VideoGenHub

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A one-stop library to standardize the inference and evaluation of all the conditional video generation models.

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# VideoGenHub

[![contributors](https://img.shields.io/github/contributors/TIGER-AI-Lab/VideoGenHub)](https://github.com/TIGER-AI-Lab/VideoGenHub/graphs/contributors)
[![license](https://img.shields.io/github/license/TIGER-AI-Lab/VideoGenHub.svg)](https://github.com/TIGER-AI-Lab/VidenGenHub/blob/main/LICENSE)
[![GitHub](https://img.shields.io/github/stars/TIGER-AI-Lab/VideoGenHub?style=social)](https://github.com/TIGER-AI-Lab/VideoGenHub)
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VideoGenHub is a one-stop library to standardize the inference and evaluation of all the conditional video generation models.
![DALL·E 2025-02-13 03 26 01 - A highly detailed and ultra-high-quality image of a person sitting at a desk watching videos on a computer  The screen displays a video player with a ](https://github.com/user-attachments/assets/88d7268c-56f9-48bf-a8e4-19b223516b76)

* We define 2 prominent generation tasks (Text-to-Video and Image-to-Video).
* We built a unified inference pipeline to ensure fair comparison. We currently support around 10 models.

## 📰 News
* 2024 Jun 10: [GenAI-Arena](https://arxiv.org/abs/2406.04485v1) Paper is out. It is featured on [Huggingface Daily Papers](https://huggingface.co/papers?date=2024-06-10).

## 📄 Table of Contents

- [🛠️ Installation](#%EF%B8%8F-installation-)
- [👨‍🏫 Get Started](#-get-started-)
- [🎫 License](#-license-)
- [🖊️ Citation](#%EF%B8%8F-citation-)

## 🛠️ Installation [🔝](#-table-of-contents)
To install from pypi:
```commandline
pip install videogen-hub
```

To install from github:
```python
git clone https://github.com/TIGER-AI-Lab/VideoGenHub.git
cd VideoGenHub
cd env_cfg
pip install -r requirements.txt
cd ..
pip install -e .
```
The requirement of opensora is in `env_cfg/opensora.txt`

For some models like show one, you need to login through `huggingface-cli`.

```shell
huggingface-cli login
```

## 👨‍🏫 Get Started [🔝](#-table-of-contents)

### Benchmarking
To reproduce our experiment using benchmark.

For text-to-video generation: 
```commandline
./t2v_inference.sh --<model_name> --<device>
```

### Infering one model
```python
import videogen_hub

model = videogen_hub.load('VideoCrafter2')
video = model.infer_one_video(prompt="A child excitedly swings on a rusty swing set, laughter filling the air.")

# Here video is a torch tensor of shape torch.Size([16, 3, 320, 512])
```
See Google Colab here: https://colab.research.google.com/drive/145UMsBOe5JLqZ2m0LKqvvqsyRJA1IeaE?usp=sharing

## 🧠 Philosophy [🔝](#-philosophy-)
By streamlining research and collaboration, VideoGenHub plays a pivotal role in propelling the field of Video Generation.

* Purity of Evaluation: We ensure a fair and consistent evaluation for all models, eliminating biases.
* Research Roadmap: By defining tasks and curating datasets, we provide clear direction for researchers. 
* Open Collaboration: Our platform fosters the exchange and cooperation of related technologies, bringing together minds and innovations.

### Implemented Models
We included more than 10 Models in video generation.

## TODO

* [ ] Add ComfyUI Support 
* [ ] Add Metrics Support
* [ ] Add Visualization Support (Similar to [ImagenHub](https://chromaica.github.io/#imagen-museum))
* [ ] Add Video Editing Task

## 🎫 License [🔝](#-table-of-contents)

This project is released under the [License](LICENSE).

## 🖊️ Citation [🔝](#-table-of-contents)

This work is a part of GenAI-Arena work.

Please kindly cite our paper if you use our code, data, models or results:

```bibtex
@misc{jiang2024genai,
      title={GenAI Arena: An Open Evaluation Platform for Generative Models}, 
      author={Dongfu Jiang and Max Ku and Tianle Li and Yuansheng Ni and Shizhuo Sun and Rongqi Fan and Wenhu Chen},
      year={2024},
      eprint={2406.04485},
      archivePrefix={arXiv},
      primaryClass={cs.AI}
}
```

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