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Author SHA1 Message Date
DefTruth
6e0192a31c
Merge ae832d4ff7 into ae487cc653 2025-12-16 01:22:36 +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
DefTruth
ae832d4ff7 docs: add cache-dit to community works 2025-10-17 02:12:59 +00:00
yanjun.qiu
f500fcd8d2 docs: add cache-dit to community works 2025-10-17 02:08:27 +00:00
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@ -36,6 +36,7 @@ In this repository, we present **Wan2.1**, a comprehensive and open suite of vid
## Community Works
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).
- [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).
@ -50,7 +51,7 @@ If your work has improved **Wan2.1** and you would like more people to see it, p
- [CFG-Zero](https://github.com/WeichenFan/CFG-Zero-star) enhances **Wan2.1** (covering both T2V and I2V models) from the perspective of CFG.
- [TeaCache](https://github.com/ali-vilab/TeaCache) now supports **Wan2.1** acceleration, capable of increasing speed by approximately 2x. Feel free to give it a try!
- [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio) provides more support for **Wan2.1**, including video-to-video, FP8 quantization, VRAM optimization, LoRA training, and more. Please refer to [their examples](https://github.com/modelscope/DiffSynth-Studio/tree/main/examples/wanvideo).
- [CacheDiT](https://github.com/vipshop/cache-dit) offers Fully Cache Acceleration support for Wan2.1 with DBCache, TaylorSeer and Cache CFG. Visit their [example](https://github.com/vipshop/cache-dit/blob/main/examples/pipeline/run_wan.py) for more details.
## 📑 Todo List
- Wan2.1 Text-to-Video