From df28984990ee9aec9b206ed6e873db8957cbae7c Mon Sep 17 00:00:00 2001 From: helloyongyang Date: Mon, 15 Dec 2025 04:07:38 +0000 Subject: [PATCH] update --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 1bf25fa..4622697 100644 --- a/README.md +++ b/README.md @@ -36,7 +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. -- [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. +- [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. - [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). - [Wan-Move](https://github.com/ali-vilab/Wan-Move), accepted to NeurIPS 2025, a framework that brings **Wan2.1-I2V-14B** to SOTA fine-grained, point-level motion control! Refer to [their project page](https://wan-move.github.io/) for more information.