# 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/) [](https://video-p2p.github.io/) [](https://arxiv.org/abs/2303.04761)  ## 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 |
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configs/man-motor-p2p.yaml | configs/car-drive-p2p.yaml |
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configs/tiger-forest-p2p.yaml | configs/bird-forest-p2p.yaml |
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