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ControlVideo: Adding Conditional Control for One Shot Text-to-Video Editing

This is the official implementation for "ControlVideo: Adding Conditional Control for One Shot Text-to-Video Editing". The project page is available here. Code will be released soon.

Overview

ControlVideo incorporates visual conditions for all frames to amplify the source video's guidance, key-frame attention that aligns all frames with a selected one and temporal attention modules succeeded by a zero convolutional layer for temporal consistency and faithfulness. The three key components and corresponding fine-tuned parameters are designed by a systematic empirical study. Built upon the trained ControlVideo, during inference, we employ DDIM inversion and then generate the edited video using the target prompt via DDIM sampling.

Main Results

To Do List

Environment

conda env create -f environment.yml

The environment is similar to Tune-A-Video

Prepare Pretrained Text-to-Image Diffusion Model

Download the Stable Diffusion 1.5 and ControlNet 1.0 for canny, HED, depth and pose. Put them in ./ .

Quick Start

python main.py --control_type hed --video_path videos/car10.mp4 --source 'a car' --target 'a red car' --out_root outputs/ --max_step 300

The control_type is the type of controls, which is chosen from canny/hed/depth/pose. The video_path is the path to the input video. The source is the source prompt for the source video. The target is the target prompt. The max_step is the step for training. The out_root is the path for saving results.

Run More Demos

Download the data and put them in videos/.

python run_demos.py

References

If you find this repository helpful, please cite as:

@article{zhao2023controlvideo,
  title={ControlVideo: Adding Conditional Control for One Shot Text-to-Video Editing},
  author={Zhao, Min and Wang, Rongzhen and Bao, Fan and Li, Chongxuan and Zhu, Jun},
  journal={arXiv preprint arXiv:2305.17098},
  year={2023}
}

This implementation is based on Tune-A-Video and Video-p2p.