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Author SHA1 Message Date
Didier Durand
750a197f43
Merge e8820d0ac8 into ae487cc653 2026-01-08 00:12:44 -08:00
Yuxuan BIAN
ae487cc653
Add Wan2.1-related community project Video-As-Prompt (#561) 
Co-authored-by: Shiwei Zhang <134917139+Steven-SWZhang@users.noreply.github.com>
 2025-12-16 00:18:50 +08:00
Didier Durand
e8820d0ac8 Adding env variables for parametrized Gradio login 2025-03-01 05:58:29 +01:00
2 changed files with 6 additions and 1 deletions
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1
README.md
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@ -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).

6
gradio/t2v_1.3B_singleGPU.py
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@ -205,4 +205,8 @@ if __name__ == '__main__':
print("done", flush=True) print("done", flush=True)
demo = gradio_interface() demo = gradio_interface()
demo.launch(server_name="0.0.0.0", share=False, server_port=7860) if "WAN_USER" in os.environ and "WAN_PWD" in os.environ:
demo.launch(server_name="0.0.0.0", share=False, server_port=7860,
auth=(os.environ["WAN_USER"], os.environ["WAN_PWD"]))
else:
demo.launch(server_name="0.0.0.0", share=False, server_port=7860)