**LightX2V** is an advanced lightweight video generation inference framework engineered to deliver efficient, high-performance video synthesis solutions. This unified platform integrates multiple state-of-the-art video generation techniques, supporting diverse generation tasks including text-to-video (T2V) and image-to-video (I2V). **X2V represents the transformation of different input modalities (X, such as text or images) into video output (V)**.
## :fire: Latest News
- **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 our models on our [HuggingFace page](https://huggingface.co/lightx2v), including quantization, step distillation, VAE distillation, and other related models. Refer to [this](https://github.com/ModelTC/LightX2V/tree/main/scripts/hunyuan_video_15) for usage tutorials.
## 💡 Quick Start
For comprehensive usage instructions, please refer to our documentation: **[English Docs](https://lightx2v-en.readthedocs.io/en/latest/) | [中文文档](https://lightx2v-zhcn.readthedocs.io/zh-cn/latest/)**
For attention operators installation, please refer to our documentation: **[English Docs](https://lightx2v-en.readthedocs.io/en/latest/getting_started/quickstart.html#step-4-install-attention-operators) | [中文文档](https://lightx2v-zhcn.readthedocs.io/zh-cn/latest/getting_started/quickstart.html#id9)**
### Quick Start
```python
# examples/hunyuan_video/hunyuan_t2v.py
from lightx2v import LightX2VPipeline
pipe = LightX2VPipeline(
model_path="/path/to/ckpts/hunyuanvideo-1.5/",
model_cls="hunyuan_video_1.5",
transformer_model_name="720p_t2v",
task="t2v",
)
pipe.create_generator(
attn_mode="sage_attn2",
infer_steps=50,
num_frames=121,
guidance_scale=6.0,
sample_shift=9.0,
aspect_ratio="16:9",
fps=24,
)
seed = 123
prompt = "A close-up shot captures a scene on a polished, light-colored granite kitchen counter, illuminated by soft natural light from an unseen window."
🔔 Follow our [HuggingFace page](https://huggingface.co/lightx2v) for the latest model releases from our team.
💡 Refer to the [Model Structure Documentation](https://lightx2v-en.readthedocs.io/en/latest/getting_started/model_structure.html) to quickly get started with LightX2V
## 🚀 Frontend Interfaces
We provide multiple frontend interface deployment options:
- **🎨 Gradio Interface**: Clean and user-friendly web interface, perfect for quick experience and prototyping
- **🚀 Windows One-Click Deployment**: Convenient deployment solution designed for Windows users, featuring automatic environment configuration and intelligent parameter optimization
- **🎞️ 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**
- [Model Quantization](https://lightx2v-en.readthedocs.io/en/latest/method_tutorials/quantization.html) - Comprehensive guide to quantization strategies
- [Video Frame Interpolation](https://lightx2v-en.readthedocs.io/en/latest/method_tutorials/video_frame_interpolation.html) - Base on the RIFE technology
- [Gradio Deployment](https://lightx2v-en.readthedocs.io/en/latest/deploy_guides/deploy_gradio.html) - Web interface setup
- [Service Deployment](https://lightx2v-en.readthedocs.io/en/latest/deploy_guides/deploy_service.html) - Production API service deployment
- [Lora Model Deployment](https://lightx2v-en.readthedocs.io/en/latest/deploy_guides/lora_deploy.html) - Flexible Lora deployment
## 🧾 Contributing Guidelines
We maintain code quality through automated pre-commit hooks to ensure consistent formatting across the project.
> [!TIP]
> **Setup Instructions:**
>
> 1. Install required dependencies:
> ```shell
> pip install ruff pre-commit
> ```
>
> 2. Run before committing:
> ```shell
> pre-commit run --all-files
> ```
We appreciate your contributions to making LightX2V better!
## 🤝 Acknowledgments
We extend our gratitude to all the model repositories and research communities that inspired and contributed to the development of LightX2V. This framework builds upon the collective efforts of the open-source community.
## 🌟 Star History
[](https://star-history.com/#ModelTC/lightx2v&Timeline)
## ✏️ Citation
If you find LightX2V useful in your research, please consider citing our work:
```bibtex
@misc{lightx2v,
author = {LightX2V Contributors},
title = {LightX2V: Light Video Generation Inference Framework},
