# 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](https://ecnu-cilab.github.io/ExVideoProjectPage/) * [Technical report](https://arxiv.org/abs/2406.14130) * [Demo](https://huggingface.co/spaces/modelscope/ExVideo-SVD-128f-v1) * Extended models * [HuggingFace](https://huggingface.co/ECNU-CILab/ExVideo-SVD-128f-v1) * [ModelScope](https://modelscope.cn/models/ECNU-CILab/ExVideo-SVD-128f-v1) ## 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](./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](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt/resolve/main/svd_xt.safetensors) or [ModelScope](https://www.modelscope.cn/api/v1/models/AI-ModelScope/stable-video-diffusion-img2vid-xt/repo?Revision=master&FilePath=svd_xt.safetensors)) 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](./ExVideo_svd_test.py) and then enjoy your model.