# Wan2.2: Open and Advanced Large-Scale Video Generative Models

This repository profile is provided by osrepos.com, an open source repository discovery platform.

Source: osrepos.com
Repository profile: https://osrepos.com/repo/wan-video-wan2.2
Generated for open source discovery and AI-assisted research.

Wan2.2 is an open-source and advanced suite of large-scale video generative models, introducing innovations like a Mixture-of-Experts (MoE) architecture for enhanced capacity and cinematic-level aesthetics. It offers efficient high-definition video generation capabilities, including text-to-video, image-to-video, speech-to-video, and character animation. This powerful framework is designed for both industrial and academic applications, pushing the boundaries of AI-driven video creation.

GitHub: https://github.com/Wan-Video/Wan2.2
OSRepos URL: https://osrepos.com/repo/wan-video-wan2.2

## Summary

Wan2.2 is an open-source and advanced suite of large-scale video generative models, introducing innovations like a Mixture-of-Experts (MoE) architecture for enhanced capacity and cinematic-level aesthetics. It offers efficient high-definition video generation capabilities, including text-to-video, image-to-video, speech-to-video, and character animation. This powerful framework is designed for both industrial and academic applications, pushing the boundaries of AI-driven video creation.

## Topics

- aigc
- video-generation
- Python
- generative AI
- deep learning
- MoE
- video models

## Repository Information

Last analyzed by OSRepos: Wed Oct 29 2025 00:01:02 GMT+0000 (Western European Standard Time)
Detail views: 15
GitHub clicks: 17

## Safety Notice

OSRepos shares public repositories for knowledge and discovery only. Review source code, dependencies, licenses, and security implications before running or installing anything.

## Content

## Introduction

Wan2.2 is an open and advanced suite of large-scale video generative models, pushing the boundaries of AI-driven video creation. This major upgrade builds upon its predecessor, Wan2.1, by incorporating several key innovations designed to enhance generation quality, model capability, and computational efficiency. It introduces a Mixture-of-Experts (MoE) architecture, achieves cinematic-level aesthetics through meticulously curated data, and significantly improves complex motion generation with a larger training dataset. Furthermore, Wan2.2 offers an efficient high-definition hybrid Text-Image-to-Video (TI2V) model, capable of generating 720P videos at 24fps on consumer-grade GPUs.

## Installation

To get started with Wan2.2, follow these simple steps:

First, clone the repository:

sh
git clone https://github.com/Wan-Video/Wan2.2.git
cd Wan2.2


Next, install the required dependencies. Ensure you have `torch >= 2.4.0`. If `flash_attn` installation fails, try installing other packages first and then `flash_attn`.

sh
pip install -r requirements.txt
# If you want to use CosyVoice for Speech-to-Video Generation, install additional requirements:
pip install -r requirements_s2v.txt


## Examples

Wan2.2 supports various video generation tasks, including Text-to-Video (T2V), Image-to-Video (I2V), Text-Image-to-Video (TI2V), Speech-to-Video (S2V), and character animation with Wan-Animate.

You can download the models using `huggingface-cli` or `modelscope-cli`. For instance, to download the T2V-A14B model:

sh
pip install "huggingface_hub[cli]"
huggingface-cli download Wan-AI/Wan2.2-T2V-A14B --local-dir ./Wan2.2-T2V-A14B


Here's an example of running Text-to-Video generation without prompt extension on a single GPU:

sh
python generate.py --task t2v-A14B --size 1280*720 --ckpt_dir ./Wan2.2-T2V-A14B --offload_model True --convert_model_dtype --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage."


For multi-GPU inference or other generation tasks like Image-to-Video, Speech-to-Video, or Wan-Animate, please refer to the comprehensive documentation in the repository's README.

## Why Use Wan2.2?

Wan2.2 stands out for its cutting-edge features and performance:

*   **Mixture-of-Experts (MoE) Architecture:** Enhances model capacity and generation quality while maintaining efficient computational costs.
*   **Cinematic-level Aesthetics:** Generates videos with precise and controllable cinematic styles, thanks to meticulously curated aesthetic data.
*   **Complex Motion Generation:** Trained on significantly larger datasets, leading to superior generalization across motions, semantics, and aesthetics.
*   **Efficient High-Definition TI2V:** The 5B model, powered by an advanced Wan2.2-VAE, supports 720P video generation at 24fps, even on consumer-grade GPUs like the RTX 4090.
*   **Versatile Applications:** Supports Text-to-Video, Image-to-Video, Speech-to-Video, and Character Animation, making it suitable for a wide range of creative and industrial uses.
*   **Open-Source:** Provides an accessible and powerful tool for researchers and developers in the generative AI community.

## Links

*   **GitHub Repository:** [Wan-Video/Wan2.2](https://github.com/Wan-Video/Wan2.2){:target="_blank"}
*   **Official Website:** [Wan.video](https://wan.video){:target="_blank"}
*   **Research Paper:** [arXiv:2503.20314](https://arxiv.org/abs/2503.20314){:target="_blank"}
*   **Hugging Face:** [Wan-AI](https://huggingface.co/Wan-AI/){:target="_blank"}
*   **Discord Community:** [Join Discord](https://discord.gg/AKNgpMK4Yj){:target="_blank"}