Zengtudor/Wan2.1
1
0
Fork 0
mirror of https://github.com/Wan-Video/Wan2.1.git synced 2025-12-16 20:23:21 +00:00
Code Issues Actions Packages Projects Releases Wiki Activity

Add Wan2.1-related community project Video-As-Prompt (#561)

Browse Source 
Co-authored-by: Shiwei Zhang <134917139+Steven-SWZhang@users.noreply.github.com>
This commit is contained in:
Yuxuan BIAN 2025-12-16 00:18:50 +08:00 committed by GitHub
parent 854bd88e7f
commit ae487cc653
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
1 changed files with 1 additions and 0 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

1
README.md
View File

@ -36,6 +36,7 @@ In this repository, we present **Wan2.1**, a comprehensive and open suite of vid
## Community Works ## Community Works
If your work has improved **Wan2.1** and you would like more people to see it, please inform us. If your work has improved **Wan2.1** and you would like more people to see it, please inform us.
- [Video-As-Prompt](https://github.com/bytedance/Video-As-Prompt), the first unified semantic-controlled video generation model based on **Wan2.1-14B-I2V** with a Mixture-of-Transformers architecture and in-context controls (e.g., concept, style, motion, camera). Refer to the [project page](https://bytedance.github.io/Video-As-Prompt/) for more examples.
- [LightX2V](https://github.com/ModelTC/LightX2V), a lightweight and efficient video generation framework that integrates **Wan2.1** and **Wan2.2**, supports multiple engineering acceleration techniques for fast inference, which can run on RTX 5090 and RTX 4060 (8GB VRAM). - [LightX2V](https://github.com/ModelTC/LightX2V), a lightweight and efficient video generation framework that integrates **Wan2.1** and **Wan2.2**, supports multiple engineering acceleration techniques for fast inference, which can run on RTX 5090 and RTX 4060 (8GB VRAM).
- [DriVerse](https://github.com/shalfun/DriVerse), an autonomous driving world model based on **Wan2.1-14B-I2V**, generates future driving videos conditioned on any scene frame and given trajectory. Refer to the [project page](https://github.com/shalfun/DriVerse/tree/main) for more examples. - [DriVerse](https://github.com/shalfun/DriVerse), an autonomous driving world model based on **Wan2.1-14B-I2V**, generates future driving videos conditioned on any scene frame and given trajectory. Refer to the [project page](https://github.com/shalfun/DriVerse/tree/main) for more examples.
- [Training-Free-WAN-Editing](https://github.com/KyujinHan/Awesome-Training-Free-WAN2.1-Editing), built on **Wan2.1-T2V-1.3B**, allows training-free video editing with image-based training-free methods, such as [FlowEdit](https://arxiv.org/abs/2412.08629) and [FlowAlign](https://arxiv.org/abs/2505.23145). - [Training-Free-WAN-Editing](https://github.com/KyujinHan/Awesome-Training-Free-WAN2.1-Editing), built on **Wan2.1-T2V-1.3B**, allows training-free video editing with image-based training-free methods, such as [FlowEdit](https://arxiv.org/abs/2412.08629) and [FlowAlign](https://arxiv.org/abs/2505.23145).