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README.md
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- **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.
## 🏆 Performance Benchmarks (Updated on 2025.12.01)
### 📊 Cross-Framework Performance Comparison (H100)
| Framework | Cards | Step Time | Speedup |
|-----------|---------|---------|---------|
| Diffusers | 1 | 9.77s/it | 1x |
| xDiT | 1 | 8.93s/it | 1.1x |
| FastVideo | 1 | 7.35s/it | 1.3x |
| SGL-Diffusion | 1 | 6.13s/it | 1.6x |
| **LightX2V** | 1 | **5.18s/it** | **1.9x** 🚀 |
| FastVideo | 8 | 2.94s/it | 1x |
| xDiT | 8 | 2.70s/it | 1.1x |
| SGL-Diffusion | 8 | 1.19s/it | 2.5x |
| **LightX2V** | 8 | **0.75s/it** | **3.9x** 🚀 |
### 📊 Cross-Framework Performance Comparison (RTX 4090D)
| Framework | Cards | Step Time | Speedup |
|-----------|---------|---------|---------|
| Diffusers | 1 | 30.50s/it | 1x |
| xDiT | 1 | OOM | OOM |
| FastVideo | 1 | OOM | OOM |
| SGL-Diffusion | 1 | 22.66s/it | 1.3x |
| **LightX2V** | 1 | **20.26s/it** | **1.5x** 🚀 |
| FastVideo | 8 | 15.48s/it | 1x |
| xDiT | 8 | OOM | OOM |
| SGL-Diffusion | 8 | OOM | OOM |
| **LightX2V** | 8 | **4.75s/it** | **3.3x** 🚀 |
### 📊 LightX2V Performance Comparison
| Framework | GPU | Configuration | Step Time | Speedup |
|-----------|-----|---------------|-----------|---------------|
| **LightX2V** | H100 | 8 cards + cfg | 0.75s/it | 1x |
| **LightX2V** | H100 | 8 cards + no cfg | 0.39s/it | 1.9x |
| **LightX2V** | H100 | **8 cards + no cfg + fp8** | **0.35s/it** | **2.1x** 🚀 |
| **LightX2V** | 4090D | 8 cards + cfg | 4.75s/it | 1x |
| **LightX2V** | 4090D | 8 cards + no cfg | 3.13s/it | 1.5x |
| **LightX2V** | 4090D | **8 cards + no cfg + fp8** | **2.35s/it** | **2.0x** 🚀 |
**Note**: All the above performance data were tested on Wan2.1-I2V-14B-480P(40 steps, 81 frames). In addition, we also provide a 4-step distilled model on the [HuggingFace page](https://huggingface.co/lightx2v).
## 💡 Quick Start
> 🌐 **Try it online now!** Experience LightX2V without installation: **[LightX2V Online Service](https://x2v.light-ai.top/login)** - Free, lightweight, and fast AI digital human video generation platform.
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- **🎞️ Video Frame Interpolation**: RIFE-based frame interpolation for smooth frame rate enhancement
## 🏆 Performance Benchmarks
For detailed performance metrics and comparisons, please refer to our [benchmark documentation](https://github.com/ModelTC/LightX2V/blob/main/docs/EN/source/getting_started/benchmark_source.md).
[Detailed Service Deployment Guide →](https://lightx2v-en.readthedocs.io/en/latest/deploy_guides/deploy_service.html)
## 📚 Technical Documentation
### 📖 **Method Tutorials**
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For questions, suggestions, or support, please feel free to reach out through:
- 🐛 [GitHub Issues](https://github.com/ModelTC/lightx2v/issues) - Bug reports and feature requests
- 💬 [GitHub Discussions](https://github.com/ModelTC/lightx2v/discussions) - Community discussions and Q&A
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README_zh.md
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- **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。
## 🏆 性能测试数据 (更新于 2025.12.01)
### 📊 推理框架之间性能对比 (H100)
| Framework | Cards | Step Time | Speedup |
|-----------|---------|---------|---------|
| Diffusers | 1 | 9.77s/it | 1x |
| xDiT | 1 | 8.93s/it | 1.1x |
| FastVideo | 1 | 7.35s/it | 1.3x |
| SGL-Diffusion | 1 | 6.13s/it | 1.6x |
| **LightX2V** | 1 | **5.18s/it** | **1.9x** 🚀 |
| FastVideo | 8 | 2.94s/it | 1x |
| xDiT | 8 | 2.70s/it | 1.1x |
| SGL-Diffusion | 8 | 1.19s/it | 2.5x |
| **LightX2V** | 8 | **0.75s/it** | **3.9x** 🚀 |
### 📊 推理框架之间性能对比 (RTX 4090D)
| Framework | Cards | Step Time | Speedup |
|-----------|---------|---------|---------|
| Diffusers | 1 | 30.50s/it | 1x |
| xDiT | 1 | OOM | OOM |
| FastVideo | 1 | OOM | OOM |
| SGL-Diffusion | 1 | 22.66s/it | 1.3x |
| **LightX2V** | 1 | **20.26s/it** | **1.5x** 🚀 |
| FastVideo | 8 | 15.48s/it | 1x |
| xDiT | 8 | OOM | OOM |
| SGL-Diffusion | 8 | OOM | OOM |
| **LightX2V** | 8 | **4.75s/it** | **3.3x** 🚀 |
### 📊 LightX2V不同配置之间性能对比
| Framework | GPU | Configuration | Step Time | Speedup |
|-----------|-----|---------------|-----------|---------------|
| **LightX2V** | H100 | 8 cards + cfg | 0.75s/it | 1x |
| **LightX2V** | H100 | 8 cards + no cfg | 0.39s/it | 1.9x |
| **LightX2V** | H100 | **8 cards + no cfg + fp8** | **0.35s/it** | **2.1x** 🚀 |
| **LightX2V** | 4090D | 8 cards + cfg | 4.75s/it | 1x |
| **LightX2V** | 4090D | 8 cards + no cfg | 3.13s/it | 1.5x |
| **LightX2V** | 4090D | **8 cards + no cfg + fp8** | **2.35s/it** | **2.0x** 🚀 |
**Note**: All the above performance data were tested on Wan2.1-I2V-14B-480P(40 steps, 81 frames). In addition, we also provide a 4-step distilled model on the [HuggingFace page](https://huggingface.co/lightx2v).
## 💡 快速开始
> 🌐 **立即在线体验！** 无需安装即可体验 LightX2V：**[LightX2V 在线服务](https://x2v.light-ai.top/login)** - 免费、轻量、快速的AI数字人视频生成平台。
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- **🎞️ 视频帧插值**: 基于RIFE的帧插值技术，实现流畅的帧率提升
## 🏆 性能基准测试
详细的性能指标和对比分析，请参考我们的[基准测试文档](https://github.com/ModelTC/LightX2V/blob/main/docs/ZH_CN/source/getting_started/benchmark_source.md)。
[详细服务部署指南 →](https://lightx2v-zhcn.readthedocs.io/zh-cn/latest/deploy_guides/deploy_service.html)
## 📚 技术文档
### 📖 **方法教程**
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如有任何问题、建议或需要支持，欢迎通过以下方式联系我们：
- 🐛 [GitHub Issues](https://github.com/ModelTC/lightx2v/issues) - 错误报告和功能请求
- 💬 [GitHub Discussions](https://github.com/ModelTC/lightx2v/discussions) - 社区讨论和问答
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