From 8177ee5bc6bcd49f7c9979f4c0abd426156df106 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Yang=20Yong=20=28=E9=9B=8D=E6=B4=8B=29?= Date: Mon, 15 Dec 2025 16:59:29 +0800 Subject: [PATCH] Add LightX2V Community Works (#558) * Add LightX2V Community Works * update * update * update --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 8f03f2a..6d80802 100644 --- a/README.md +++ b/README.md @@ -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. +- [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). - [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.