44 lines
1.4 KiB
Markdown
44 lines
1.4 KiB
Markdown
(v0-data-preprocess)=
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# 🧱 Data Preprocess for Distillation
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For distillation, we use the same data preprocessing pipeline as training. Please refer to the [Training Data Preprocess](../training/data_preprocess.md) for general preprocessing steps.
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## Distillation-Specific Datasets
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### FastVideo 480P Synthetic Wan Dataset
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For Wan2.1 T2V distillation, we use the **FastVideo 480P Synthetic Wan dataset** ([FastVideo/Wan-Syn_77x448x832_600k](https://huggingface.co/datasets/FastVideo/Wan-Syn_77x448x832_600k)) which contains 600k synthetic latents.
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```bash
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# Download the preprocessed dataset
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python scripts/huggingface/download_hf.py \
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--repo_id "FastVideo/Wan-Syn_77x448x832_600k" \
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--local_dir "FastVideo/Wan-Syn_77x448x832_600k" \
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--repo_type "dataset"
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```
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### Crush Smol Dataset
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For Wan2.2 TI2V distillation, we use the crush_smol dataset which includes both raw videos and preprocessed latents.
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```bash
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# Download dataset
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python scripts/huggingface/download_hf.py \
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--repo_id=FastVideo/mini_i2v_dataset \
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--local_dir=data/mini_i2v_dataset \
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--repo_type=dataset
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```
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## Preprocessing for Distillation
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The preprocessing steps are identical to training. Run the appropriate preprocessing script based on your model:
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```bash
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# For Wan2.1 T2V
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bash scripts/preprocess/v1_preprocess_wan_data_t2v
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# For Wan2.2 TI2V
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bash examples/distill/Wan2.2-TI2V-5B-Diffusers/crush_smol/preprocess_wan_data_ti2v_5b.sh
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```
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