ExVideo

ExVideo is a post-tuning technique aimed at enhancing the capability of video generation models. We have extended Stable Video Diffusion to achieve the generation of long videos up to 128 frames.

Project Page
Technical report
Demo
Extended models
- HuggingFace
- ModelScope

Example: Text-to-video via extended Stable Video Diffusion

Generate a video using a text-to-image model and our image-to-video model. See ExVideo_svd_test.py.

https://github.com/modelscope/DiffSynth-Studio/assets/35051019/d97f6aa9-8064-4b5b-9d49-ed6001bb9acc

Train

Step 1: Install additional packages

pip install lightning deepspeed

Step 2: Download base model (from HuggingFace or ModelScope) to models/stable_video_diffusion/svd_xt.safetensors.
Step 3: Prepare datasets

path/to/your/dataset
├── metadata.json
└── videos
    ├── video_1.mp4
    ├── video_2.mp4
    └── video_3.mp4

where the metadata.json is

[
    {
        "path": "videos/video_1.mp4"
    },
    {
        "path": "videos/video_2.mp4"
    },
    {
        "path": "videos/video_3.mp4"
    }
]

Step 4: Run

CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7" python -u ExVideo_svd_train.py \
  --pretrained_path "models/stable_video_diffusion/svd_xt.safetensors" \
  --dataset_path "path/to/your/dataset" \
  --output_path "path/to/save/models" \
  --steps_per_epoch 8000 \
  --num_frames 128 \
  --height 512 \
  --width 512 \
  --dataloader_num_workers 2 \
  --learning_rate 1e-5 \
  --max_epochs 100

Step 5: Post-process checkpoints

Calculate Exponential Moving Average (EMA) and package it using safetensors.

python ExVideo_ema.py --output_path "path/to/save/models/lightning_logs/version_xx" --gamma 0.9

Step 6: Enjoy your model

The EMA model is at path/to/save/models/lightning_logs/version_xx/checkpoints/epoch=xx-step=yyy-ema.safetensors. Load it in ExVideo_svd_test.py and then enjoy your model.