For distillation, we use the same data preprocessing pipeline as training. Please refer to the Training Data Preprocess for general preprocessing steps.
For Wan2.1 T2V distillation, we use the FastVideo 480P Synthetic Wan dataset (FastVideo/Wan-Syn_77x448x832_600k) which contains 600k synthetic latents.
# Download the preprocessed dataset
python examples/huggingface/download_hf.py \
--repo_id "FastVideo/Wan-Syn_77x448x832_600k" \
--local_dir "FastVideo/Wan-Syn_77x448x832_600k" \
--repo_type "dataset"For Wan2.2 TI2V distillation, we use the crush_smol dataset which includes both raw videos and preprocessed latents.
# Download dataset
python examples/huggingface/download_hf.py \
--repo_id=FastVideo/mini_i2v_dataset \
--local_dir=data/mini_i2v_dataset \
--repo_type=datasetThe preprocessing steps are identical to training. Run the appropriate preprocessing script based on your model:
# For Wan2.1 T2V
bash examples/preprocessing/v1_preprocess_wan_data_t2v
# For Wan2.2 TI2V
bash examples/distill/Wan2.2-TI2V-5B-Diffusers/crush_smol/preprocess_wan_data_ti2v_5b.sh