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README.md

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 ## :fire: Latest News
 
+- **December 4, 2025:** 🚀 We support GGUF format model inference. We support Cambricon MLU590 and MetaX C500 deployment.
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 - **November 24, 2025:** 🚀 We released 4-step distilled models for HunyuanVideo-1.5! These models enable **ultra-fast 4-step inference** without CFG requirements, achieving approximately **25x speedup** compared to standard 50-step inference. Both base and FP8 quantized versions are now available: [Hy1.5-Distill-Models](https://huggingface.co/lightx2v/Hy1.5-Distill-Models).
 
 - **November 21, 2025:** 🚀 We support the [HunyuanVideo-1.5](https://huggingface.co/tencent/HunyuanVideo-1.5) video generation model since Day 0. With the same number of GPUs, LightX2V can achieve a speed improvement of over 2 times and supports deployment on GPUs with lower memory (such as the 24GB RTX 4090). It also supports CFG/Ulysses parallelism, efficient offloading, TeaCache/MagCache technologies, and more. We will soon update more models on our [HuggingFace page](https://huggingface.co/lightx2v), including step distillation, VAE distillation, and other related models. Quantized models and lightweight VAE models are now available: [Hy1.5-Quantized-Models](https://huggingface.co/lightx2v/Hy1.5-Quantized-Models) for quantized inference, and [LightTAE for HunyuanVideo-1.5](https://huggingface.co/lightx2v/Autoencoders/blob/main/lighttaehy1_5.safetensors) for fast VAE decoding. Refer to [this](https://github.com/ModelTC/LightX2V/tree/main/scripts/hunyuan_video_15) for usage tutorials, or check out the [examples directory](https://github.com/ModelTC/LightX2V/tree/main/examples) for code examples.

README_zh.md

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 ## :fire: 最新动态
 
+- **2025年12月4日:** 🚀 我们支持了GGUF格式模型推理。我们支持了寒武纪MLU590和沐曦C500的部署。
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 - **2025年11月24日:** 🚀 我们发布了HunyuanVideo-1.5的4步蒸馏模型！这些模型支持**超快速4步推理**，无需CFG配置，相比标准50步推理可实现约**25倍加速**。现已提供基础版本和FP8量化版本：[Hy1.5-Distill-Models](https://huggingface.co/lightx2v/Hy1.5-Distill-Models)。
 
 - **2025年11月21日:** 🚀 我们Day0支持了[HunyuanVideo-1.5](https://huggingface.co/tencent/HunyuanVideo-1.5)的视频生成模型，同样GPU数量，LightX2V可带来约2倍以上的速度提升，并支持更低显存GPU部署(如24G RTX4090)。支持CFG并行/Ulysses并行，高效Offload，TeaCache/MagCache等技术。同时支持沐曦，寒武纪等国产芯片部署。我们很快将在我们的[HuggingFace主页](https://huggingface.co/lightx2v)更新更多模型，包括步数蒸馏，VAE蒸馏等相关模型。量化模型和轻量VAE模型现已可用：[Hy1.5-Quantized-Models](https://huggingface.co/lightx2v/Hy1.5-Quantized-Models)用于量化推理，[HunyuanVideo-1.5轻量TAE](https://huggingface.co/lightx2v/Autoencoders/blob/main/lighttaehy1_5.safetensors)用于快速VAE解码。使用教程参考[这里](https://github.com/ModelTC/LightX2V/tree/main/scripts/hunyuan_video_15)，或查看[示例目录](https://github.com/ModelTC/LightX2V/tree/main/examples)获取代码示例。