🧱 Data Preprocessing

To save GPU memory during training, FastVideo precomputes text embeddings and VAE latents. This eliminates the need to load the text encoder and VAE during training.

Quick Start

Download the sample dataset and run preprocessing:

# Download the crush-smol dataset
python scripts/huggingface/download_hf.py \
    --repo_id "wlsaidhi/crush-smol-merged" \
    --local_dir "data/crush-smol" \
    --repo_type "dataset"

# Run preprocessing
bash examples/training/finetune/wan_t2v_1.3B/crush_smol/preprocess_wan_data_t2v_new.sh

Preprocessing Pipeline

The new preprocessing pipeline supports multiple dataset formats and video loaders:

GPU_NUM=2
MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
DATASET_PATH="data/crush-smol/"
OUTPUT_DIR="data/crush-smol_processed_t2v/"

torchrun --nproc_per_node=$GPU_NUM \
    -m fastvideo.pipelines.preprocess.v1_preprocessing_new \
    --model_path $MODEL_PATH \
    --mode preprocess \
    --workload_type t2v \
    --preprocess.video_loader_type torchvision \
    --preprocess.dataset_type merged \
    --preprocess.dataset_path $DATASET_PATH \
    --preprocess.dataset_output_dir $OUTPUT_DIR \
    --preprocess.preprocess_video_batch_size 2 \
    --preprocess.dataloader_num_workers 0 \
    --preprocess.max_height 480 \
    --preprocess.max_width 832 \
    --preprocess.num_frames 77 \
    --preprocess.train_fps 16 \
    --preprocess.samples_per_file 8 \
    --preprocess.flush_frequency 8 \
    --preprocess.video_length_tolerance_range 5

Key Parameters

Parameter	Description
--workload_type	Task type: t2v (text-to-video) or i2v (image-to-video)
--preprocess.dataset_type	Input format: hf (HuggingFace) or merged (local folder)
--preprocess.dataset_path	Path to dataset (HF repo ID or local folder)
--preprocess.dataset_output_dir	Output directory for Parquet files
--preprocess.video_loader_type	Video decoder: torchcodec or torchvision
--preprocess.max_height / max_width	Target resolution for videos
--preprocess.num_frames	Number of frames to extract per video
--preprocess.train_fps	Target FPS for frame extraction

Dataset Formats

Merged Dataset (Local Folder)

Structure your dataset as follows:

your_dataset/
├── videos/
│   ├── video_001.mp4
│   ├── video_002.mp4
│   └── ...
└── videos2caption.json

The videos2caption.json maps video filenames to captions:

[
  {"path": "video_001.mp4", "cap": "A cat playing with yarn..."},
  {"path": "video_002.mp4", "cap": "Ocean waves at sunset..."}
]

HuggingFace Dataset

Use --preprocess.dataset_type hf and point --preprocess.dataset_path to a HuggingFace dataset with video and caption columns.

Creating Your Own Dataset

If you have raw videos and captions in separate files, generate the videos2caption.json:

python scripts/dataset_preparation/prepare_json_file.py \
    --data_folder path/to/your_raw_data/ \
    --output path/to/output_folder

Your raw data folder should contain:

your_raw_data/
├── videos/
│   ├── 0.mp4
│   ├── 1.mp4
│   └── ...
├── videos.txt    # list of video filenames
└── prompt.txt    # corresponding captions (one per line)

Output Format

Preprocessing outputs Parquet files in the combined_parquet_dataset/ subdirectory containing:

• vae_latent_bytes — VAE-encoded video latent
• text_embedding_bytes — text encoder output
• clip_feature_bytes — CLIP image features (I2V only)
• first_frame_latent_bytes — first frame latent (I2V only)
• Metadata: shapes, dtypes, and sample identifiers

Examples

See ready-to-run preprocessing scripts in the training examples:

• T2V: examples/training/finetune/wan_t2v_1.3B/crush_smol/preprocess_wan_data_t2v_new.sh
• I2V: examples/training/finetune/wan_i2v_14B_480p/crush_smol/preprocess_wan_data_i2v_new.sh

→ Browse all training examples