cs-mshah/layergen-eval-latents
LayerGen — Eval-Set Latents (VAE latents + baked text embeddings) Pre-encoded evaluation-set inputs for the LayerGen layer-decomposition / harmonization models, so inference can run anywhere (off-AIP) without the raw video → VAE-encode → umT5-encode pipeline. Each *.parquet is one clip and is fully self-contained: column group contents {composite,mask,fg,bg}_latent_bytes (+ _shape, _dtype) 4-stream Wan-VAE latents, 81f/21 latent-T, fp16, [16,21,60,104]… See the full description on the dataset page: https://huggingface.co/datasets/cs-mshah/layergen-eval-latents.
LayerGen — Eval-Set Latents (VAE latents + baked text embeddings)
Pre-encoded evaluation-set inputs for the LayerGen layer-decomposition / harmonization models, so inference can run anywhere (off-AIP) without the raw video → VAE-encode → umT5-encode pipeline.
Each *.parquet is one clip and is fully self-contained:
Because the umT5 embedding is baked in, no text encoder is needed at inference — read with data_format: latents and the per-clip text_embedding comes straight from the parquet.
Pools (latest editcap captioning scheme)
Crop to 41f/61f at load via num_latent_t (11 / 16 / 21). Type-4 harmonize checkpoints require FASTVIDEO_FG_CORRUPT_TYPE_ID=4; combined checkpoints need COMBINED_INFER=1.
Off-AIP inference (recipe)
from huggingface_hub import snapshot_download
# pull one pool locally
snapshot_download("cs-mshah/layergen-eval-latents", repo_type="dataset",
allow_patterns="_val_latents_81f_editcap_dec_combined/**",
local_dir="evals")
# then point the inference config's validation.data_path at
# evals/_val_latents_81f_editcap_dec_combined/all (data_format: latents)DiT checkpoints are public under `cs-mshah`; decode uses the public Wan2.2-14B VAE. Companion output-viewer datasets: cs-mshah/layergen-evals (+ -baselines).
