# Video-P2P: Video Editing with Cross-attention Control The official implementation of [Video-P2P](https://video-p2p.github.io/). [Shaoteng Liu](https://www.shaotengliu.com/), [Yuechen Zhang](https://julianjuaner.github.io/), [Wenbo Li](https://fenglinglwb.github.io/), [Zhe Lin](https://sites.google.com/site/zhelin625/), [Jiaya Jia](https://jiaya.me/) [![Project Website](https://img.shields.io/badge/Project-Website-orange)](https://video-p2p.github.io/) [![arXiv](https://img.shields.io/badge/arXiv-2303.04761-b31b1b.svg)](https://arxiv.org/abs/2303.04761) ![Teaser](./docs/teaser.png) ## Changelog - 2023.03.20 Release Gradio Demo. - 2023.03.19 Release Code. - 2023.03.09 Paper preprint on arxiv. ## Todo - [x] Release the code with 6 examples. - [x] Update a faster version. - [x] Release all data. - [ ] Release the Gradio Demo. - [ ] Release more configs and new applications. ## Setup ``` bash pip install -r requirements.txt ``` The code was tested on both Tesla V100 32GB and RTX3090 24GB. The environment is similar to [Tune-A-video](https://github.com/showlab/Tune-A-Video) and [prompt-to-prompt](https://github.com/google/prompt-to-prompt/). [xformers](https://github.com/facebookresearch/xformers) on 3090 may meet this [issue](https://github.com/bryandlee/Tune-A-Video/issues/4). ## Quickstart Please replace ``pretrained_model_path'' with the path to your stable-diffusion. ``` bash # You can minimize the tuning epochs to speed up. python run_tuning.py --config="configs/rabbit-jump-tune.yaml" # Tuning to do model initialization. # We develop a faster mode (1 min on V100): python run_videop2p.py --config="configs/rabbit-jump-p2p.yaml" --fast # The official mode (10 mins on V100, more stable): python run_videop2p.py --config="configs/rabbit-jump-p2p.yaml" ``` ## Dataset We release our dataset [here](). Download them under ./data and explore your creativity! ## Results
configs/rabbit-jump-p2p.yaml configs/penguin-run-p2p.yaml
configs/man-motor-p2p.yaml configs/car-drive-p2p.yaml
configs/tiger-forest-p2p.yaml configs/bird-forest-p2p.yaml
## Citation ``` @misc{liu2023videop2p, author={Liu, Shaoteng and Zhang, Yuechen and Li, Wenbo and Lin, Zhe and Jia, Jiaya}, title={Video-P2P: Video Editing with Cross-attention Control}, journal={arXiv:2303.04761}, year={2023}, } ``` ## References * prompt-to-prompt: https://github.com/google/prompt-to-prompt * Tune-A-Video: https://github.com/showlab/Tune-A-Video * diffusers: https://github.com/huggingface/diffusers