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 ## Community Works
 If your research or project builds upon [**Wan2.1**](https://github.com/Wan-Video/Wan2.1) or [**Wan2.2**](https://github.com/Wan-Video/Wan2.2), and you would like more people to see it, please inform us.
 
-- [LightX2V](https://github.com/ModelTC/LightX2V), a lightweight and efficient video generation framework that integrates **Wan2.1** and **Wan2.2**, supporting multiple engineering acceleration techniques for fast inference. [LightX2V-HuggingFace](https://huggingface.co/lightx2v), offers a variety of Wan-based step-distillation models, quantized models, and lightweight VAE models. Combined with step-distillation models, LightX2V running with multi-GPU parallelism on RTX 5090 can generate a 5-second video in under 5 seconds. With offloading techniques, LightX2V can also run inference on an RTX 4060 (8GB VRAM).
+- [LightX2V](https://github.com/ModelTC/LightX2V), a lightweight and efficient video generation framework that integrates **Wan2.1** and **Wan2.2**, supporting multiple engineering acceleration techniques for fast inference. [LightX2V-HuggingFace](https://huggingface.co/lightx2v), offers a variety of Wan-based step-distillation models, quantized models, and lightweight VAE models.
 - [HuMo](https://github.com/Phantom-video/HuMo) proposed a unified, human-centric framework based on **Wan** to produce high-quality, fine-grained, and controllable human videos from multimodal inputs—including text, images, and audio. Visit their [webpage](https://phantom-video.github.io/HuMo/) for more details.
 - [FastVideo](https://github.com/hao-ai-lab/FastVideo) includes distilled **Wan** models with sparse attention that significanly speed up the inference time. 
 - [Cache-dit](https://github.com/vipshop/cache-dit) offers Fully Cache Acceleration support for **Wan2.2** MoE with DBCache, TaylorSeer and Cache CFG. Visit their [example](https://github.com/vipshop/cache-dit/blob/main/examples/pipeline/run_wan_2.2.py) for more details.