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[ICCV 2023] StableVideo: Text-driven Consistency-aware Diffusion Video Editing

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StableVideo: Text-driven Consistency-aware Diffusion Video Editing

Installation

git clone https://github.com/rese1f/StableVideo.git
conda create -n stablevideo python=3.11
pip install -r requirements.txt

Download Pretrained Model

All models and detectors can be downloaded from ControlNet Hugging Face page. Download Link

Download example videos

Download the example atlas pretrained by Text2Live.

You can also train on your own video following NLA.

And it will create a folder data:

StableVideo
├── ...
├── ckpt
│   ├── cldm_v15.yaml
|   ├── dpt_hybrid-midas-501f0c75.pt
│   ├── control_sd15_canny.pth
│   └── control_sd15_depth.pth
├── data
│   └── car-turn
│       ├── checkpoint # NLA models are stored here
│       ├── car-turn # contains video frames
│       ├── ...
│   ├── blackswan
│   ├── ...
└── ...

Run and Play!

Run the following command to start. We provide some prompt template to help you achieve better result.

python app.py

the result .mp4 video and keyframe will be stored in the directory ./log after clicking render button.

Acknowledgement

This implementation is built partly on Text2LIVE and ControlNet.

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