dvlab-research/Video-P2P dvlab-research/Video-P2P

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Video-P2P: Video Editing with Cross-attention Control

Video-P2P: Video Editing with Cross-attention Control

The official implementation of Video-P2P.

Shaoteng Liu, Yuechen Zhang, Wenbo Li, Zhe Lin, Jiaya Jia

Project Website arXiv Hugging Face Demo

Teaser

Changelog

  • 2023.03.20 Release Demo.
  • 2023.03.19 Release Code.
  • 2023.03.09 Paper preprint on arxiv.

Todo

  • Release the code with 6 examples.
  • Update a faster version.
  • Release data.
  • Release the Gradio Demo.
  • Add local Gradio Demo.
  • Release more configs and new applications.

Setup

pip install -r requirements.txt

The code was tested on both Tesla V100 32GB and RTX3090 24GB. At least 20GB VRAM is required.

The environment is similar to Tune-A-Video and prompt-to-prompt.

xformers on 3090 may meet this issue.

Quickstart

Please replace pretrained_model_path with the path to your stable-diffusion.

To download the pre-trained model, please refer to diffusers.

# Stage 1: Tuning to do model initialization.

# You can minimize the tuning epochs to speed up.
python run_tuning.py  --config="configs/rabbit-jump-tune.yaml"
# Stage 2: Attention Control

# 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"

Find your results in Video-P2P/outputs/xxx/results.

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

Gradio demo

Running the following command to launch the local demo built with gradio:

python app_gradio.py

Find the demo on HuggingFace here. The demo code borrows heavily from Tune-A-Video.

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},
}

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