds 0.00000806 0.00113748 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00779453 0.79382467 vae.encoder_f1 0.00779802 0.79430085 vae.decoder 0.00023829 0.01994855 ------------------------------------------------------------------------------------- TOTAL 0.00367359 6.95642537 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 310828 BPFP 1.0998 bits/point EBPFP 2.1996 equivalent bits/point MSE 6.956425 ---------------------- -------------------------------------------------------- Time: 0.757s Load: 0.008s, Pack+Encode: 0.297s, Decode+Unpack: 0.453s ---------------------- -------------------------------------------------------- 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 6.9564 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000023937.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 2,332B, BPFP=24.2917 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: 13,452B, BPFP=1.8198 Using 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: 3,792B, BPFP=23.7000 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: 22,472B, BPFP=1.8240 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: 69,572B, BPFP=1.7647 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 64,804B, BPFP=0.9888 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 64,800B, BPFP=0.9888 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 34,724B, BPFP=1.0597 ⌛️ [2/4] FRONTEND: Frontend time: 0.291s (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: 0.456s [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 0.47535769 text_encoder-item0.clip_prompt_embeds 0.00025651 23.98524587 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023478 0.53364406 text_encoder_2-item1.clip_prompt_embeds 0.00016148 0.11392257 text_encoder_3-item2.t5_prompt_embeds 0.00000844 0.00144920 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00655775 0.86760908 vae.encoder_f1 0.00656268 0.86729401 vae.decoder 0.00020283 0.02016536 ------------------------------------------------------------------------------------- TOTAL 0.00309620 2.61791780 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 363012 BPFP 1.2844 bits/point EBPFP 2.5689 equivalent bits/point MSE 2.617918 ---------------------- -------------------------------------------------------- Time: 0.756s Load: 0.009s, Pack+Encode: 0.291s, Decode+Unpack: 0.456s ---------------------- -------------------------------------------------------- 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 2.6179 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000027620.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 0.008s ------------------------------------------------------------ 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: 2,364B, BPFP=24.6250 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: 11,716B, BPFP=1.5850 Using 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: 3,836B, BPFP=23.9750 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: 20,748B, BPFP=1.6841 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: 64,328B, BPFP=1.6317 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 64,652B, BPFP=0.9865 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 64,656B, BPFP=0.9866 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 43,308B, BPFP=1.3217 ⌛️ [2/4] FRONTEND: Frontend time: 0.292s (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: 0.458s [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 0.50844034 text_encoder-item0.clip_prompt_embeds 0.00022242 167.91935538 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022710 0.50561209 text_encoder_2-item1.clip_prompt_embeds 0.00016311 0.07228813 text_encoder_3-item2.t5_prompt_embeds 0.00000924 0.00114281 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00593415 0.65065122 vae.encoder_f1 0.00594307 0.65037298 vae.decoder 0.00018992 0.02479925 ------------------------------------------------------------------------------------- TOTAL 0.00280571 6.28056417 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 362672 BPFP 1.2832 bits/point EBPFP 2.5665 equivalent bits/point MSE 6.280564 ---------------------- -------------------------------------------------------- Time: 0.758s Load: 0.008s, Pack+Encode: 0.292s, Decode+Unpack: 0.458s ---------------------- -------------------------------------------------------- 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 6.2806 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000030504.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 0.009s ------------------------------------------------------------ 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: 2,356B, BPFP=24.5417 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: 13,296B, BPFP=1.7987 Using 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: 3,656B, BPFP=22.8500 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: 22,924B, BPFP=1.8607 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: 71,984B, BPFP=1.8259 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 55,832B, BPFP=0.8519 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 55,832B, BPFP=0.8519 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 31,436B, BPFP=0.9594 ⌛️ [2/4] FRONTEND: Frontend time: 0.300s (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: 0.474s [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 0.49713059 text_encoder-item0.clip_prompt_embeds 0.00022110 96.40587798 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00042957 0.50203609 text_encoder_2-item1.clip_prompt_embeds 0.00091506 0.08834902 text_encoder_3-item2.t5_prompt_embeds 0.00000774 0.00124337 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00641770 0.75440943 vae.encoder_f1 0.00642053 0.75393313 vae.decoder 0.00017498 0.01638398 ------------------------------------------------------------------------------------- TOTAL 0.00305947 4.45794337 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 344380 BPFP 1.2185 bits/point EBPFP 2.4370 equivalent bits/point MSE 4.457943 ---------------------- -------------------------------------------------------- Time: 0.783s Load: 0.009s, Pack+Encode: 0.300s, Decode+Unpack: 0.474s ---------------------- -------------------------------------------------------- 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 4.4579 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000031248.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 0.008s ------------------------------------------------------------ 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: 2,340B, BPFP=24.3750 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: 11,776B, BPFP=1.5931 Using 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: 3,864B, BPFP=24.1500 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: 21,740B, BPFP=1.7646 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: 71,892B, BPFP=1.8236 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 49,784B, BPFP=0.7596 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 49,784B, BPFP=0.7596 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 39,744B, BPFP=1.2129 ⌛️ [2/4] FRONTEND: Frontend time: 0.296s (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: 0.470s [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 0.47578295 text_encoder-item0.clip_prompt_embeds 0.00021654 167.91788420 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022548 0.52663388 text_encoder_2-item1.clip_prompt_embeds 0.00022218 0.07825245 text_encoder_3-item2.t5_prompt_embeds 0.00000780 0.00120224 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00577698 0.56894982 vae.encoder_f1 0.00578348 0.56913251 vae.decoder 0.00017559 0.02138683 ------------------------------------------------------------------------------------- TOTAL 0.00273280 6.24261552 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 337988 BPFP 1.1959 bits/point EBPFP 2.3918 equivalent bits/point MSE 6.242616 ---------------------- -------------------------------------------------------- Time: 0.775s Load: 0.008s, Pack+Encode: 0.296s, Decode+Unpack: 0.470s ---------------------- -------------------------------------------------------- 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 6.2426 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000055072.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 0.009s ------------------------------------------------------------ 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: 2,308B, BPFP=24.0417 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: 12,720B, BPFP=1.7208 Using 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: 3,556B, BPFP=22.2250 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: 22,272B, BPFP=1.8078 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: 73,224B, BPFP=1.8573 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 56,772B, BPFP=0.8663 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 56,772B, BPFP=0.8663 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 32,256B, BPFP=0.9844 ⌛️ [2/4] FRONTEND: Frontend time: 0.304s (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: 0.464s [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 0.51773858 text_encoder-item0.clip_prompt_embeds 0.00022160 96.20558543 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00041183 0.48401198 text_encoder_2-item1.clip_prompt_embeds 0.00016827 0.07685303 text_encoder_3-item2.t5_prompt_embeds 0.00000781 0.00135345 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00668450 0.70928884 vae.encoder_f1 0.00668875 0.70954418 vae.decoder 0.00023059 0.02081185 ------------------------------------------------------------------------------------- TOTAL 0.00315742 4.43197330 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 346944 BPFP 1.2276 bits/point EBPFP 2.4552 equivalent bits/point MSE 4.431973 ---------------------- -------------------------------------------------------- Time: 0.777s Load: 0.009s, Pack+Encode: 0.304s, Decode+Unpack: 0.464s ---------------------- -------------------------------------------------------- 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 4.4320 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000060932.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 2,380B, BPFP=24.7917 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: 13,428B, BPFP=1.8166 Using 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: 3,492B, BPFP=21.8250 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: 22,960B, BPFP=1.8636 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: 73,776B, BPFP=1.8713 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 48,044B, BPFP=0.7331 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 48,036B, BPFP=0.7330 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 23,232B, BPFP=0.7090 ⌛️ [2/4] FRONTEND: Frontend time: 0.297s (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: 0.464s [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 0.47330427 text_encoder-item0.clip_prompt_embeds 0.00023190 48.67861793 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00016235 0.46719513 text_encoder_2-item1.clip_prompt_embeds 0.00020162 0.08613259 text_encoder_3-item2.t5_prompt_embeds 0.00000881 0.00132737 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.04018118 1.37007689 vae.encoder_f1 0.04018488 1.36999381 vae.decoder 0.00016201 0.01441940 ------------------------------------------------------------------------------------- TOTAL 0.01868571 3.49491969 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 322412 BPFP 1.1408 bits/point EBPFP 2.2816 equivalent bits/point MSE 3.494920 ---------------------- -------------------------------------------------------- Time: 0.770s Load: 0.009s, Pack+Encode: 0.297s, Decode+Unpack: 0.464s ---------------------- -------------------------------------------------------- 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 3.4949 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000062025.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 2,420B, BPFP=25.2083 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: 12,144B, BPFP=1.6429 Using 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: 3,724B, BPFP=23.2750 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: 21,564B, BPFP=1.7503 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: 71,924B, BPFP=1.8244 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 57,508B, BPFP=0.8775 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 57,500B, BPFP=0.8774 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 33,288B, BPFP=1.0159 ⌛️ [2/4] FRONTEND: Frontend time: 0.302s (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: 0.465s [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 0.49609264 text_encoder-item0.clip_prompt_embeds 0.00023140 84.40095712 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025605 0.46329479 text_encoder_2-item1.clip_prompt_embeds 0.00016636 0.07499158 text_encoder_3-item2.t5_prompt_embeds 0.00000797 0.00129849 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.04874706 1.19500160 vae.encoder_f1 0.04875064 1.19441509 vae.decoder 0.00019641 0.01771322 ------------------------------------------------------------------------------------- TOTAL 0.02266071 4.34782026 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 347136 BPFP 1.2283 bits/point EBPFP 2.4565 equivalent bits/point MSE 4.347820 ---------------------- -------------------------------------------------------- Time: 0.775s Load: 0.008s, Pack+Encode: 0.302s, Decode+Unpack: 0.465s ---------------------- -------------------------------------------------------- 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 4.3478 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000064718.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 0.009s ------------------------------------------------------------ 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: 2,372B, BPFP=24.7083 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: 12,600B, BPFP=1.7045 Using 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: 3,668B, BPFP=22.9250 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: 21,708B, BPFP=1.7620 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: 67,912B, BPFP=1.7226 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 68,468B, BPFP=1.0447 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 68,472B, BPFP=1.0448 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 25,252B, BPFP=0.7706 ⌛️ [2/4] FRONTEND: Frontend time: 0.297s (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: 0.463s [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 0.48138889 text_encoder-item0.clip_prompt_embeds 0.00030893 23.71936934 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00035783 0.52510552 text_encoder_2-item1.clip_prompt_embeds 0.00024047 0.08044514 text_encoder_3-item2.t5_prompt_embeds 0.00000770 0.00121198 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.01360236 1.18262327 vae.encoder_f1 0.01360807 1.18224573 vae.decoder 0.00023006 0.01823518 ------------------------------------------------------------------------------------- TOTAL 0.00637132 2.75532388 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 357516 BPFP 1.2650 bits/point EBPFP 2.5300 equivalent bits/point MSE 2.755324 ---------------------- -------------------------------------------------------- Time: 0.769s Load: 0.009s, Pack+Encode: 0.297s, Decode+Unpack: 0.463s ---------------------- -------------------------------------------------------- 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 2.7553 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000070739.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 2,472B, BPFP=25.7500 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: 14,020B, BPFP=1.8966 Using 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: 3,700B, BPFP=23.1250 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: 22,404B, BPFP=1.8185 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: 73,548B, BPFP=1.8656 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 24,328B, BPFP=0.3712 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 24,328B, BPFP=0.3712 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 13,184B, BPFP=0.4023 ⌛️ [2/4] FRONTEND: Frontend time: 0.298s (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: 0.458s [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 0.54265690 text_encoder-item0.clip_prompt_embeds 0.00024198 155.83172687 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023989 0.50633712 text_encoder_2-item1.clip_prompt_embeds 0.00015983 0.11615043 text_encoder_3-item2.t5_prompt_embeds 0.00000786 0.00119178 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 1.67190456 4.71110821 vae.encoder_f1 1.67190480 4.71055174 vae.decoder 0.00017417 0.00939602 ------------------------------------------------------------------------------------- TOTAL 0.77542609 7.84760454 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 265048 BPFP 0.9378 bits/point EBPFP 1.8756 equivalent bits/point MSE 7.847605 ---------------------- -------------------------------------------------------- Time: 0.763s Load: 0.007s, Pack+Encode: 0.298s, Decode+Unpack: 0.458s ---------------------- -------------------------------------------------------- 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 7.8476 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000074646.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 0.009s ------------------------------------------------------------ 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: 2,364B, BPFP=24.6250 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: 12,968B, BPFP=1.7543 Using 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: 3,548B, BPFP=22.1750 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: 21,980B, BPFP=1.7841 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: 74,220B, BPFP=1.8826 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 69,020B, BPFP=1.0532 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 69,024B, BPFP=1.0532 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 37,944B, BPFP=1.1580 ⌛️ [2/4] FRONTEND: Frontend time: 0.290s (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: 0.463s [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 0.48747981 text_encoder-item0.clip_prompt_embeds 0.00025129 23.55641234 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023862 0.51996045 text_encoder_2-item1.clip_prompt_embeds 0.00021627 0.08000228 text_encoder_3-item2.t5_prompt_embeds 0.00000880 0.00160508 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00621760 0.76899570 vae.encoder_f1 0.00622505 0.76852208 vae.decoder 0.00025114 0.02307880 ------------------------------------------------------------------------------------- TOTAL 0.00294689 2.55980847 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 378132 BPFP 1.3379 bits/point EBPFP 2.6759 equivalent bits/point MSE 2.559808 ---------------------- -------------------------------------------------------- Time: 0.763s Load: 0.009s, Pack+Encode: 0.290s, Decode+Unpack: 0.463s ---------------------- -------------------------------------------------------- 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 2.5598 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000085157.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 0.010s ------------------------------------------------------------ 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: 2,236B, BPFP=23.2917 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: 12,372B, BPFP=1.6737 Using 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: 3,684B, BPFP=23.0250 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: 21,736B, BPFP=1.7643 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: 67,492B, BPFP=1.7120 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 72,372B, BPFP=1.1043 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 72,372B, BPFP=1.1043 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 38,556B, BPFP=1.1766 ⌛️ [2/4] FRONTEND: Frontend time: 0.290s (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: 0.456s [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 0.51888049 text_encoder-item0.clip_prompt_embeds 0.00020838 23.65058932 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021520 0.52990861 text_encoder_2-item1.clip_prompt_embeds 0.00018543 0.07540971 text_encoder_3-item2.t5_prompt_embeds 0.00000844 0.00111033 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00675961 1.00314224 vae.encoder_f1 0.00676652 1.00374055 vae.decoder 0.00021373 0.02320858 ------------------------------------------------------------------------------------- TOTAL 0.00319201 2.67087205 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 377884 BPFP 1.3371 bits/point EBPFP 2.6741 equivalent bits/point MSE 2.670872 ---------------------- -------------------------------------------------------- Time: 0.756s Load: 0.010s, Pack+Encode: 0.290s, Decode+Unpack: 0.456s ---------------------- -------------------------------------------------------- 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 2.6709 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000089648.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 2,340B, BPFP=24.3750 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: 12,712B, BPFP=1.7197 Using 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: 3,688B, BPFP=23.0500 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: 22,380B, BPFP=1.8166 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: 75,816B, BPFP=1.9231 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 43,064B, BPFP=0.6571 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 43,060B, BPFP=0.6570 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 53,836B, BPFP=1.6429 ⌛️ [2/4] FRONTEND: Frontend time: 0.292s (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: 0.456s [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 0.50930520 text_encoder-item0.clip_prompt_embeds 0.00021387 35.82431598 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00028145 0.50193815 text_encoder_2-item1.clip_prompt_embeds 0.00018115 0.08109919 text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00135092 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00596338 0.47066346 vae.encoder_f1 0.00596322 0.47061217 vae.decoder 0.00018207 0.02888557 ------------------------------------------------------------------------------------- TOTAL 0.00281657 2.74309793 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 343960 BPFP 1.2170 bits/point EBPFP 2.4340 equivalent bits/point MSE 2.743098 ---------------------- -------------------------------------------------------- Time: 0.756s Load: 0.008s, Pack+Encode: 0.292s, Decode+Unpack: 0.456s ---------------------- -------------------------------------------------------- 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 2.7431 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000093965.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 2,356B, BPFP=24.5417 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: 12,584B, BPFP=1.7024 Using 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: 3,704B, BPFP=23.1500 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: 22,100B, BPFP=1.7938 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: 68,452B, BPFP=1.7363 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 32,736B, BPFP=0.4995 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 32,736B, BPFP=0.4995 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 44,412B, BPFP=1.3553 ⌛️ [2/4] FRONTEND: Frontend time: 0.294s (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: 0.456s [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 0.47191763 text_encoder-item0.clip_prompt_embeds 0.00022138 47.04744572 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00034234 0.50264111 text_encoder_2-item1.clip_prompt_embeds 0.00019942 0.08334400 text_encoder_3-item2.t5_prompt_embeds 0.00000807 0.00117561 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00552804 0.37992704 vae.encoder_f1 0.00552758 0.37994546 vae.decoder 0.00018040 0.02411232 ------------------------------------------------------------------------------------- TOTAL 0.00261550 2.99408085 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 306144 BPFP 1.0832 bits/point EBPFP 2.1664 equivalent bits/point MSE 2.994081 ---------------------- -------------------------------------------------------- Time: 0.758s Load: 0.008s, Pack+Encode: 0.294s, Decode+Unpack: 0.456s ---------------------- -------------------------------------------------------- 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 2.9941 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000094852.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 0.008s ------------------------------------------------------------ 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: 2,440B, BPFP=25.4167 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: 12,320B, BPFP=1.6667 Using 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: 3,756B, BPFP=23.4750 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: 21,656B, BPFP=1.7578 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: 67,668B, BPFP=1.7164 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 38,488B, BPFP=0.5873 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 38,484B, BPFP=0.5872 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 27,212B, BPFP=0.8304 ⌛️ [2/4] FRONTEND: Frontend time: 0.292s (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: 0.458s [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 0.49007730 text_encoder-item0.clip_prompt_embeds 0.00024507 23.73426508 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020802 0.49623203 text_encoder_2-item1.clip_prompt_embeds 0.00034897 0.08659878 text_encoder_3-item2.t5_prompt_embeds 0.00000820 0.00126268 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00721525 0.64476132 vae.encoder_f1 0.00721777 0.64436603 vae.decoder 0.00018707 0.01661482 ------------------------------------------------------------------------------------- TOTAL 0.00340651 2.50634020 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 299088 BPFP 1.0583 bits/point EBPFP 2.1165 equivalent bits/point MSE 2.506340 ---------------------- -------------------------------------------------------- Time: 0.758s Load: 0.008s, Pack+Encode: 0.292s, Decode+Unpack: 0.458s ---------------------- -------------------------------------------------------- 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 2.5063 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000117914.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 2,424B, BPFP=25.2500 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: 11,740B, BPFP=1.5882 Using 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: 3,928B, BPFP=24.5500 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: 20,744B, BPFP=1.6838 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: 62,544B, BPFP=1.5864 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 54,692B, BPFP=0.8345 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 54,696B, BPFP=0.8346 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 33,708B, BPFP=1.0287 ⌛️ [2/4] FRONTEND: Frontend time: 0.291s (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: 0.459s [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 0.50160547 text_encoder-item0.clip_prompt_embeds 0.00046272 191.51636905 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022428 0.51882677 text_encoder_2-item1.clip_prompt_embeds 0.00014574 0.07738749 text_encoder_3-item2.t5_prompt_embeds 0.00000853 0.00106671 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.01999603 1.20153999 vae.encoder_f1 0.01999529 1.20093882 vae.decoder 0.00024882 0.02237918 ------------------------------------------------------------------------------------- TOTAL 0.00933711 7.15308751 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 331540 BPFP 1.1731 bits/point EBPFP 2.3462 equivalent bits/point MSE 7.153088 ---------------------- -------------------------------------------------------- Time: 0.757s Load: 0.008s, Pack+Encode: 0.291s, Decode+Unpack: 0.459s ---------------------- -------------------------------------------------------- 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 7.1531 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000123321.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 2,392B, BPFP=24.9167 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: 12,032B, BPFP=1.6277 Using 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: 3,720B, BPFP=23.2500 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: 21,256B, BPFP=1.7253 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: 64,552B, BPFP=1.6374 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 57,160B, BPFP=0.8722 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 57,160B, BPFP=0.8722 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 22,224B, BPFP=0.6782 ⌛️ [2/4] FRONTEND: Frontend time: 0.292s (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: 0.459s [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 0.51519823 text_encoder-item0.clip_prompt_embeds 0.00020334 23.75412608 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00017433 0.47212248 text_encoder_2-item1.clip_prompt_embeds 0.00020202 0.07956481 text_encoder_3-item2.t5_prompt_embeds 0.00000787 0.00110097 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.01341345 1.09189987 vae.encoder_f1 0.01341645 1.09189379 vae.decoder 0.00018350 0.01396219 ------------------------------------------------------------------------------------- TOTAL 0.00627332 2.71367667 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 327560 BPFP 1.1590 bits/point EBPFP 2.3180 equivalent bits/point MSE 2.713677 ---------------------- -------------------------------------------------------- Time: 0.759s Load: 0.009s, Pack+Encode: 0.292s, Decode+Unpack: 0.459s ---------------------- -------------------------------------------------------- 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 2.7137 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127182.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 0.010s ------------------------------------------------------------ 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: 2,308B, BPFP=24.0417 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: 12,184B, BPFP=1.6483 Using 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: 3,716B, BPFP=23.2250 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: 21,440B, BPFP=1.7403 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: 72,436B, BPFP=1.8374 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 62,848B, BPFP=0.9590 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 62,856B, BPFP=0.9591 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 44,540B, BPFP=1.3593 ⌛️ [2/4] FRONTEND: Frontend time: 0.292s (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: 0.455s [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 0.53660615 text_encoder-item0.clip_prompt_embeds 0.00022316 96.48066322 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00045791 0.51479607 text_encoder_2-item1.clip_prompt_embeds 0.00022852 0.07850448 text_encoder_3-item2.t5_prompt_embeds 0.00000822 0.00142578 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00606298 0.65119195 vae.encoder_f1 0.00607096 0.65096605 vae.decoder 0.00023408 0.02561883 ------------------------------------------------------------------------------------- TOTAL 0.00287331 4.41277610 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 369392 BPFP 1.3070 bits/point EBPFP 2.6140 equivalent bits/point MSE 4.412776 ---------------------- -------------------------------------------------------- Time: 0.757s Load: 0.010s, Pack+Encode: 0.292s, Decode+Unpack: 0.455s ---------------------- -------------------------------------------------------- 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 4.4128 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127394.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 2,328B, BPFP=24.2500 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: 11,988B, BPFP=1.6218 Using 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: 3,776B, BPFP=23.6000 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: 21,372B, BPFP=1.7347 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: 70,516B, BPFP=1.7887 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 58,076B, BPFP=0.8862 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 58,072B, BPFP=0.8861 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 40,436B, BPFP=1.2340 ⌛️ [2/4] FRONTEND: Frontend time: 0.293s (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: 0.457s [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 0.46075769 text_encoder-item0.clip_prompt_embeds 0.00023597 47.82645935 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00026316 0.50234394 text_encoder_2-item1.clip_prompt_embeds 0.00018757 0.07672961 text_encoder_3-item2.t5_prompt_embeds 0.00000828 0.00129005 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00653100 0.70934260 vae.encoder_f1 0.00653745 0.70936871 vae.decoder 0.00020026 0.02215014 ------------------------------------------------------------------------------------- TOTAL 0.00308450 3.16672626 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 353628 BPFP 1.2512 bits/point EBPFP 2.5025 equivalent bits/point MSE 3.166726 ---------------------- -------------------------------------------------------- Time: 0.758s Load: 0.008s, Pack+Encode: 0.293s, Decode+Unpack: 0.457s ---------------------- -------------------------------------------------------- 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 3.1667 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000133969.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 0.009s ------------------------------------------------------------ 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: 2,288B, BPFP=23.8333 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: 12,836B, BPFP=1.7365 Using 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: 3,804B, BPFP=23.7750 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: 22,092B, BPFP=1.7932 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: 68,232B, BPFP=1.7307 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 58,400B, BPFP=0.8911 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 58,396B, BPFP=0.8911 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 28,344B, BPFP=0.8650 ⌛️ [2/4] FRONTEND: Frontend time: 0.291s (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: 0.455s [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 0.50802243 text_encoder-item0.clip_prompt_embeds 0.00022433 120.48906757 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00107168 0.52761889 text_encoder_2-item1.clip_prompt_embeds 0.00016492 0.07224874 text_encoder_3-item2.t5_prompt_embeds 0.00000806 0.00123930 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00869686 1.19688261 vae.encoder_f1 0.00870063 1.19614518 vae.decoder 0.00021246 0.01918346 ------------------------------------------------------------------------------------- TOTAL 0.00408877 5.29262133 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 341456 BPFP 1.2082 bits/point EBPFP 2.4163 equivalent bits/point MSE 5.292621 ---------------------- -------------------------------------------------------- Time: 0.755s Load: 0.009s, Pack+Encode: 0.291s, Decode+Unpack: 0.455s ---------------------- -------------------------------------------------------- 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 5.2926 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000140270.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 0.009s ------------------------------------------------------------ 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: 2,324B, BPFP=24.2083 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: 13,076B, BPFP=1.7689 Using 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: 3,732B, BPFP=23.3250 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: 23,000B, BPFP=1.8669 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: 69,536B, BPFP=1.7638 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 67,232B, BPFP=1.0259 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 67,236B, BPFP=1.0259 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 33,516B, BPFP=1.0228 ⌛️ [2/4] FRONTEND: Frontend time: 0.294s (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: 0.456s [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 0.52541637 text_encoder-item0.clip_prompt_embeds 0.00022433 34.64081735 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020112 0.45581689 text_encoder_2-item1.clip_prompt_embeds 0.00017331 0.07613430 text_encoder_3-item2.t5_prompt_embeds 0.00000752 0.00119501 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00626512 0.89477164 vae.encoder_f1 0.00626949 0.89477986 vae.decoder 0.00018936 0.01889040 ------------------------------------------------------------------------------------- TOTAL 0.00295827 2.90742762 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 366716 BPFP 1.2975 bits/point EBPFP 2.5951 equivalent bits/point MSE 2.907428 ---------------------- -------------------------------------------------------- Time: 0.759s Load: 0.009s, Pack+Encode: 0.294s, Decode+Unpack: 0.456s ---------------------- -------------------------------------------------------- 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 2.9074 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000146358.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 2,304B, BPFP=24.0000 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: 13,560B, BPFP=1.8344 Using 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: 3,532B, BPFP=22.0750 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: 23,164B, BPFP=1.8802 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: 72,720B, BPFP=1.8446 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 48,756B, BPFP=0.7440 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 48,756B, BPFP=0.7440 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 24,052B, BPFP=0.7340 ⌛️ [2/4] FRONTEND: Frontend time: 0.294s (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: 0.460s [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 0.56566926 text_encoder-item0.clip_prompt_embeds 0.00026137 120.52751285 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00138553 0.49686694 text_encoder_2-item1.clip_prompt_embeds 0.00019680 0.08122905 text_encoder_3-item2.t5_prompt_embeds 0.00000808 0.00130268 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.35915655 2.30982113 vae.encoder_f1 0.35915723 2.30903935 vae.decoder 0.00024181 0.01732877 ------------------------------------------------------------------------------------- TOTAL 0.16663024 5.80994942 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 323908 BPFP 1.1461 bits/point EBPFP 2.2921 equivalent bits/point MSE 5.809949 ---------------------- -------------------------------------------------------- Time: 0.763s Load: 0.008s, Pack+Encode: 0.294s, Decode+Unpack: 0.460s ---------------------- -------------------------------------------------------- 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 5.8099 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000148662.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 0.008s ------------------------------------------------------------ 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: 2,344B, BPFP=24.4167 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: 11,288B, BPFP=1.5271 Using 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: 3,772B, BPFP=23.5750 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: 20,636B, BPFP=1.6750 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: 65,492B, BPFP=1.6612 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 22,832B, BPFP=0.3484 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 22,832B, BPFP=0.3484 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 30,712B, BPFP=0.9373 ⌛️ [2/4] FRONTEND: Frontend time: 0.292s (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: 0.456s [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 0.52745442 text_encoder-item0.clip_prompt_embeds 0.00021656 155.64517384 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00019988 0.51618619 text_encoder_2-item1.clip_prompt_embeds 0.00016555 0.06933978 text_encoder_3-item2.t5_prompt_embeds 0.00000783 0.00117382 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.29031765 1.97527528 vae.encoder_f1 0.29031771 1.97528768 vae.decoder 0.00019965 0.02115830 ------------------------------------------------------------------------------------- TOTAL 0.13469251 6.57338620 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 266972 BPFP 0.9446 bits/point EBPFP 1.8892 equivalent bits/point MSE 6.573386 ---------------------- -------------------------------------------------------- Time: 0.755s Load: 0.008s, Pack+Encode: 0.292s, Decode+Unpack: 0.456s ---------------------- -------------------------------------------------------- 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 6.5734 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000151051.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 0.009s ------------------------------------------------------------ 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: 2,316B, BPFP=24.1250 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: 11,388B, BPFP=1.5406 Using 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: 3,712B, BPFP=23.2000 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: 21,132B, BPFP=1.7153 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: 62,304B, BPFP=1.5804 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 50,508B, BPFP=0.7707 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 50,512B, BPFP=0.7708 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 48,028B, BPFP=1.4657 ⌛️ [2/4] FRONTEND: Frontend time: 0.289s (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: 0.455s [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 0.53749744 text_encoder-item0.clip_prompt_embeds 0.00025451 83.97307055 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023552 0.48756971 text_encoder_2-item1.clip_prompt_embeds 0.00017758 0.07394200 text_encoder_3-item2.t5_prompt_embeds 0.00000816 0.00123060 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00595764 0.49202594 vae.encoder_f1 0.00596395 0.49205506 vae.decoder 0.00019845 0.02712763 ------------------------------------------------------------------------------------- TOTAL 0.00281886 4.01181810 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 336964 BPFP 1.1923 bits/point EBPFP 2.3845 equivalent bits/point MSE 4.011818 ---------------------- -------------------------------------------------------- Time: 0.753s Load: 0.009s, Pack+Encode: 0.289s, Decode+Unpack: 0.455s ---------------------- -------------------------------------------------------- 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 4.0118 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000155443.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 0.008s ------------------------------------------------------------ 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: 2,244B, BPFP=23.3750 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: 13,280B, BPFP=1.7965 Using 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: 3,660B, BPFP=22.8750 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: 23,168B, BPFP=1.8805 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: 72,656B, BPFP=1.8429 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 37,900B, BPFP=0.5783 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 37,896B, BPFP=0.5782 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 24,172B, BPFP=0.7377 ⌛️ [2/4] FRONTEND: Frontend time: 0.291s (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: 0.454s [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 0.50406118 text_encoder-item0.clip_prompt_embeds 0.00026157 144.25101461 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022221 0.51753731 text_encoder_2-item1.clip_prompt_embeds 0.00022582 0.08685431 text_encoder_3-item2.t5_prompt_embeds 0.00000776 0.00135130 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.40456498 3.15106440 vae.encoder_f1 0.40456539 3.14994621 vae.decoder 0.00020503 0.01713419 ------------------------------------------------------------------------------------- TOTAL 0.18768128 6.82071904 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 302040 BPFP 1.0687 bits/point EBPFP 2.1374 equivalent bits/point MSE 6.820719 ---------------------- -------------------------------------------------------- Time: 0.753s Load: 0.008s, Pack+Encode: 0.291s, Decode+Unpack: 0.454s ---------------------- -------------------------------------------------------- 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 6.8207 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000159458.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 0.009s ------------------------------------------------------------ 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: 2,336B, BPFP=24.3333 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: 12,888B, BPFP=1.7435 Using 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: 3,856B, BPFP=24.1000 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: 21,140B, BPFP=1.7159 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: 65,324B, BPFP=1.6570 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 58,632B, BPFP=0.8947 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 58,632B, BPFP=0.8947 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 43,260B, BPFP=1.3202 ⌛️ [2/4] FRONTEND: Frontend time: 0.294s (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: 0.456s [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 0.48591455 text_encoder-item0.clip_prompt_embeds 0.00027179 71.98405371 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025795 0.54521971 text_encoder_2-item1.clip_prompt_embeds 0.00015124 0.07653546 text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00103451 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00673531 1.04844451 vae.encoder_f1 0.00673732 1.04887092 vae.decoder 0.00020129 0.02396178 ------------------------------------------------------------------------------------- TOTAL 0.00317768 3.95612174 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 353132 BPFP 1.2495 bits/point EBPFP 2.4990 equivalent bits/point MSE 3.956122 ---------------------- -------------------------------------------------------- Time: 0.758s Load: 0.009s, Pack+Encode: 0.294s, Decode+Unpack: 0.456s ---------------------- -------------------------------------------------------- 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 3.9561 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000161128.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 0.008s ------------------------------------------------------------ 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: 2,360B, BPFP=24.5833 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: 13,056B, BPFP=1.7662 Using 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: 3,740B, BPFP=23.3750 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: 22,068B, BPFP=1.7912 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: 73,144B, BPFP=1.8553 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 48,764B, BPFP=0.7441 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 48,768B, BPFP=0.7441 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 22,248B, BPFP=0.6790 ⌛️ [2/4] FRONTEND: Frontend time: 0.294s (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: 0.454s [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 0.48081660 text_encoder-item0.clip_prompt_embeds 0.00023057 83.33961546 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023879 0.53325825 text_encoder_2-item1.clip_prompt_embeds 0.00123217 0.08637823 text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00127551 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00881784 1.07970083 vae.encoder_f1 0.00882136 1.07958722 vae.decoder 0.00017598 0.01474020 ------------------------------------------------------------------------------------- TOTAL 0.00418676 4.26688069 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 321212 BPFP 1.1365 bits/point EBPFP 2.2731 equivalent bits/point MSE 4.266881 ---------------------- -------------------------------------------------------- Time: 0.757s Load: 0.008s, Pack+Encode: 0.294s, Decode+Unpack: 0.454s ---------------------- -------------------------------------------------------- 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 4.2669 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000168458.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 2,388B, BPFP=24.8750 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: 12,732B, BPFP=1.7224 Using 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: 3,596B, BPFP=22.4750 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: 21,732B, BPFP=1.7640 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: 70,472B, BPFP=1.7875 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 54,088B, BPFP=0.8253 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 54,092B, BPFP=0.8254 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 47,160B, BPFP=1.4392 ⌛️ [2/4] FRONTEND: Frontend time: 0.292s (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: 0.465s [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 0.45848346 text_encoder-item0.clip_prompt_embeds 0.00025208 23.95837984 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00047028 0.49840498 text_encoder_2-item1.clip_prompt_embeds 0.00113921 0.08663124 text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00126138 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00582247 0.52246672 vae.encoder_f1 0.00582996 0.52246433 vae.decoder 0.00016099 0.02362412 ------------------------------------------------------------------------------------- TOTAL 0.00279351 2.45638107 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 353324 BPFP 1.2502 bits/point EBPFP 2.5003 equivalent bits/point MSE 2.456381 ---------------------- -------------------------------------------------------- Time: 0.766s Load: 0.008s, Pack+Encode: 0.292s, Decode+Unpack: 0.465s ---------------------- -------------------------------------------------------- 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 2.4564 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000171788.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 0.009s ------------------------------------------------------------ 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: 2,420B, BPFP=25.2083 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: 12,392B, BPFP=1.6764 Using 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: 3,704B, BPFP=23.1500 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: 22,292B, BPFP=1.8094 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: 68,252B, BPFP=1.7312 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 66,020B, BPFP=1.0074 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 66,016B, BPFP=1.0073 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 36,560B, BPFP=1.1157 ⌛️ [2/4] FRONTEND: Frontend time: 0.299s (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: 0.466s [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 0.49308046 text_encoder-item0.clip_prompt_embeds 0.00020809 83.88460498 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00035925 0.48315067 text_encoder_2-item1.clip_prompt_embeds 0.00112984 0.09029607 text_encoder_3-item2.t5_prompt_embeds 0.00000832 0.00111407 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00602745 0.89384651 vae.encoder_f1 0.00603159 0.89379698 vae.decoder 0.00017526 0.02093792 ------------------------------------------------------------------------------------- TOTAL 0.00288782 4.19579903 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 364720 BPFP 1.2905 bits/point EBPFP 2.5810 equivalent bits/point MSE 4.195799 ---------------------- -------------------------------------------------------- Time: 0.774s Load: 0.009s, Pack+Encode: 0.299s, Decode+Unpack: 0.466s ---------------------- -------------------------------------------------------- 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 4.1958 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000179265.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 0.009s ------------------------------------------------------------ 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: 2,376B, BPFP=24.7500 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: 12,000B, BPFP=1.6234 Using 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: 3,648B, BPFP=22.8000 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: 21,316B, BPFP=1.7302 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: 66,076B, BPFP=1.6760 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 67,436B, BPFP=1.0290 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 67,436B, BPFP=1.0290 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 40,012B, BPFP=1.2211 ⌛️ [2/4] FRONTEND: Frontend time: 0.301s (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: 0.467s [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 0.52492702 text_encoder-item0.clip_prompt_embeds 0.00020908 23.71116790 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00048701 0.51482992 text_encoder_2-item1.clip_prompt_embeds 0.00016227 0.07916478 text_encoder_3-item2.t5_prompt_embeds 0.00000845 0.00124098 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00634616 0.86809397 vae.encoder_f1 0.00635208 0.86802471 vae.decoder 0.00022721 0.02233287 ------------------------------------------------------------------------------------- TOTAL 0.00300000 2.60974450 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 367364 BPFP 1.2998 bits/point EBPFP 2.5997 equivalent bits/point MSE 2.609744 ---------------------- -------------------------------------------------------- Time: 0.777s Load: 0.009s, Pack+Encode: 0.301s, Decode+Unpack: 0.467s ---------------------- -------------------------------------------------------- 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 2.6097 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000189752.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 0.008s ------------------------------------------------------------ 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: 2,348B, BPFP=24.4583 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: 12,160B, BPFP=1.6450 Using 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: 3,788B, BPFP=23.6750 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: 20,920B, BPFP=1.6981 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: 65,004B, BPFP=1.6488 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 33,236B, BPFP=0.5071 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 33,236B, BPFP=0.5071 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 18,928B, BPFP=0.5776 ⌛️ [2/4] FRONTEND: Frontend time: 0.298s (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: 0.466s [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 0.49475741 text_encoder-item0.clip_prompt_embeds 0.00022947 132.49727746 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00031292 0.55463305 text_encoder_2-item1.clip_prompt_embeds 0.00017460 0.07349422 text_encoder_3-item2.t5_prompt_embeds 0.00000789 0.00110960 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.05448642 1.12797141 vae.encoder_f1 0.05448771 1.12816596 vae.decoder 0.00017748 0.01409720 ------------------------------------------------------------------------------------- TOTAL 0.02531999 5.57440932 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 276684 BPFP 0.9790 bits/point EBPFP 1.9580 equivalent bits/point MSE 5.574409 ---------------------- -------------------------------------------------------- Time: 0.772s Load: 0.008s, Pack+Encode: 0.298s, Decode+Unpack: 0.466s ---------------------- -------------------------------------------------------- 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 5.5744 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222118.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 0.009s ------------------------------------------------------------ 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: 2,356B, BPFP=24.5417 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: 12,436B, BPFP=1.6824 Using 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: 3,796B, BPFP=23.7250 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: 22,356B, BPFP=1.8146 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: 69,736B, BPFP=1.7689 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 46,060B, BPFP=0.7028 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 46,056B, BPFP=0.7028 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 18,840B, BPFP=0.5750 ⌛️ [2/4] FRONTEND: Frontend time: 0.301s (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: 0.467s [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 0.50963799 text_encoder-item0.clip_prompt_embeds 0.00020169 36.63345086 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00017591 0.49709654 text_encoder_2-item1.clip_prompt_embeds 0.00015739 0.07620022 text_encoder_3-item2.t5_prompt_embeds 0.00000811 0.00137747 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.06876971 1.70475209 vae.encoder_f1 0.06877109 1.70504105 vae.decoder 0.00023999 0.01337044 ------------------------------------------------------------------------------------- TOTAL 0.03194988 3.33465928 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 308700 BPFP 1.0923 bits/point EBPFP 2.1845 equivalent bits/point MSE 3.334659 ---------------------- -------------------------------------------------------- Time: 0.776s Load: 0.009s, Pack+Encode: 0.301s, Decode+Unpack: 0.467s ---------------------- -------------------------------------------------------- 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 3.3347 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222825.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 0.009s ------------------------------------------------------------ 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: 2,380B, BPFP=24.7917 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: 12,620B, BPFP=1.7073 Using 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: 3,720B, BPFP=23.2500 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: 21,212B, BPFP=1.7218 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: 68,256B, BPFP=1.7313 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 50,992B, BPFP=0.7781 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 50,992B, BPFP=0.7781 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 47,532B, BPFP=1.4506 ⌛️ [2/4] FRONTEND: Frontend time: 0.298s (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: 0.462s [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 0.47360365 text_encoder-item0.clip_prompt_embeds 0.00025253 23.73753297 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00041073 0.54306488 text_encoder_2-item1.clip_prompt_embeds 0.00018825 0.11932257 text_encoder_3-item2.t5_prompt_embeds 0.00000859 0.00119023 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00595097 0.49281138 vae.encoder_f1 0.00595882 0.49284381 vae.decoder 0.00020134 0.02782257 ------------------------------------------------------------------------------------- TOTAL 0.00281645 2.43879205 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 344768 BPFP 1.2199 bits/point EBPFP 2.4398 equivalent bits/point MSE 2.438792 ---------------------- -------------------------------------------------------- Time: 0.769s Load: 0.009s, Pack+Encode: 0.298s, Decode+Unpack: 0.462s ---------------------- -------------------------------------------------------- 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 2.4388 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000227478.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 2,428B, BPFP=25.2917 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: 12,652B, BPFP=1.7116 Using 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: 3,844B, BPFP=24.0250 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: 22,192B, BPFP=1.8013 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: 68,540B, BPFP=1.7385 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 40,448B, BPFP=0.6172 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 40,448B, BPFP=0.6172 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 33,216B, BPFP=1.0137 ⌛️ [2/4] FRONTEND: Frontend time: 0.300s (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: 0.464s [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 0.50924591 text_encoder-item0.clip_prompt_embeds 0.00022201 59.93744927 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00030500 0.52193484 text_encoder_2-item1.clip_prompt_embeds 0.00020541 0.12586998 text_encoder_3-item2.t5_prompt_embeds 0.00000847 0.00134491 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00831743 0.84119773 vae.encoder_f1 0.00831926 0.84146702 vae.decoder 0.00028593 0.02029091 ------------------------------------------------------------------------------------- TOTAL 0.00392223 3.54665623 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 310832 BPFP 1.0998 bits/point EBPFP 2.1996 equivalent bits/point MSE 3.546656 ---------------------- -------------------------------------------------------- Time: 0.773s Load: 0.008s, Pack+Encode: 0.300s, Decode+Unpack: 0.464s ---------------------- -------------------------------------------------------- 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 3.5467 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000239843.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 0.009s ------------------------------------------------------------ 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: 2,288B, BPFP=23.8333 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: 13,084B, BPFP=1.7700 Using 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: 3,732B, BPFP=23.3250 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: 22,280B, BPFP=1.8084 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: 72,876B, BPFP=1.8485 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 66,868B, BPFP=1.0203 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 66,868B, BPFP=1.0203 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 35,428B, BPFP=1.0812 ⌛️ [2/4] FRONTEND: Frontend time: 0.298s (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: 0.463s [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 0.54362551 text_encoder-item0.clip_prompt_embeds 0.00026808 35.81664299 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00033998 0.53691812 text_encoder_2-item1.clip_prompt_embeds 0.00021475 0.08287752 text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00129219 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00606586 0.90239537 vae.encoder_f1 0.00607066 0.90251613 vae.decoder 0.00019664 0.02154293 ------------------------------------------------------------------------------------- TOTAL 0.00286987 2.94241010 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 370488 BPFP 1.3109 bits/point EBPFP 2.6218 equivalent bits/point MSE 2.942410 ---------------------- -------------------------------------------------------- Time: 0.770s Load: 0.009s, Pack+Encode: 0.298s, Decode+Unpack: 0.463s ---------------------- -------------------------------------------------------- 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 2.9424 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000240250.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 0.007s ------------------------------------------------------------ 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: 2,252B, BPFP=23.4583 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: 12,912B, BPFP=1.7468 Using 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: 3,780B, BPFP=23.6250 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: 23,560B, BPFP=1.9123 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: 73,376B, BPFP=1.8612 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 49,744B, BPFP=0.7590 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 49,740B, BPFP=0.7590 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 34,344B, BPFP=1.0481 ⌛️ [2/4] FRONTEND: Frontend time: 0.304s (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: 0.468s [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 0.52347287 text_encoder-item0.clip_prompt_embeds 0.00023198 48.14593479 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00035192 0.54044819 text_encoder_2-item1.clip_prompt_embeds 0.00017676 0.08290307 text_encoder_3-item2.t5_prompt_embeds 0.00000830 0.00117175 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.05216765 1.61536670 vae.encoder_f1 0.05216896 1.61537707 vae.decoder 0.00017960 0.01899431 ------------------------------------------------------------------------------------- TOTAL 0.02424513 3.59519313 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 336772 BPFP 1.1916 bits/point EBPFP 2.3832 equivalent bits/point MSE 3.595193 ---------------------- -------------------------------------------------------- Time: 0.780s Load: 0.007s, Pack+Encode: 0.304s, Decode+Unpack: 0.468s ---------------------- -------------------------------------------------------- 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 3.5952 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000258793.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 0.009s ------------------------------------------------------------ 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: 2,312B, BPFP=24.0833 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: 12,824B, BPFP=1.7348 Using 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: 3,792B, BPFP=23.7000 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: 21,324B, BPFP=1.7308 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: 66,916B, BPFP=1.6973 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 67,904B, BPFP=1.0361 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 67,904B, BPFP=1.0361 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 37,464B, BPFP=1.1433 ⌛️ [2/4] FRONTEND: Frontend time: 0.303s (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: 0.463s [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 0.50486636 text_encoder-item0.clip_prompt_embeds 0.00023125 190.75740666 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00024484 0.56392651 text_encoder_2-item1.clip_prompt_embeds 0.00020589 0.08125670 text_encoder_3-item2.t5_prompt_embeds 0.00000799 0.00123059 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00620361 0.78358567 vae.encoder_f1 0.00620966 0.78373992 vae.decoder 0.00020748 0.02170954 ------------------------------------------------------------------------------------- TOTAL 0.00293402 6.93971874 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 367504 BPFP 1.3003 bits/point EBPFP 2.6007 equivalent bits/point MSE 6.939719 ---------------------- -------------------------------------------------------- Time: 0.776s Load: 0.009s, Pack+Encode: 0.303s, Decode+Unpack: 0.463s ---------------------- -------------------------------------------------------- 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 6.9397 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000270402.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 0.009s ------------------------------------------------------------ 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: 2,440B, BPFP=25.4167 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: 12,912B, BPFP=1.7468 Using 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: 3,656B, BPFP=22.8500 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: 22,052B, BPFP=1.7899 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: 69,620B, BPFP=1.7659 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 57,636B, BPFP=0.8795 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 57,632B, BPFP=0.8794 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 37,536B, BPFP=1.1455 ⌛️ [2/4] FRONTEND: Frontend time: 0.300s (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: 0.462s [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 0.45993360 text_encoder-item0.clip_prompt_embeds 0.00023066 36.41133996 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00044687 0.48206329 text_encoder_2-item1.clip_prompt_embeds 0.00018171 0.08382292 text_encoder_3-item2.t5_prompt_embeds 0.00000793 0.00131729 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.03159856 1.00012004 vae.encoder_f1 0.03160188 1.00028515 vae.decoder 0.00018417 0.02025001 ------------------------------------------------------------------------------------- TOTAL 0.01470700 3.00313153 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 350548 BPFP 1.2403 bits/point EBPFP 2.4807 equivalent bits/point MSE 3.003132 ---------------------- -------------------------------------------------------- Time: 0.771s Load: 0.009s, Pack+Encode: 0.300s, Decode+Unpack: 0.462s ---------------------- -------------------------------------------------------- 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 3.0031 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000274272.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 0.009s ------------------------------------------------------------ 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: 2,352B, BPFP=24.5000 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: 12,912B, BPFP=1.7468 Using 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: 3,692B, BPFP=23.0750 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: 22,404B, BPFP=1.8185 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: 71,940B, BPFP=1.8248 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 70,420B, BPFP=1.0745 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 70,416B, BPFP=1.0745 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 35,856B, BPFP=1.0942 ⌛️ [2/4] FRONTEND: Frontend time: 0.299s (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: 0.465s [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 0.47190841 text_encoder-item0.clip_prompt_embeds 0.00024948 47.83052202 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00032352 0.44361448 text_encoder_2-item1.clip_prompt_embeds 0.00019749 0.07657974 text_encoder_3-item2.t5_prompt_embeds 0.00000781 0.00134123 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.03490865 1.76708245 vae.encoder_f1 0.03491008 1.76675832 vae.decoder 0.00028462 0.02449891 ------------------------------------------------------------------------------------- TOTAL 0.01625440 3.65754079 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 377056 BPFP 1.3341 bits/point EBPFP 2.6683 equivalent bits/point MSE 3.657541 ---------------------- -------------------------------------------------------- Time: 0.773s Load: 0.009s, Pack+Encode: 0.299s, Decode+Unpack: 0.465s ---------------------- -------------------------------------------------------- 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 3.6575 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000280891.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 0.009s ------------------------------------------------------------ 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: 2,324B, BPFP=24.2083 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: 11,988B, BPFP=1.6218 Using 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: 3,564B, BPFP=22.2750 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: 22,488B, BPFP=1.8253 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: 71,844B, BPFP=1.8223 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 37,080B, BPFP=0.5658 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 37,084B, BPFP=0.5659 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 46,332B, BPFP=1.4139 ⌛️ [2/4] FRONTEND: Frontend time: 0.298s (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: 0.464s [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 0.49246740 text_encoder-item0.clip_prompt_embeds 0.00021560 23.74827093 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023980 0.49839144 text_encoder_2-item1.clip_prompt_embeds 0.00021108 0.07869168 text_encoder_3-item2.t5_prompt_embeds 0.00000804 0.00122574 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00544735 0.40402344 vae.encoder_f1 0.00544843 0.40396836 vae.decoder 0.00018632 0.02519291 ------------------------------------------------------------------------------------- TOTAL 0.00257940 2.39578562 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 319768 BPFP 1.1314 bits/point EBPFP 2.2629 equivalent bits/point MSE 2.395786 ---------------------- -------------------------------------------------------- Time: 0.770s Load: 0.009s, Pack+Encode: 0.298s, Decode+Unpack: 0.464s ---------------------- -------------------------------------------------------- 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 2.3958 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000285788.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 0.009s ------------------------------------------------------------ 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: 2,232B, BPFP=23.2500 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: 12,008B, BPFP=1.6245 Using 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: 3,804B, BPFP=23.7750 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: 21,840B, BPFP=1.7727 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: 67,520B, BPFP=1.7127 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 60,616B, BPFP=0.9249 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 60,612B, BPFP=0.9249 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 36,756B, BPFP=1.1217 ⌛️ [2/4] FRONTEND: Frontend time: 0.297s (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: 0.467s [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 0.50398537 text_encoder-item0.clip_prompt_embeds 0.00022698 180.14133523 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00024914 0.52750278 text_encoder_2-item1.clip_prompt_embeds 0.00021102 0.07842491 text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00123545 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00630479 0.71739650 vae.encoder_f1 0.00631430 0.71754038 vae.decoder 0.00018596 0.02009618 ------------------------------------------------------------------------------------- TOTAL 0.00298001 6.63102697 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 352452 BPFP 1.2471 bits/point EBPFP 2.4941 equivalent bits/point MSE 6.631027 ---------------------- -------------------------------------------------------- Time: 0.773s Load: 0.009s, Pack+Encode: 0.297s, Decode+Unpack: 0.467s ---------------------- -------------------------------------------------------- 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 6.6310 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000287291.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 0.009s ------------------------------------------------------------ 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: 2,404B, BPFP=25.0417 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: 12,184B, BPFP=1.6483 Using 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: 3,780B, BPFP=23.6250 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: 21,412B, BPFP=1.7380 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: 65,404B, BPFP=1.6590 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 58,328B, BPFP=0.8900 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 58,340B, BPFP=0.8902 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 42,136B, BPFP=1.2859 ⌛️ [2/4] FRONTEND: Frontend time: 0.304s (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: 0.463s [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 0.52021043 text_encoder-item0.clip_prompt_embeds 0.00024643 144.01396780 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022451 0.53653822 text_encoder_2-item1.clip_prompt_embeds 0.00018967 0.07997934 text_encoder_3-item2.t5_prompt_embeds 0.00000778 0.00110564 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00612578 0.62046170 vae.encoder_f1 0.00613243 0.62047559 vae.decoder 0.00018179 0.02162029 ------------------------------------------------------------------------------------- TOTAL 0.00289482 5.64137118 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 351052 BPFP 1.2421 bits/point EBPFP 2.4842 equivalent bits/point MSE 5.641371 ---------------------- -------------------------------------------------------- Time: 0.777s Load: 0.009s, Pack+Encode: 0.304s, Decode+Unpack: 0.463s ---------------------- -------------------------------------------------------- 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 5.6414 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000289343.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 0.009s ------------------------------------------------------------ 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: 2,316B, BPFP=24.1250 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: 14,048B, BPFP=1.9004 Using 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: 3,788B, BPFP=23.6750 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: 24,908B, BPFP=2.0218 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: 77,220B, BPFP=1.9587 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 18,116B, BPFP=0.2764 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 18,116B, BPFP=0.2764 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 46,724B, BPFP=1.4259 ⌛️ [2/4] FRONTEND: Frontend time: 0.300s (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: 0.462s [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 0.48118234 text_encoder-item0.clip_prompt_embeds 0.00024049 23.80690273 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023104 0.52865558 text_encoder_2-item1.clip_prompt_embeds 0.00016878 0.08980509 text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00142007 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00526071 0.21915515 vae.encoder_f1 0.00526072 0.21916495 vae.decoder 0.00016981 0.02302129 ------------------------------------------------------------------------------------- TOTAL 0.00248947 2.31187123 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 292300 BPFP 1.0342 bits/point EBPFP 2.0685 equivalent bits/point MSE 2.311871 ---------------------- -------------------------------------------------------- Time: 0.770s Load: 0.009s, Pack+Encode: 0.300s, Decode+Unpack: 0.462s ---------------------- -------------------------------------------------------- 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 2.3119 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000304545.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 2,312B, BPFP=24.0833 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: 12,380B, BPFP=1.6748 Using 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: 3,660B, BPFP=22.8750 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: 20,908B, BPFP=1.6971 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: 67,920B, BPFP=1.7228 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 62,080B, BPFP=0.9473 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 62,092B, BPFP=0.9474 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 40,384B, BPFP=1.2324 ⌛️ [2/4] FRONTEND: Frontend time: 0.299s (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: 0.465s [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 0.48976898 text_encoder-item0.clip_prompt_embeds 0.00022843 60.66954140 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00086038 0.50823560 text_encoder_2-item1.clip_prompt_embeds 0.00016207 0.07512865 text_encoder_3-item2.t5_prompt_embeds 0.00000746 0.00129153 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00622977 0.72328627 vae.encoder_f1 0.00623684 0.72343653 vae.decoder 0.00019755 0.02191581 ------------------------------------------------------------------------------------- TOTAL 0.00294358 3.50904753 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 358800 BPFP 1.2695 bits/point EBPFP 2.5391 equivalent bits/point MSE 3.509048 ---------------------- -------------------------------------------------------- Time: 0.772s Load: 0.008s, Pack+Encode: 0.299s, Decode+Unpack: 0.465s ---------------------- -------------------------------------------------------- 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 3.5090 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000310622.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 0.009s ------------------------------------------------------------ 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: 2,408B, BPFP=25.0833 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: 13,196B, BPFP=1.7852 Using 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: 3,800B, BPFP=23.7500 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: 21,796B, BPFP=1.7692 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: 68,064B, BPFP=1.7265 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 44,664B, BPFP=0.6815 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 44,664B, BPFP=0.6815 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 30,400B, BPFP=0.9277 ⌛️ [2/4] FRONTEND: Frontend time: 0.299s (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: 0.464s [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 0.50971278 text_encoder-item0.clip_prompt_embeds 0.00026004 47.90287219 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025016 0.55880423 text_encoder_2-item1.clip_prompt_embeds 0.00015074 0.07980881 text_encoder_3-item2.t5_prompt_embeds 0.00000873 0.00120938 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00725303 0.70728201 vae.encoder_f1 0.00725507 0.70711422 vae.decoder 0.00017991 0.01756849 ------------------------------------------------------------------------------------- TOTAL 0.00341494 3.16736459 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 316056 BPFP 1.1183 bits/point EBPFP 2.2366 equivalent bits/point MSE 3.167365 ---------------------- -------------------------------------------------------- Time: 0.772s Load: 0.009s, Pack+Encode: 0.299s, Decode+Unpack: 0.464s ---------------------- -------------------------------------------------------- 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 3.1674 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000311394.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 2,340B, BPFP=24.3750 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: 13,368B, BPFP=1.8084 Using 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: 3,820B, BPFP=23.8750 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: 22,232B, BPFP=1.8045 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: 69,064B, BPFP=1.7518 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 44,112B, BPFP=0.6731 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 44,116B, BPFP=0.6732 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 24,584B, BPFP=0.7502 ⌛️ [2/4] FRONTEND: Frontend time: 0.301s (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: 0.463s [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 0.53437507 text_encoder-item0.clip_prompt_embeds 0.00031748 23.76459771 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022063 0.58040566 text_encoder_2-item1.clip_prompt_embeds 0.00019717 0.08562690 text_encoder_3-item2.t5_prompt_embeds 0.00000812 0.00119875 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.42111695 2.52626872 vae.encoder_f1 0.42111716 2.52713490 vae.decoder 0.00019827 0.01641201 ------------------------------------------------------------------------------------- TOTAL 0.19535708 3.37999710 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 310700 BPFP 1.0993 bits/point EBPFP 2.1987 equivalent bits/point MSE 3.379997 ---------------------- -------------------------------------------------------- Time: 0.773s Load: 0.008s, Pack+Encode: 0.301s, Decode+Unpack: 0.463s ---------------------- -------------------------------------------------------- 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 3.3800 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000316015.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 0.009s ------------------------------------------------------------ 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: 2,384B, BPFP=24.8333 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: 12,924B, BPFP=1.7484 Using 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: 3,640B, BPFP=22.7500 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: 22,384B, BPFP=1.8169 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: 73,932B, BPFP=1.8753 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 53,672B, BPFP=0.8190 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 53,672B, BPFP=0.8190 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 28,840B, BPFP=0.8801 ⌛️ [2/4] FRONTEND: Frontend time: 0.301s (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: 0.466s [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 0.49854306 text_encoder-item0.clip_prompt_embeds 0.00024951 119.91502638 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020437 0.49751949 text_encoder_2-item1.clip_prompt_embeds 0.00016387 0.08333195 text_encoder_3-item2.t5_prompt_embeds 0.00000772 0.00136960 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.10376993 2.41922903 vae.encoder_f1 0.10377157 2.41908550 vae.decoder 0.00019787 0.01805470 ------------------------------------------------------------------------------------- TOTAL 0.04817852 5.84498054 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 338512 BPFP 1.1977 bits/point EBPFP 2.3955 equivalent bits/point MSE 5.844981 ---------------------- -------------------------------------------------------- Time: 0.775s Load: 0.009s, Pack+Encode: 0.301s, Decode+Unpack: 0.466s ---------------------- -------------------------------------------------------- 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 5.8450 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000323571.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 2,320B, BPFP=24.1667 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: 12,796B, BPFP=1.7311 Using 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: 3,660B, BPFP=22.8750 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: 21,916B, BPFP=1.7789 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: 67,736B, BPFP=1.7181 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 55,208B, BPFP=0.8424 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 55,208B, BPFP=0.8424 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 26,576B, BPFP=0.8110 ⌛️ [2/4] FRONTEND: Frontend time: 0.304s (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: 0.463s [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 0.48565257 text_encoder-item0.clip_prompt_embeds 0.00022350 108.41731771 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00046887 0.49732246 text_encoder_2-item1.clip_prompt_embeds 0.00019271 0.08280684 text_encoder_3-item2.t5_prompt_embeds 0.00000799 0.00104349 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.01346414 1.14341223 vae.encoder_f1 0.01346933 1.14482534 vae.decoder 0.00019243 0.01649771 ------------------------------------------------------------------------------------- TOTAL 0.00629858 4.95268363 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 332484 BPFP 1.1764 bits/point EBPFP 2.3528 equivalent bits/point MSE 4.952684 ---------------------- -------------------------------------------------------- Time: 0.774s Load: 0.008s, Pack+Encode: 0.304s, Decode+Unpack: 0.463s ---------------------- -------------------------------------------------------- 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 4.9527 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325483.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 2,448B, BPFP=25.5000 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: 12,748B, BPFP=1.7246 Using 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: 3,776B, BPFP=23.6000 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: 21,736B, BPFP=1.7643 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: 73,364B, BPFP=1.8609 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 47,504B, BPFP=0.7249 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 47,500B, BPFP=0.7248 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 27,004B, BPFP=0.8241 ⌛️ [2/4] FRONTEND: Frontend time: 0.302s (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: 0.471s [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 0.49797034 text_encoder-item0.clip_prompt_embeds 0.00024958 119.63567877 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00135081 0.55335102 text_encoder_2-item1.clip_prompt_embeds 0.00018030 0.08308481 text_encoder_3-item2.t5_prompt_embeds 0.00000823 0.00127722 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.11196710 1.84779167 vae.encoder_f1 0.11196851 1.84796393 vae.decoder 0.00023459 0.01975918 ------------------------------------------------------------------------------------- TOTAL 0.05198575 5.57293840 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 323144 BPFP 1.1434 bits/point EBPFP 2.2867 equivalent bits/point MSE 5.572938 ---------------------- -------------------------------------------------------- Time: 0.780s Load: 0.008s, Pack+Encode: 0.302s, Decode+Unpack: 0.471s ---------------------- -------------------------------------------------------- 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 5.5729 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325991.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 0.009s ------------------------------------------------------------ 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: 2,408B, BPFP=25.0833 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: 13,588B, BPFP=1.8382 Using 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: 3,840B, BPFP=24.0000 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: 22,244B, BPFP=1.8055 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: 67,296B, BPFP=1.7070 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 66,300B, BPFP=1.0117 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 66,304B, BPFP=1.0117 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 34,172B, BPFP=1.0428 ⌛️ [2/4] FRONTEND: Frontend time: 0.300s (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: 0.471s [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 0.47203640 text_encoder-item0.clip_prompt_embeds 0.00025929 59.78582420 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021916 0.53630886 text_encoder_2-item1.clip_prompt_embeds 0.00042246 0.13297087 text_encoder_3-item2.t5_prompt_embeds 0.00000781 0.00113334 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00675017 0.93124115 vae.encoder_f1 0.00675421 0.93134141 vae.decoder 0.00023635 0.02251898 ------------------------------------------------------------------------------------- TOTAL 0.00320042 3.58494442 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 363216 BPFP 1.2852 bits/point EBPFP 2.5703 equivalent bits/point MSE 3.584944 ---------------------- -------------------------------------------------------- Time: 0.780s Load: 0.009s, Pack+Encode: 0.300s, Decode+Unpack: 0.471s ---------------------- -------------------------------------------------------- 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 3.5849 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000329319.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 2,344B, BPFP=24.4167 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: 12,176B, BPFP=1.6472 Using 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: 3,720B, BPFP=23.2500 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: 21,308B, BPFP=1.7295 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: 71,088B, BPFP=1.8032 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 74,980B, BPFP=1.1441 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 74,980B, BPFP=1.1441 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 33,956B, BPFP=1.0363 ⌛️ [2/4] FRONTEND: Frontend time: 0.299s (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: 0.464s [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 0.45372311 text_encoder-item0.clip_prompt_embeds 0.00064775 179.77473958 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00128483 0.52098689 text_encoder_2-item1.clip_prompt_embeds 0.00019620 0.07480772 text_encoder_3-item2.t5_prompt_embeds 0.00000792 0.00138885 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00728993 1.18077743 vae.encoder_f1 0.00729572 1.18090463 vae.decoder 0.00026488 0.02244144 ------------------------------------------------------------------------------------- TOTAL 0.00345536 6.83645101 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 381616 BPFP 1.3503 bits/point EBPFP 2.7005 equivalent bits/point MSE 6.836451 ---------------------- -------------------------------------------------------- Time: 0.772s Load: 0.009s, Pack+Encode: 0.299s, Decode+Unpack: 0.464s ---------------------- -------------------------------------------------------- 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 6.8365 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000335081.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 2,424B, BPFP=25.2500 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: 12,996B, BPFP=1.7581 Using 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: 3,776B, BPFP=23.6000 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: 21,752B, BPFP=1.7656 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: 71,856B, BPFP=1.8226 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 65,648B, BPFP=1.0017 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 65,648B, BPFP=1.0017 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 35,988B, BPFP=1.0983 ⌛️ [2/4] FRONTEND: Frontend time: 0.300s (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: 0.465s [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 0.55332029 text_encoder-item0.clip_prompt_embeds 0.00023188 23.77874729 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022679 0.51550674 text_encoder_2-item1.clip_prompt_embeds 0.00015622 0.08121444 text_encoder_3-item2.t5_prompt_embeds 0.00000782 0.00122988 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00613207 0.74432629 vae.encoder_f1 0.00613899 0.74413580 vae.decoder 0.00023812 0.02258370 ------------------------------------------------------------------------------------- TOTAL 0.00290239 2.55421132 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 367152 BPFP 1.2991 bits/point EBPFP 2.5982 equivalent bits/point MSE 2.554211 ---------------------- -------------------------------------------------------- Time: 0.773s Load: 0.009s, Pack+Encode: 0.300s, Decode+Unpack: 0.465s ---------------------- -------------------------------------------------------- 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 2.5542 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000342186.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 0.008s ------------------------------------------------------------ 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: 2,412B, BPFP=25.1250 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: 12,996B, BPFP=1.7581 Using 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: 3,624B, BPFP=22.6500 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: 21,848B, BPFP=1.7734 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: 71,020B, BPFP=1.8014 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 55,760B, BPFP=0.8508 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 55,760B, BPFP=0.8508 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 32,388B, BPFP=0.9884 ⌛️ [2/4] FRONTEND: Frontend time: 0.297s (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: 0.462s [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 0.46438134 text_encoder-item0.clip_prompt_embeds 0.00023678 34.75587417 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00028948 0.52131362 text_encoder_2-item1.clip_prompt_embeds 0.00019061 0.08598251 text_encoder_3-item2.t5_prompt_embeds 0.00000767 0.00118111 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00636537 0.87504423 vae.encoder_f1 0.00636991 0.87537432 vae.decoder 0.00025538 0.02209177 ------------------------------------------------------------------------------------- TOTAL 0.00301360 2.90217750 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 342872 BPFP 1.2132 bits/point EBPFP 2.4263 equivalent bits/point MSE 2.902177 ---------------------- -------------------------------------------------------- Time: 0.767s Load: 0.008s, Pack+Encode: 0.297s, Decode+Unpack: 0.462s ---------------------- -------------------------------------------------------- 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 2.9022 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000343976.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 2,384B, BPFP=24.8333 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: 12,124B, BPFP=1.6402 Using 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: 3,508B, BPFP=21.9250 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: 20,900B, BPFP=1.6964 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: 66,024B, BPFP=1.6747 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 54,896B, BPFP=0.8376 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 54,900B, BPFP=0.8377 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 25,796B, BPFP=0.7872 ⌛️ [2/4] FRONTEND: Frontend time: 0.298s (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: 0.467s [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 0.50232073 text_encoder-item0.clip_prompt_embeds 0.00023432 144.56659226 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018703 0.46344872 text_encoder_2-item1.clip_prompt_embeds 0.00017889 0.07839063 text_encoder_3-item2.t5_prompt_embeds 0.00000811 0.00126104 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.23155926 2.37509537 vae.encoder_f1 0.23156048 2.37418079 vae.decoder 0.00018572 0.01659951 ------------------------------------------------------------------------------------- TOTAL 0.10744199 6.46867572 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 327596 BPFP 1.1591 bits/point EBPFP 2.3182 equivalent bits/point MSE 6.468676 ---------------------- -------------------------------------------------------- Time: 0.772s Load: 0.008s, Pack+Encode: 0.298s, Decode+Unpack: 0.467s ---------------------- -------------------------------------------------------- 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 6.4687 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000351362.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 2,328B, BPFP=24.2500 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: 12,116B, BPFP=1.6391 Using 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: 3,672B, BPFP=22.9500 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: 21,708B, BPFP=1.7620 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: 69,328B, BPFP=1.7585 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 68,504B, BPFP=1.0453 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 68,500B, BPFP=1.0452 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 40,684B, BPFP=1.2416 ⌛️ [2/4] FRONTEND: Frontend time: 0.296s (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: 0.465s [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 0.52839712 text_encoder-item0.clip_prompt_embeds 0.00022528 84.14475954 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022839 0.52532291 text_encoder_2-item1.clip_prompt_embeds 0.00016484 0.07652411 text_encoder_3-item2.t5_prompt_embeds 0.00000786 0.00135395 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00729824 0.97324800 vae.encoder_f1 0.00730369 0.97339004 vae.decoder 0.00019938 0.02409129 ------------------------------------------------------------------------------------- TOTAL 0.00343853 4.23930625 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 373904 BPFP 1.3230 bits/point EBPFP 2.6459 equivalent bits/point MSE 4.239306 ---------------------- -------------------------------------------------------- Time: 0.769s Load: 0.008s, Pack+Encode: 0.296s, Decode+Unpack: 0.465s ---------------------- -------------------------------------------------------- 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 4.2393 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000357816.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 0.009s ------------------------------------------------------------ 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: 2,328B, BPFP=24.2500 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: 11,688B, BPFP=1.5812 Using 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: 3,744B, BPFP=23.4000 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: 21,740B, BPFP=1.7646 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: 63,928B, BPFP=1.6216 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 45,992B, BPFP=0.7018 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 45,988B, BPFP=0.7017 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 45,332B, BPFP=1.3834 ⌛️ [2/4] FRONTEND: Frontend time: 0.301s (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: 0.462s [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 0.46040050 text_encoder-item0.clip_prompt_embeds 0.00022149 143.95987216 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018477 0.51889801 text_encoder_2-item1.clip_prompt_embeds 0.00103146 0.08532460 text_encoder_3-item2.t5_prompt_embeds 0.00000778 0.00107802 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00564371 0.54373801 vae.encoder_f1 0.00565042 0.54394186 vae.decoder 0.00019980 0.02516175 ------------------------------------------------------------------------------------- TOTAL 0.00270919 5.60502782 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 327804 BPFP 1.1599 bits/point EBPFP 2.3197 equivalent bits/point MSE 5.605028 ---------------------- -------------------------------------------------------- Time: 0.771s Load: 0.009s, Pack+Encode: 0.301s, Decode+Unpack: 0.462s ---------------------- -------------------------------------------------------- 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 5.6050 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361180.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 2,424B, BPFP=25.2500 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: 12,532B, BPFP=1.6953 Using 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: 3,796B, BPFP=23.7250 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: 22,304B, BPFP=1.8104 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: 66,656B, BPFP=1.6907 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 49,780B, BPFP=0.7596 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 49,788B, BPFP=0.7597 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 43,572B, BPFP=1.3297 ⌛️ [2/4] FRONTEND: Frontend time: 0.301s (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: 0.462s [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 0.46923780 text_encoder-item0.clip_prompt_embeds 0.00022173 71.74961107 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022739 0.50175037 text_encoder_2-item1.clip_prompt_embeds 0.00103962 0.09104679 text_encoder_3-item2.t5_prompt_embeds 0.00000788 0.00100949 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00576096 0.49931312 vae.encoder_f1 0.00576981 0.49893039 vae.decoder 0.00019592 0.02383656 ------------------------------------------------------------------------------------- TOTAL 0.00276400 3.69571695 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 337916 BPFP 1.1956 bits/point EBPFP 2.3913 equivalent bits/point MSE 3.695717 ---------------------- -------------------------------------------------------- Time: 0.772s Load: 0.008s, Pack+Encode: 0.301s, Decode+Unpack: 0.462s ---------------------- -------------------------------------------------------- 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 3.6957 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361268.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 2,480B, BPFP=25.8333 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: 13,776B, BPFP=1.8636 Using 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: 3,772B, BPFP=23.5750 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: 23,060B, BPFP=1.8718 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: 74,668B, BPFP=1.8940 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 41,916B, BPFP=0.6396 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 41,916B, BPFP=0.6396 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 30,028B, BPFP=0.9164 ⌛️ [2/4] FRONTEND: Frontend time: 0.300s (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: 0.464s [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 0.50556572 text_encoder-item0.clip_prompt_embeds 0.00025917 84.77468885 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023350 0.51933002 text_encoder_2-item1.clip_prompt_embeds 0.00019057 0.08904693 text_encoder_3-item2.t5_prompt_embeds 0.00000791 0.00124197 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00594818 0.56097913 vae.encoder_f1 0.00595328 0.56112051 vae.decoder 0.00023462 0.01981568 ------------------------------------------------------------------------------------- TOTAL 0.00281845 4.06460806 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 318680 BPFP 1.1276 bits/point EBPFP 2.2552 equivalent bits/point MSE 4.064608 ---------------------- -------------------------------------------------------- Time: 0.771s Load: 0.008s, Pack+Encode: 0.300s, Decode+Unpack: 0.464s ---------------------- -------------------------------------------------------- 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 4.0646 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000367228.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 2,356B, BPFP=24.5417 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: 14,272B, BPFP=1.9307 Using 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: 3,856B, BPFP=24.1000 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: 24,184B, BPFP=1.9630 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: 75,832B, BPFP=1.9235 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 37,720B, BPFP=0.5756 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 37,720B, BPFP=0.5756 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 15,712B, BPFP=0.4795 ⌛️ [2/4] FRONTEND: Frontend time: 0.304s (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: 0.466s [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 0.47235640 text_encoder-item0.clip_prompt_embeds 0.00022579 23.76673684 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020263 0.52824516 text_encoder_2-item1.clip_prompt_embeds 0.00017578 0.11955396 text_encoder_3-item2.t5_prompt_embeds 0.00000800 0.00142868 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.85445058 3.82381535 vae.encoder_f1 0.85445166 3.82573938 vae.decoder 0.00025257 0.01197428 ------------------------------------------------------------------------------------- TOTAL 0.39632643 3.98300499 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 298716 BPFP 1.0569 bits/point EBPFP 2.1139 equivalent bits/point MSE 3.983005 ---------------------- -------------------------------------------------------- Time: 0.778s Load: 0.008s, Pack+Encode: 0.304s, Decode+Unpack: 0.466s ---------------------- -------------------------------------------------------- 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 3.9830 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000369503.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 2,404B, BPFP=25.0417 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: 12,448B, BPFP=1.6840 Using 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: 3,740B, BPFP=23.3750 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: 21,736B, BPFP=1.7643 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: 69,920B, BPFP=1.7735 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 72,004B, BPFP=1.0987 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 72,004B, BPFP=1.0987 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 43,968B, BPFP=1.3418 ⌛️ [2/4] FRONTEND: Frontend time: 0.301s (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: 0.463s [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 0.45654376 text_encoder-item0.clip_prompt_embeds 0.00025458 58.80261178 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00158787 0.49952922 text_encoder_2-item1.clip_prompt_embeds 0.00016969 0.12080444 text_encoder_3-item2.t5_prompt_embeds 0.00000826 0.00120752 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00628510 0.77043593 vae.encoder_f1 0.00629234 0.77047807 vae.decoder 0.00023521 0.02617096 ------------------------------------------------------------------------------------- TOTAL 0.00297516 3.48451612 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 385288 BPFP 1.3633 bits/point EBPFP 2.7265 equivalent bits/point MSE 3.484516 ---------------------- -------------------------------------------------------- Time: 0.772s Load: 0.008s, Pack+Encode: 0.301s, Decode+Unpack: 0.463s ---------------------- -------------------------------------------------------- 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 3.4845 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000370486.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 0.009s ------------------------------------------------------------ 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: 2,316B, BPFP=24.1250 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: 12,348B, BPFP=1.6705 Using 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: 3,780B, BPFP=23.6250 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: 21,516B, BPFP=1.7464 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: 71,464B, BPFP=1.8127 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 51,392B, BPFP=0.7842 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 51,392B, BPFP=0.7842 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 38,744B, BPFP=1.1824 ⌛️ [2/4] FRONTEND: Frontend time: 0.299s (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: 0.464s [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 0.52794874 text_encoder-item0.clip_prompt_embeds 0.00022807 155.18114177 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00029471 0.56390877 text_encoder_2-item1.clip_prompt_embeds 0.00018746 0.07938852 text_encoder_3-item2.t5_prompt_embeds 0.00000782 0.00126110 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00573429 0.53626847 vae.encoder_f1 0.00574192 0.53630054 vae.decoder 0.00017875 0.02077804 ------------------------------------------------------------------------------------- TOTAL 0.00271248 5.89432189 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 340016 BPFP 1.2031 bits/point EBPFP 2.4061 equivalent bits/point MSE 5.894322 ---------------------- -------------------------------------------------------- Time: 0.771s Load: 0.009s, Pack+Encode: 0.299s, Decode+Unpack: 0.464s ---------------------- -------------------------------------------------------- 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 5.8943 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377635.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 2,332B, BPFP=24.2917 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: 14,656B, BPFP=1.9827 Using 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: 3,660B, BPFP=22.8750 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: 25,108B, BPFP=2.0380 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: 86,512B, BPFP=2.1944 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 65,092B, BPFP=0.9932 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 65,092B, BPFP=0.9932 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 40,056B, BPFP=1.2224 ⌛️ [2/4] FRONTEND: Frontend time: 0.302s (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: 0.466s [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 0.50380373 text_encoder-item0.clip_prompt_embeds 0.00027120 47.87293696 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023509 0.51744938 text_encoder_2-item1.clip_prompt_embeds 0.00019567 0.09034401 text_encoder_3-item2.t5_prompt_embeds 0.00000829 0.00180148 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00781570 1.10295236 vae.encoder_f1 0.00781878 1.10300326 vae.decoder 0.00029724 0.02721874 ------------------------------------------------------------------------------------- TOTAL 0.00369190 3.35176693 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 389572 BPFP 1.3784 bits/point EBPFP 2.7568 equivalent bits/point MSE 3.351767 ---------------------- -------------------------------------------------------- Time: 0.775s Load: 0.008s, Pack+Encode: 0.302s, Decode+Unpack: 0.466s ---------------------- -------------------------------------------------------- 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 3.3518 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377814.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 0.009s ------------------------------------------------------------ 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: 2,432B, BPFP=25.3333 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: 12,592B, BPFP=1.7035 Using 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: 3,504B, BPFP=21.9000 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: 22,208B, BPFP=1.8026 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: 70,164B, BPFP=1.7797 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 59,456B, BPFP=0.9072 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 59,468B, BPFP=0.9074 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 46,992B, BPFP=1.4341 ⌛️ [2/4] FRONTEND: Frontend time: 0.297s (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: 0.504s [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 0.53115157 text_encoder-item0.clip_prompt_embeds 0.00022930 60.60784040 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00047978 0.48325181 text_encoder_2-item1.clip_prompt_embeds 0.00018160 0.08385413 text_encoder_3-item2.t5_prompt_embeds 0.00000828 0.00121788 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00577752 0.54080039 vae.encoder_f1 0.00578475 0.54072678 vae.decoder 0.00024190 0.02594409 ------------------------------------------------------------------------------------- TOTAL 0.00273964 3.42358774 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 363880 BPFP 1.2875 bits/point EBPFP 2.5750 equivalent bits/point MSE 3.423588 ---------------------- -------------------------------------------------------- Time: 0.811s Load: 0.009s, Pack+Encode: 0.297s, Decode+Unpack: 0.504s ---------------------- -------------------------------------------------------- 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 3.4236 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000379800.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 2,344B, BPFP=24.4167 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: 14,232B, BPFP=1.9253 Using 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: 3,940B, BPFP=24.6250 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: 23,580B, BPFP=1.9140 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: 75,520B, BPFP=1.9156 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 62,396B, BPFP=0.9521 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 62,384B, BPFP=0.9519 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 22,092B, BPFP=0.6742 ⌛️ [2/4] FRONTEND: Frontend time: 0.297s (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: 0.457s [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 0.54648733 text_encoder-item0.clip_prompt_embeds 0.00028764 35.77863738 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021081 0.55077477 text_encoder_2-item1.clip_prompt_embeds 0.00018283 0.07889577 text_encoder_3-item2.t5_prompt_embeds 0.00000777 0.00146587 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.03343784 1.86558807 vae.encoder_f1 0.03344063 1.86563993 vae.decoder 0.00016139 0.01404665 ------------------------------------------------------------------------------------- TOTAL 0.01555870 3.38708849 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 353552 BPFP 1.2510 bits/point EBPFP 2.5019 equivalent bits/point MSE 3.387088 ---------------------- -------------------------------------------------------- Time: 0.764s Load: 0.009s, Pack+Encode: 0.297s, Decode+Unpack: 0.457s ---------------------- -------------------------------------------------------- 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 3.3871 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000384808.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 0.009s ------------------------------------------------------------ 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: 2,300B, BPFP=23.9583 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: 11,980B, BPFP=1.6207 Using 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: 3,644B, BPFP=22.7750 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: 21,280B, BPFP=1.7273 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: 68,608B, BPFP=1.7403 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 70,512B, BPFP=1.0759 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 70,516B, BPFP=1.0760 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 42,252B, BPFP=1.2894 ⌛️ [2/4] FRONTEND: Frontend time: 0.294s (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: 0.456s [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 0.51390346 text_encoder-item0.clip_prompt_embeds 0.00023094 156.06772524 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00027942 0.47902541 text_encoder_2-item1.clip_prompt_embeds 0.00018965 0.07915428 text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00128839 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00637455 0.83273411 vae.encoder_f1 0.00637988 0.83272392 vae.decoder 0.00020059 0.02313625 ------------------------------------------------------------------------------------- TOTAL 0.00301333 6.05520608 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 378156 BPFP 1.3380 bits/point EBPFP 2.6760 equivalent bits/point MSE 6.055206 ---------------------- -------------------------------------------------------- Time: 0.759s Load: 0.009s, Pack+Encode: 0.294s, Decode+Unpack: 0.456s ---------------------- -------------------------------------------------------- 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 6.0552 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000396338.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 0.011s ------------------------------------------------------------ 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: 2,308B, BPFP=24.0417 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: 13,208B, BPFP=1.7868 Using 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: 3,852B, BPFP=24.0750 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: 22,220B, BPFP=1.8036 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: 71,512B, BPFP=1.8139 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 58,180B, BPFP=0.8878 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 58,180B, BPFP=0.8878 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 41,032B, BPFP=1.2522 ⌛️ [2/4] FRONTEND: Frontend time: 0.295s (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: 0.455s [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 0.51919866 text_encoder-item0.clip_prompt_embeds 0.00025217 24.07511288 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00026091 0.59651647 text_encoder_2-item1.clip_prompt_embeds 0.00018200 0.07665325 text_encoder_3-item2.t5_prompt_embeds 0.00000809 0.00121887 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00581597 0.55849028 vae.encoder_f1 0.00582356 0.55836791 vae.decoder 0.00019494 0.02260762 ------------------------------------------------------------------------------------- TOTAL 0.00275264 2.47563039 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 357556 BPFP 1.2651 bits/point EBPFP 2.5303 equivalent bits/point MSE 2.475630 ---------------------- -------------------------------------------------------- Time: 0.760s Load: 0.011s, Pack+Encode: 0.295s, Decode+Unpack: 0.455s ---------------------- -------------------------------------------------------- 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 2.4756 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000397303.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 0.008s ------------------------------------------------------------ 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: 2,400B, BPFP=25.0000 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: 12,080B, BPFP=1.6342 Using 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: 3,808B, BPFP=23.8000 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: 20,112B, BPFP=1.6325 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: 63,920B, BPFP=1.6213 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 33,256B, BPFP=0.5074 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 33,256B, BPFP=0.5074 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 33,512B, BPFP=1.0227 ⌛️ [2/4] FRONTEND: Frontend time: 0.295s (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: 0.459s [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 0.51617547 text_encoder-item0.clip_prompt_embeds 0.00026975 23.58670269 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022593 0.54160380 text_encoder_2-item1.clip_prompt_embeds 0.00015480 0.06935856 text_encoder_3-item2.t5_prompt_embeds 0.00000862 0.00111629 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 1.11695218 3.40792990 vae.encoder_f1 1.11695278 3.40815735 vae.decoder 0.00019720 0.02036643 ------------------------------------------------------------------------------------- TOTAL 0.51806274 3.78379218 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 289408 BPFP 1.0240 bits/point EBPFP 2.0480 equivalent bits/point MSE 3.783792 ---------------------- -------------------------------------------------------- Time: 0.761s Load: 0.008s, Pack+Encode: 0.295s, Decode+Unpack: 0.459s ---------------------- -------------------------------------------------------- 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 3.7838 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000402473.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 2,408B, BPFP=25.0833 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: 13,060B, BPFP=1.7668 Using 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: 3,660B, BPFP=22.8750 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: 21,672B, BPFP=1.7591 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: 70,652B, BPFP=1.7921 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 57,932B, BPFP=0.8840 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 57,932B, BPFP=0.8840 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 36,124B, BPFP=1.1024 ⌛️ [2/4] FRONTEND: Frontend time: 0.300s (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: 0.456s [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 0.49560225 text_encoder-item0.clip_prompt_embeds 0.00025545 71.88273640 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018422 0.43396549 text_encoder_2-item1.clip_prompt_embeds 0.00016916 0.07409274 text_encoder_3-item2.t5_prompt_embeds 0.00000823 0.00124754 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.01535016 1.26440990 vae.encoder_f1 0.01535382 1.26512825 vae.decoder 0.00021460 0.02240366 ------------------------------------------------------------------------------------- TOTAL 0.00717511 4.05338025 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 350504 BPFP 1.2402 bits/point EBPFP 2.4804 equivalent bits/point MSE 4.053380 ---------------------- -------------------------------------------------------- Time: 0.765s Load: 0.009s, Pack+Encode: 0.300s, Decode+Unpack: 0.456s ---------------------- -------------------------------------------------------- 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 4.0534 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000409211.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 2,412B, BPFP=25.1250 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: 13,448B, BPFP=1.8193 Using 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: 3,744B, BPFP=23.4000 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: 23,312B, BPFP=1.8922 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: 74,672B, BPFP=1.8941 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 50,720B, BPFP=0.7739 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 50,720B, BPFP=0.7739 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 49,256B, BPFP=1.5032 ⌛️ [2/4] FRONTEND: Frontend time: 0.295s (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: 0.455s [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 0.47210526 text_encoder-item0.clip_prompt_embeds 0.00022628 23.78665280 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00027089 0.55705237 text_encoder_2-item1.clip_prompt_embeds 0.00017658 0.08510340 text_encoder_3-item2.t5_prompt_embeds 0.00000761 0.00132027 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00589589 0.50998384 vae.encoder_f1 0.00590398 0.50985152 vae.decoder 0.00017838 0.02726898 ------------------------------------------------------------------------------------- TOTAL 0.00278687 2.44647231 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 355348 BPFP 1.2573 bits/point EBPFP 2.5146 equivalent bits/point MSE 2.446472 ---------------------- -------------------------------------------------------- Time: 0.759s Load: 0.009s, Pack+Encode: 0.295s, Decode+Unpack: 0.455s ---------------------- -------------------------------------------------------- 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 2.4465 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000427500.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 0.008s ------------------------------------------------------------ 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: 2,316B, BPFP=24.1250 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: 12,668B, BPFP=1.7137 Using 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: 3,748B, BPFP=23.4250 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: 21,668B, BPFP=1.7588 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: 66,864B, BPFP=1.6960 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 68,424B, BPFP=1.0441 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 68,424B, BPFP=1.0441 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 29,120B, BPFP=0.8887 ⌛️ [2/4] FRONTEND: Frontend time: 0.298s (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: 0.468s [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 0.48177838 text_encoder-item0.clip_prompt_embeds 0.00031548 23.74561181 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020720 0.52332687 text_encoder_2-item1.clip_prompt_embeds 0.00018318 0.07959194 text_encoder_3-item2.t5_prompt_embeds 0.00000772 0.00107137 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00725484 1.14497983 vae.encoder_f1 0.00725992 1.14463294 vae.decoder 0.00019960 0.01869438 ------------------------------------------------------------------------------------- TOTAL 0.00342155 2.73855509 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 360296 BPFP 1.2748 bits/point EBPFP 2.5496 equivalent bits/point MSE 2.738555 ---------------------- -------------------------------------------------------- Time: 0.774s Load: 0.008s, Pack+Encode: 0.298s, Decode+Unpack: 0.468s ---------------------- -------------------------------------------------------- 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 2.7386 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435208.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 0.009s ------------------------------------------------------------ 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: 2,380B, BPFP=24.7917 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: 11,580B, BPFP=1.5666 Using 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: 4,096B, BPFP=25.6000 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: 19,808B, BPFP=1.6078 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: 59,500B, BPFP=1.5092 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 59,324B, BPFP=0.9052 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 59,324B, BPFP=0.9052 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 26,332B, BPFP=0.8036 ⌛️ [2/4] FRONTEND: Frontend time: 0.300s (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: 0.467s [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 0.48641745 text_encoder-item0.clip_prompt_embeds 0.00021831 168.11999459 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025602 0.56489944 text_encoder_2-item1.clip_prompt_embeds 0.00016110 0.07500890 text_encoder_3-item2.t5_prompt_embeds 0.00000740 0.00096339 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00923516 1.09265172 vae.encoder_f1 0.00923823 1.09296989 vae.decoder 0.00019521 0.01622261 ------------------------------------------------------------------------------------- TOTAL 0.00433552 6.49006117 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 329408 BPFP 1.1655 bits/point EBPFP 2.3311 equivalent bits/point MSE 6.490061 ---------------------- -------------------------------------------------------- Time: 0.776s Load: 0.009s, Pack+Encode: 0.300s, Decode+Unpack: 0.467s ---------------------- -------------------------------------------------------- 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 6.4901 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435880.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 2,300B, BPFP=23.9583 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: 12,560B, BPFP=1.6991 Using 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: 3,656B, BPFP=22.8500 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: 22,372B, BPFP=1.8159 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: 71,636B, BPFP=1.8171 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 61,256B, BPFP=0.9347 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 61,256B, BPFP=0.9347 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 31,744B, BPFP=0.9688 ⌛️ [2/4] FRONTEND: Frontend time: 0.299s (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: 0.477s [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 0.50395902 text_encoder-item0.clip_prompt_embeds 0.00062166 191.48464556 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00050487 0.48646469 text_encoder_2-item1.clip_prompt_embeds 0.00018638 0.08166122 text_encoder_3-item2.t5_prompt_embeds 0.00000762 0.00141707 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00831779 1.06929135 vae.encoder_f1 0.00832197 1.06910133 vae.decoder 0.00023271 0.02029689 ------------------------------------------------------------------------------------- TOTAL 0.00392639 7.09099665 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 353844 BPFP 1.2520 bits/point EBPFP 2.5040 equivalent bits/point MSE 7.090997 ---------------------- -------------------------------------------------------- Time: 0.785s Load: 0.009s, Pack+Encode: 0.299s, Decode+Unpack: 0.477s ---------------------- -------------------------------------------------------- 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 7.0910 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000439593.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 0.009s ------------------------------------------------------------ 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: 2,368B, BPFP=24.6667 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: 12,860B, BPFP=1.7397 Using 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: 3,652B, BPFP=22.8250 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: 21,236B, BPFP=1.7237 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: 71,820B, BPFP=1.8217 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 52,148B, BPFP=0.7957 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 52,148B, BPFP=0.7957 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 34,216B, BPFP=1.0442 ⌛️ [2/4] FRONTEND: Frontend time: 0.302s (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: 0.467s [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 0.51537681 text_encoder-item0.clip_prompt_embeds 0.00022938 167.88309997 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00028331 0.49972801 text_encoder_2-item1.clip_prompt_embeds 0.00016501 0.07634946 text_encoder_3-item2.t5_prompt_embeds 0.00000786 0.00124272 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00626977 0.69195652 vae.encoder_f1 0.00627489 0.69191718 vae.decoder 0.00017842 0.02060118 ------------------------------------------------------------------------------------- TOTAL 0.00295919 6.29853066 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 337512 BPFP 1.1942 bits/point EBPFP 2.3884 equivalent bits/point MSE 6.298531 ---------------------- -------------------------------------------------------- Time: 0.778s Load: 0.009s, Pack+Encode: 0.302s, Decode+Unpack: 0.467s ---------------------- -------------------------------------------------------- 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 6.2985 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000441286.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 2,372B, BPFP=24.7083 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: 12,684B, BPFP=1.7159 Using 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: 3,676B, BPFP=22.9750 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: 22,072B, BPFP=1.7916 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: 69,856B, BPFP=1.7719 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 58,136B, BPFP=0.8871 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 58,132B, BPFP=0.8870 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 46,936B, BPFP=1.4324 ⌛️ [2/4] FRONTEND: Frontend time: 0.296s (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: 0.460s [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 0.42722551 text_encoder-item0.clip_prompt_embeds 0.00022180 48.24660951 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00120074 0.48497877 text_encoder_2-item1.clip_prompt_embeds 0.00017918 0.08620037 text_encoder_3-item2.t5_prompt_embeds 0.00000774 0.00121572 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00585720 0.56819499 vae.encoder_f1 0.00586586 0.56821704 vae.decoder 0.00016520 0.02346971 ------------------------------------------------------------------------------------- TOTAL 0.00276807 3.11278878 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 360928 BPFP 1.2771 bits/point EBPFP 2.5541 equivalent bits/point MSE 3.112789 ---------------------- -------------------------------------------------------- Time: 0.764s Load: 0.008s, Pack+Encode: 0.296s, Decode+Unpack: 0.460s ---------------------- -------------------------------------------------------- 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 3.1128 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000445365.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 2,380B, BPFP=24.7917 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: 12,560B, BPFP=1.6991 Using 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: 3,556B, BPFP=22.2250 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: 22,112B, BPFP=1.7948 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: 69,784B, BPFP=1.7701 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 42,260B, BPFP=0.6448 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 42,256B, BPFP=0.6448 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 24,684B, BPFP=0.7533 ⌛️ [2/4] FRONTEND: Frontend time: 0.293s (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: 0.456s [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 0.52954495 text_encoder-item0.clip_prompt_embeds 0.00025784 23.68481551 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00017733 0.45779386 text_encoder_2-item1.clip_prompt_embeds 0.00015430 0.07763542 text_encoder_3-item2.t5_prompt_embeds 0.00000823 0.00125978 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00734802 0.92926228 vae.encoder_f1 0.00734987 0.92918175 vae.decoder 0.00018093 0.01495154 ------------------------------------------------------------------------------------- TOTAL 0.00345989 2.63646998 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 306656 BPFP 1.0850 bits/point EBPFP 2.1701 equivalent bits/point MSE 2.636470 ---------------------- -------------------------------------------------------- Time: 0.757s Load: 0.008s, Pack+Encode: 0.293s, Decode+Unpack: 0.456s ---------------------- -------------------------------------------------------- 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 2.6365 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000449996.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 2,380B, BPFP=24.7917 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: 13,384B, BPFP=1.8106 Using 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: 3,624B, BPFP=22.6500 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: 22,604B, BPFP=1.8347 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: 68,280B, BPFP=1.7319 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 68,024B, BPFP=1.0380 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 68,024B, BPFP=1.0380 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 39,228B, BPFP=1.1971 ⌛️ [2/4] FRONTEND: Frontend time: 0.293s (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: 0.460s [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 0.50658651 text_encoder-item0.clip_prompt_embeds 0.00023510 84.16739380 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023039 0.50067997 text_encoder_2-item1.clip_prompt_embeds 0.00019044 0.07716956 text_encoder_3-item2.t5_prompt_embeds 0.00000826 0.00117310 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00637359 0.87771606 vae.encoder_f1 0.00637830 0.87792820 vae.decoder 0.00018566 0.02221701 ------------------------------------------------------------------------------------- TOTAL 0.00300937 4.19537407 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 372612 BPFP 1.3184 bits/point EBPFP 2.6368 equivalent bits/point MSE 4.195374 ---------------------- -------------------------------------------------------- Time: 0.762s Load: 0.008s, Pack+Encode: 0.293s, Decode+Unpack: 0.460s ---------------------- -------------------------------------------------------- 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 4.1954 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000451714.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 0.009s ------------------------------------------------------------ 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: 2,404B, BPFP=25.0417 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: 12,088B, BPFP=1.6353 Using 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: 3,732B, BPFP=23.3250 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: 22,420B, BPFP=1.8198 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: 71,188B, BPFP=1.8057 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 50,016B, BPFP=0.7632 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 50,016B, BPFP=0.7632 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 26,232B, BPFP=0.8005 ⌛️ [2/4] FRONTEND: Frontend time: 0.293s (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: 0.456s [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 0.47545513 text_encoder-item0.clip_prompt_embeds 0.00026418 203.70999053 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018200 0.49676242 text_encoder_2-item1.clip_prompt_embeds 0.00017999 0.11779821 text_encoder_3-item2.t5_prompt_embeds 0.00000755 0.00134702 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.01530954 1.25993657 vae.encoder_f1 0.01531230 1.25808525 vae.decoder 0.00017892 0.01593295 ------------------------------------------------------------------------------------- TOTAL 0.00715252 7.49983484 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 325160 BPFP 1.1505 bits/point EBPFP 2.3010 equivalent bits/point MSE 7.499835 ---------------------- -------------------------------------------------------- Time: 0.757s Load: 0.009s, Pack+Encode: 0.293s, Decode+Unpack: 0.456s ---------------------- -------------------------------------------------------- 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 7.4998 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000464358.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 0.008s ------------------------------------------------------------ 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: 2,420B, BPFP=25.2083 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: 12,868B, BPFP=1.7408 Using 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: 3,660B, BPFP=22.8750 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: 22,404B, BPFP=1.8185 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: 71,248B, BPFP=1.8072 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 65,788B, BPFP=1.0038 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 65,784B, BPFP=1.0038 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 37,412B, BPFP=1.1417 ⌛️ [2/4] FRONTEND: Frontend time: 0.295s (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: 0.459s [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 0.51503861 text_encoder-item0.clip_prompt_embeds 0.00021481 59.82544051 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00019636 0.47887006 text_encoder_2-item1.clip_prompt_embeds 0.00020983 0.08466583 text_encoder_3-item2.t5_prompt_embeds 0.00000831 0.00127239 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00591154 0.83463204 vae.encoder_f1 0.00591973 0.83488774 vae.decoder 0.00025286 0.02320026 ------------------------------------------------------------------------------------- TOTAL 0.00280398 3.53918718 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 368648 BPFP 1.3044 bits/point EBPFP 2.6088 equivalent bits/point MSE 3.539187 ---------------------- -------------------------------------------------------- Time: 0.762s Load: 0.008s, Pack+Encode: 0.295s, Decode+Unpack: 0.459s ---------------------- -------------------------------------------------------- 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 3.5392 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000466256.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 0.009s ------------------------------------------------------------ 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: 2,332B, BPFP=24.2917 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: 13,520B, BPFP=1.8290 Using 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: 3,652B, BPFP=22.8250 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: 22,908B, BPFP=1.8594 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: 74,296B, BPFP=1.8845 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 42,892B, BPFP=0.6545 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 42,896B, BPFP=0.6545 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 40,188B, BPFP=1.2264 ⌛️ [2/4] FRONTEND: Frontend time: 0.294s (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: 0.456s [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 0.50815813 text_encoder-item0.clip_prompt_embeds 0.00023458 47.94688515 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00219611 0.52916799 text_encoder_2-item1.clip_prompt_embeds 0.00186620 0.09266527 text_encoder_3-item2.t5_prompt_embeds 0.00000775 0.00134513 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00588703 0.44190615 vae.encoder_f1 0.00589573 0.44176111 vae.decoder 0.00053402 0.02477038 ------------------------------------------------------------------------------------- TOTAL 0.00289910 3.04684522 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 329748 BPFP 1.1667 bits/point EBPFP 2.3335 equivalent bits/point MSE 3.046845 ---------------------- -------------------------------------------------------- Time: 0.759s Load: 0.009s, Pack+Encode: 0.294s, Decode+Unpack: 0.456s ---------------------- -------------------------------------------------------- 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 3.0468 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000467848.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 2,400B, BPFP=25.0000 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: 13,108B, BPFP=1.7733 Using 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: 3,664B, BPFP=22.9000 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: 22,472B, BPFP=1.8240 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: 71,604B, BPFP=1.8163 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 60,792B, BPFP=0.9276 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 60,788B, BPFP=0.9276 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 27,552B, BPFP=0.8408 ⌛️ [2/4] FRONTEND: Frontend time: 0.292s (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: 0.465s [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 0.48815882 text_encoder-item0.clip_prompt_embeds 0.00022882 107.85202753 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00110871 0.53379498 text_encoder_2-item1.clip_prompt_embeds 0.00019473 0.07884398 text_encoder_3-item2.t5_prompt_embeds 0.00000770 0.00120391 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00659691 0.98331571 vae.encoder_f1 0.00660300 0.98428726 vae.decoder 0.00023739 0.01918747 ------------------------------------------------------------------------------------- TOTAL 0.00311972 4.86373146 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 349444 BPFP 1.2364 bits/point EBPFP 2.4729 equivalent bits/point MSE 4.863731 ---------------------- -------------------------------------------------------- Time: 0.765s Load: 0.008s, Pack+Encode: 0.292s, Decode+Unpack: 0.465s ---------------------- -------------------------------------------------------- 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 4.8637 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468501.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 0.009s ------------------------------------------------------------ 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: 2,376B, BPFP=24.7500 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: 12,764B, BPFP=1.7267 Using 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: 3,792B, BPFP=23.7000 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: 21,832B, BPFP=1.7721 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: 70,080B, BPFP=1.7776 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 48,500B, BPFP=0.7401 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 48,496B, BPFP=0.7400 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 47,928B, BPFP=1.4626 ⌛️ [2/4] FRONTEND: Frontend time: 0.291s (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: 0.459s [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 0.51119224 text_encoder-item0.clip_prompt_embeds 0.00023928 47.66210515 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022734 0.52394419 text_encoder_2-item1.clip_prompt_embeds 0.00018899 0.07832755 text_encoder_3-item2.t5_prompt_embeds 0.00000832 0.00132360 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00583864 0.48258010 vae.encoder_f1 0.00583800 0.48257831 vae.decoder 0.00018889 0.02436447 ------------------------------------------------------------------------------------- TOTAL 0.00276073 3.05761633 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 342832 BPFP 1.2130 bits/point EBPFP 2.4261 equivalent bits/point MSE 3.057616 ---------------------- -------------------------------------------------------- Time: 0.759s Load: 0.009s, Pack+Encode: 0.291s, Decode+Unpack: 0.459s ---------------------- -------------------------------------------------------- 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 3.0576 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468632.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 2,400B, BPFP=25.0000 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: 13,184B, BPFP=1.7835 Using 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: 3,548B, BPFP=22.1750 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: 22,700B, BPFP=1.8425 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: 74,512B, BPFP=1.8900 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 40,372B, BPFP=0.6160 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 40,372B, BPFP=0.6160 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 41,332B, BPFP=1.2614 ⌛️ [2/4] FRONTEND: Frontend time: 0.295s (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: 0.455s [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 0.51040216 text_encoder-item0.clip_prompt_embeds 0.00024821 47.70893364 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00060829 0.43220539 text_encoder_2-item1.clip_prompt_embeds 0.00018297 0.08178535 text_encoder_3-item2.t5_prompt_embeds 0.00002546 0.00155382 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00570467 0.41767281 vae.encoder_f1 0.00570488 0.41757458 vae.decoder 0.00017302 0.02057355 ------------------------------------------------------------------------------------- TOTAL 0.00269931 3.02840795 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 325484 BPFP 1.1517 bits/point EBPFP 2.3033 equivalent bits/point MSE 3.028408 ---------------------- -------------------------------------------------------- Time: 0.759s Load: 0.009s, Pack+Encode: 0.295s, Decode+Unpack: 0.455s ---------------------- -------------------------------------------------------- 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 3.0284 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000471087.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 0.008s ------------------------------------------------------------ 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: 2,336B, BPFP=24.3333 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: 13,004B, BPFP=1.7592 Using 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: 3,612B, BPFP=22.5750 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: 22,220B, BPFP=1.8036 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: 71,656B, BPFP=1.8176 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 37,176B, BPFP=0.5673 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 37,172B, BPFP=0.5672 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 22,456B, BPFP=0.6853 ⌛️ [2/4] FRONTEND: Frontend time: 0.293s (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: 0.455s [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 0.53479866 text_encoder-item0.clip_prompt_embeds 0.00021458 167.97419508 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020115 0.45624304 text_encoder_2-item1.clip_prompt_embeds 0.00017334 0.08547989 text_encoder_3-item2.t5_prompt_embeds 0.00000867 0.00123943 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00914783 0.92597222 vae.encoder_f1 0.00914958 0.92545217 vae.decoder 0.00017527 0.01502343 ------------------------------------------------------------------------------------- TOTAL 0.00429285 6.40906363 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 296696 BPFP 1.0498 bits/point EBPFP 2.0996 equivalent bits/point MSE 6.409064 ---------------------- -------------------------------------------------------- Time: 0.755s Load: 0.008s, Pack+Encode: 0.293s, Decode+Unpack: 0.455s ---------------------- -------------------------------------------------------- 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 6.4091 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000482477.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 0.009s ------------------------------------------------------------ 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: 2,380B, BPFP=24.7917 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: 13,944B, BPFP=1.8864 Using 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: 3,760B, BPFP=23.5000 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: 23,820B, BPFP=1.9334 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: 76,192B, BPFP=1.9326 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 46,640B, BPFP=0.7117 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 46,640B, BPFP=0.7117 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 47,732B, BPFP=1.4567 ⌛️ [2/4] FRONTEND: Frontend time: 0.297s (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: 0.455s [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 0.47245594 text_encoder-item0.clip_prompt_embeds 0.00022150 23.73538116 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00048959 0.53012567 text_encoder_2-item1.clip_prompt_embeds 0.00016852 0.08811031 text_encoder_3-item2.t5_prompt_embeds 0.00000767 0.00131320 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00578482 0.46843576 vae.encoder_f1 0.00579739 0.46824351 vae.decoder 0.00017668 0.02516217 ------------------------------------------------------------------------------------- TOTAL 0.00273588 2.42571943 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 348172 BPFP 1.2319 bits/point EBPFP 2.4639 equivalent bits/point MSE 2.425719 ---------------------- -------------------------------------------------------- Time: 0.761s Load: 0.009s, Pack+Encode: 0.297s, Decode+Unpack: 0.455s ---------------------- -------------------------------------------------------- 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 2.4257 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499768.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 2,304B, BPFP=24.0000 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: 12,796B, BPFP=1.7311 Using 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: 3,668B, BPFP=22.9250 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: 21,252B, BPFP=1.7250 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: 68,260B, BPFP=1.7314 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 45,544B, BPFP=0.6949 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 45,544B, BPFP=0.6949 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 36,248B, BPFP=1.1062 ⌛️ [2/4] FRONTEND: Frontend time: 0.292s (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: 0.455s [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 0.52045540 text_encoder-item0.clip_prompt_embeds 0.00023894 36.64266268 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00033417 0.51887636 text_encoder_2-item1.clip_prompt_embeds 0.00016768 0.07464081 text_encoder_3-item2.t5_prompt_embeds 0.00000784 0.00118103 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00958025 0.96991515 vae.encoder_f1 0.00958229 0.96970177 vae.decoder 0.00019995 0.02154554 ------------------------------------------------------------------------------------- TOTAL 0.00449688 2.99485825 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 322680 BPFP 1.1417 bits/point EBPFP 2.2835 equivalent bits/point MSE 2.994858 ---------------------- -------------------------------------------------------- Time: 0.755s Load: 0.008s, Pack+Encode: 0.292s, Decode+Unpack: 0.455s ---------------------- -------------------------------------------------------- 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 2.9949 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499775.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 0.008s ------------------------------------------------------------ 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: 2,360B, BPFP=24.5833 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: 13,780B, BPFP=1.8642 Using 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: 3,700B, BPFP=23.1250 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: 24,132B, BPFP=1.9588 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: 75,452B, BPFP=1.9139 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 38,692B, BPFP=0.5904 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 38,692B, BPFP=0.5904 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 48,888B, BPFP=1.4919 ⌛️ [2/4] FRONTEND: Frontend time: 0.322s (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: 0.465s [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 0.51164599 text_encoder-item0.clip_prompt_embeds 0.00023387 168.10536729 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00060859 0.50804906 text_encoder_2-item1.clip_prompt_embeds 0.00021718 0.09367111 text_encoder_3-item2.t5_prompt_embeds 0.00000840 0.00140063 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00567713 0.43403217 vae.encoder_f1 0.00567905 0.43391028 vae.decoder 0.00019376 0.02838505 ------------------------------------------------------------------------------------- TOTAL 0.00268802 6.18639084 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 332760 BPFP 1.1774 bits/point EBPFP 2.3548 equivalent bits/point MSE 6.186391 ---------------------- -------------------------------------------------------- Time: 0.795s Load: 0.008s, Pack+Encode: 0.322s, Decode+Unpack: 0.465s ---------------------- -------------------------------------------------------- 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 6.1864 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000506454.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 2,404B, BPFP=25.0417 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: 13,228B, BPFP=1.7895 Using 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: 3,724B, BPFP=23.2750 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: 22,440B, BPFP=1.8214 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: 74,280B, BPFP=1.8841 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 46,004B, BPFP=0.7020 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 45,996B, BPFP=0.7018 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 31,420B, BPFP=0.9589 ⌛️ [2/4] FRONTEND: Frontend time: 0.306s (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: 0.455s [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 0.47169113 text_encoder-item0.clip_prompt_embeds 0.00024281 36.12568909 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020758 0.46726584 text_encoder_2-item1.clip_prompt_embeds 0.00017819 0.10866561 text_encoder_3-item2.t5_prompt_embeds 0.00000960 0.00131522 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.02387581 1.12059629 vae.encoder_f1 0.02387858 1.11977577 vae.decoder 0.00018648 0.01889406 ------------------------------------------------------------------------------------- TOTAL 0.01112583 3.05222590 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 326560 BPFP 1.1555 bits/point EBPFP 2.3109 equivalent bits/point MSE 3.052226 ---------------------- -------------------------------------------------------- Time: 0.770s Load: 0.008s, Pack+Encode: 0.306s, Decode+Unpack: 0.455s ---------------------- -------------------------------------------------------- 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 3.0522 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000515828.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 2,372B, BPFP=24.7083 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: 12,584B, BPFP=1.7024 Using 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: 3,552B, BPFP=22.2000 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: 22,060B, BPFP=1.7906 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: 69,864B, BPFP=1.7721 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 68,584B, BPFP=1.0465 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 68,580B, BPFP=1.0464 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 24,640B, BPFP=0.7520 ⌛️ [2/4] FRONTEND: Frontend time: 0.293s (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: 0.461s [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 0.48981909 text_encoder-item0.clip_prompt_embeds 0.00022399 47.85840267 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00031391 0.48056188 text_encoder_2-item1.clip_prompt_embeds 0.00020480 0.08213598 text_encoder_3-item2.t5_prompt_embeds 0.00000727 0.00119573 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.01169517 1.40762591 vae.encoder_f1 0.01169969 1.40783429 vae.decoder 0.00021186 0.01627092 ------------------------------------------------------------------------------------- TOTAL 0.00548058 3.49098394 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 359300 BPFP 1.2713 bits/point EBPFP 2.5426 equivalent bits/point MSE 3.490984 ---------------------- -------------------------------------------------------- Time: 0.763s Load: 0.009s, Pack+Encode: 0.293s, Decode+Unpack: 0.461s ---------------------- -------------------------------------------------------- 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 3.4910 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000517056.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 2,384B, BPFP=24.8333 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: 12,808B, BPFP=1.7327 Using 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: 3,656B, BPFP=22.8500 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: 21,944B, BPFP=1.7812 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: 75,712B, BPFP=1.9205 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 54,376B, BPFP=0.8297 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 54,372B, BPFP=0.8297 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 38,240B, BPFP=1.1670 ⌛️ [2/4] FRONTEND: Frontend time: 0.302s (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: 0.462s [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 0.45594700 text_encoder-item0.clip_prompt_embeds 0.00022123 155.84077381 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020346 0.47971334 text_encoder_2-item1.clip_prompt_embeds 0.00016509 0.07928349 text_encoder_3-item2.t5_prompt_embeds 0.00000793 0.00140991 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.32749966 3.91943169 vae.encoder_f1 0.32750070 3.92059422 vae.decoder 0.00039956 0.02569227 ------------------------------------------------------------------------------------- TOTAL 0.15195981 7.48135368 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 350556 BPFP 1.2404 bits/point EBPFP 2.4807 equivalent bits/point MSE 7.481354 ---------------------- -------------------------------------------------------- Time: 0.773s Load: 0.008s, Pack+Encode: 0.302s, Decode+Unpack: 0.462s ---------------------- -------------------------------------------------------- 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 7.4814 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000523100.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 0.009s ------------------------------------------------------------ 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: 2,364B, BPFP=24.6250 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: 12,844B, BPFP=1.7376 Using 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: 3,664B, BPFP=22.9000 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: 22,448B, BPFP=1.8221 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: 67,832B, BPFP=1.7206 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 47,140B, BPFP=0.7193 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 47,136B, BPFP=0.7192 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 38,412B, BPFP=1.1722 ⌛️ [2/4] FRONTEND: Frontend time: 0.297s (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: 0.459s [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 0.46559858 text_encoder-item0.clip_prompt_embeds 0.00024675 35.78797179 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00084628 0.46580000 text_encoder_2-item1.clip_prompt_embeds 0.00016730 0.08201320 text_encoder_3-item2.t5_prompt_embeds 0.00000841 0.00120209 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00566967 0.50423276 vae.encoder_f1 0.00567867 0.50439566 vae.decoder 0.00017839 0.02193683 ------------------------------------------------------------------------------------- TOTAL 0.00268303 2.75694351 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 328904 BPFP 1.1638 bits/point EBPFP 2.3275 equivalent bits/point MSE 2.756944 ---------------------- -------------------------------------------------------- Time: 0.765s Load: 0.009s, Pack+Encode: 0.297s, Decode+Unpack: 0.459s ---------------------- -------------------------------------------------------- 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 2.7569 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000526751.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 0.009s ------------------------------------------------------------ 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: 2,408B, BPFP=25.0833 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: 11,452B, BPFP=1.5492 Using 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: 3,828B, BPFP=23.9250 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: 20,456B, BPFP=1.6604 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: 70,032B, BPFP=1.7764 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 45,660B, BPFP=0.6967 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 45,660B, BPFP=0.6967 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 50,948B, BPFP=1.5548 ⌛️ [2/4] FRONTEND: Frontend time: 0.297s (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: 0.465s [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 0.48592083 text_encoder-item0.clip_prompt_embeds 0.00022364 107.92449608 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00036756 0.54190516 text_encoder_2-item1.clip_prompt_embeds 0.00015289 0.07518586 text_encoder_3-item2.t5_prompt_embeds 0.00000784 0.00119073 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00580750 0.47623336 vae.encoder_f1 0.00580664 0.47622082 vae.decoder 0.00018044 0.02692396 ------------------------------------------------------------------------------------- TOTAL 0.00274301 4.63096956 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 337508 BPFP 1.1942 bits/point EBPFP 2.3884 equivalent bits/point MSE 4.630970 ---------------------- -------------------------------------------------------- Time: 0.771s Load: 0.009s, Pack+Encode: 0.297s, Decode+Unpack: 0.465s ---------------------- -------------------------------------------------------- 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 4.6310 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000535578.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 0.008s ------------------------------------------------------------ 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: 2,380B, BPFP=24.7917 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: 12,632B, BPFP=1.7089 Using 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: 3,588B, BPFP=22.4250 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: 21,372B, BPFP=1.7347 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: 68,360B, BPFP=1.7340 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 50,472B, BPFP=0.7701 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 50,472B, BPFP=0.7701 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 31,064B, BPFP=0.9480 ⌛️ [2/4] FRONTEND: Frontend time: 0.295s (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: 0.459s [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 0.49800670 text_encoder-item0.clip_prompt_embeds 0.00030118 179.62229437 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020381 0.46345181 text_encoder_2-item1.clip_prompt_embeds 0.00019649 0.07617334 text_encoder_3-item2.t5_prompt_embeds 0.00000770 0.00126489 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.03869025 1.40097237 vae.encoder_f1 0.03869358 1.39951265 vae.decoder 0.00021614 0.01955744 ------------------------------------------------------------------------------------- TOTAL 0.01800198 6.93390556 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 327404 BPFP 1.1584 bits/point EBPFP 2.3169 equivalent bits/point MSE 6.933906 ---------------------- -------------------------------------------------------- Time: 0.762s Load: 0.008s, Pack+Encode: 0.295s, Decode+Unpack: 0.459s ---------------------- -------------------------------------------------------- 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 6.9339 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000546325.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 2,292B, BPFP=23.8750 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: 12,120B, BPFP=1.6396 Using 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: 3,808B, BPFP=23.8000 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: 21,352B, BPFP=1.7331 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: 67,904B, BPFP=1.7224 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 68,412B, BPFP=1.0439 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 68,408B, BPFP=1.0438 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 30,020B, BPFP=0.9161 ⌛️ [2/4] FRONTEND: Frontend time: 0.295s (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: 0.459s [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 0.53400755 text_encoder-item0.clip_prompt_embeds 0.00023260 156.50924986 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00026409 0.52001972 text_encoder_2-item1.clip_prompt_embeds 0.00016683 0.08282731 text_encoder_3-item2.t5_prompt_embeds 0.00000828 0.00123678 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00839879 1.36940098 vae.encoder_f1 0.00840224 1.37028241 vae.decoder 0.00019463 0.01890228 ------------------------------------------------------------------------------------- TOTAL 0.00394849 6.31554190 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 361380 BPFP 1.2787 bits/point EBPFP 2.5573 equivalent bits/point MSE 6.315542 ---------------------- -------------------------------------------------------- Time: 0.763s Load: 0.008s, Pack+Encode: 0.295s, Decode+Unpack: 0.459s ---------------------- -------------------------------------------------------- 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 6.3155 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000551780.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 2,288B, BPFP=23.8333 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: 14,128B, BPFP=1.9113 Using 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: 3,760B, BPFP=23.5000 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: 23,604B, BPFP=1.9159 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: 71,176B, BPFP=1.8054 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 62,484B, BPFP=0.9534 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 62,484B, BPFP=0.9534 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 27,760B, BPFP=0.8472 ⌛️ [2/4] FRONTEND: Frontend time: 0.293s (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: 0.459s [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 0.50109458 text_encoder-item0.clip_prompt_embeds 0.00023544 24.07707234 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022156 0.54571743 text_encoder_2-item1.clip_prompt_embeds 0.00018986 0.09217347 text_encoder_3-item2.t5_prompt_embeds 0.00000832 0.00152231 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.01160815 1.23222768 vae.encoder_f1 0.01161249 1.23235321 vae.decoder 0.00021720 0.01868590 ------------------------------------------------------------------------------------- TOTAL 0.00544054 2.78842633 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 354748 BPFP 1.2552 bits/point EBPFP 2.5104 equivalent bits/point MSE 2.788426 ---------------------- -------------------------------------------------------- Time: 0.761s Load: 0.009s, Pack+Encode: 0.293s, Decode+Unpack: 0.459s ---------------------- -------------------------------------------------------- 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 2.7884 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000555009.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 2,376B, BPFP=24.7500 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: 13,100B, BPFP=1.7722 Using 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: 3,656B, BPFP=22.8500 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: 21,516B, BPFP=1.7464 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: 71,020B, BPFP=1.8014 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 41,604B, BPFP=0.6348 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 41,604B, BPFP=0.6348 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 24,120B, BPFP=0.7361 ⌛️ [2/4] FRONTEND: Frontend time: 0.293s (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: 0.455s [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 0.45109256 text_encoder-item0.clip_prompt_embeds 0.00022923 84.55468750 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021530 0.49090524 text_encoder_2-item1.clip_prompt_embeds 0.00015521 0.07911858 text_encoder_3-item2.t5_prompt_embeds 0.00000740 0.00120127 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.02989292 1.27518702 vae.encoder_f1 0.02989391 1.27458394 vae.decoder 0.00034944 0.01871053 ------------------------------------------------------------------------------------- TOTAL 0.01393319 4.38930697 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 306060 BPFP 1.0829 bits/point EBPFP 2.1658 equivalent bits/point MSE 4.389307 ---------------------- -------------------------------------------------------- Time: 0.756s Load: 0.008s, Pack+Encode: 0.293s, Decode+Unpack: 0.455s ---------------------- -------------------------------------------------------- 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 4.3893 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000565469.zst to output-fixed/sd35/lambda0.02/hyperprior-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: 2,404B, BPFP=25.0417 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: 13,344B, BPFP=1.8052 Using 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: 3,844B, BPFP=24.0250 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: 21,808B, BPFP=1.7701 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: 71,352B, BPFP=1.8099 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: 2,600B, BPFP=27.0833 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: 10,312B, BPFP=1.3950 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: 4,528B, BPFP=28.3000 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: 19,440B, BPFP=1.5779 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: 50,184B, BPFP=1.2729 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 64,476B, BPFP=0.9838 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 64,476B, BPFP=0.9838 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 37,356B, BPFP=1.1400 ⌛️ [2/4] FRONTEND: Frontend time: 0.298s (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: 0.463s [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 0.47943628 text_encoder-item0.clip_prompt_embeds 0.00024627 155.74419981 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020424 0.52412682 text_encoder_2-item1.clip_prompt_embeds 0.00017521 0.07945030 text_encoder_3-item2.t5_prompt_embeds 0.00000803 0.00145136 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.56380248 text_encoder-item3.clip_prompt_embeds 0.00023247 59.84453210 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.33254869 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.33371209 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00119093 vae.encoder_f0 0.00613025 0.64620739 vae.encoder_f1 0.00613536 0.64596856 vae.decoder 0.00018697 0.02118693 ------------------------------------------------------------------------------------- TOTAL 0.00289634 5.96000960 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 366124 BPFP 1.2954 bits/point EBPFP 2.5909 equivalent bits/point MSE 5.960010 ---------------------- -------------------------------------------------------- Time: 0.769s Load: 0.008s, Pack+Encode: 0.298s, Decode+Unpack: 0.463s ---------------------- -------------------------------------------------------- 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 5.9600 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000575243.zst to output-fixed/sd35/lambda0.02/hyperprior-featurecoding-8bit-individual/sd35cond/000000575243.zst ------------------------ ---------------------------- TOTAL PROCESSING SUMMARY ------------------------ ---------------------------- Total files 100 Avg BPFP 1.2040 bits/point Avg EBPFP 2.4079 equivalent bits/point Avg MSE 4.305220 Avg Time 0.772s ------------------------ ----------------------------