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ConditionVideo

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Training-Free Condition-Guided Text-to-Video Generation

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# ConditionVideo: Training-Free Condition-Guided Text-to-Video Generation (AAAI 2024)
[Bo Peng](https://pengbo807.github.io/), [Xinyuan Chen](https://scholar.google.com/citations?user=3fWSC8YAAAAJ&hl=zh-CN), [Yaohui Wang](https://wyhsirius.github.io/), [Chaochao Lu](https://causallu.com/), [Yu Qiao](https://scholar.google.com/citations?user=gFtI-8QAAAAJ&hl=en)

### [Project Page](https://pengbo807.github.io/conditionvideo-website/) | [Paper](https://arxiv.org/abs/2310.07697)
This is the official PyTorch implementation of paper "ConditionVideo: Training-Free Condition-Guided Text-to-Video Generation"

Our model generates realistic dynamic videos from random noise or given scene videos based on given conditions. Currently, we support openpose keypoint, canny, depth and segment condition.

|pose|customized pose|
|:-:|:-:|
|<img src="videos/62-53-The Astronaut, brown background.gif" width="200"> The Astronaut, brown background|<img src="videos/1-2-18-ironman in the sea.gif" width="300"> Ironman in the sea|
## Setup
To install the environments, use:
```
conda create -n tune-control python=3.10
```
check cuda version then install the corresponding pytorch package, note that we need pytorch==2.0.0
```
pip install -r requirements.txt
conda install xformers -c xformers
```
You may also need to download model checkpoints manually from hugging-face.
## Usage
To run the code, use

```
accelerate launch --num_processes 1 conditionvideo.py --config="configs//config.yaml"
```
for video generation, change the configuration in `config.yaml` for different generation settings.
## Citation
```
@misc{peng2023conditionvideo,
      title={ConditionVideo: Training-Free Condition-Guided Text-to-Video Generation}, 
      author={Bo Peng and Xinyuan Chen and Yaohui Wang and Chaochao Lu and Yu Qiao},
      year={2023},
      eprint={2310.07697},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
```

ConditionVideo

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