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🧱 Data Preprocess

To save GPU memory, we precompute text embeddings and VAE latents to eliminate the need to load the text encoder and VAE during training.

We provide a sample dataset to help you get started. Download the source media using the following command:

python scripts/huggingface/download_hf.py --repo_id=FastVideo/Image-Vid-Finetune-Src --local_dir=data/Image-Vid-Finetune-Src --repo_type=dataset

To preprocess the dataset for fine-tuning or distillation, run:

bash scripts/preprocess/preprocess_mochi_data.sh # for mochi
bash scripts/preprocess/preprocess_hunyuan_data.sh # for hunyuan

The preprocessed dataset will be stored in Image-Vid-Finetune-Mochi or Image-Vid-Finetune-HunYuan correspondingly.

Process your own dataset

If you wish to create your own dataset for finetuning or distillation, please structure you video dataset in the following format:

path_to_dataset_folder/ ├── media/ │ ├── 0.jpg │ ├── 1.mp4 │ ├── 2.jpg ├── video2caption.json └── merge.txt

Format the JSON file as a list, where each item represents a media source:

For image media,

{
    "path": "0.jpg",
    "cap": ["captions"]
}

For video media,

{
    "path": "1.mp4",
    "resolution": {
      "width": 848,
      "height": 480
    },
    "fps": 30.0,
    "duration": 6.033333333333333,
    "cap": [
      "caption"
    ]
  }

Use a txt file (merge.txt) to contain the source folder for media and the JSON file for meta information:

path_to_media_source_foder,path_to_json_file

Adjust the DATA_MERGE_PATH and OUTPUT_DIR in scripts/preprocess/preprocess_****_data.sh accordingly and run:

bash scripts/preprocess/preprocess_****_data.sh

The preprocessed data will be put into the OUTPUT_DIR and the videos2caption.json can be used in finetune and distill scripts.