---
license: apache-2.0
tags:
- diffusion-single-file
- comfyui
- distillation
- NVFP4
- video
- video genration
base_model:
- Wan-AI/Wan2.1-I2V-14B-480P
- Wan-AI/Wan2.1-T2V-1.3B
pipeline_tags:
- image-to-video
- text-to-video
library_name: diffusers
---
# 🎬 Wan-NVFP4-4Steps Models
> **NVFP4 Quantization-Aware Step Distillation for Blackwell Architecture**
[](https://github.com/ModelTC/LightX2V)
[](https://huggingface.co/lightx2v/)
## 📋 Table of Contents
- [✨ Features](#-features)
- [🚀 Quick Start](#-quick-start)
- [🎬 Generation Results](#-generation-results)
- [⚡ Performance Comparison](#-performance-comparison)
- [📦 Installation](#-installation)
- [🛠️ Usage](#-usage)
- [🧭 Project Structure](#-project-structure)
- [⚠️ Notes](#️-notes)
- [🤝 Community](#-community)
## ✨ Features
- **⚡ 4-Step Inference**: Dramatically accelerated end-to-end generation approaching real-time performance (tested on RTX 5090 single GPU)
- **🎯 NVFP4 Quantization**: Reduced memory and bandwidth usage, optimized for Blackwell architecture
- **🔧 LightX2V Integration**: Optimal performance and stability on the official framework
- **🚀 High-Quality Generation**: Maintains Wan2.1's superior video quality while achieving unprecedented speed
## 🚀 Quick Start
```bash
# 1. Install LightX2V
git clone https://github.com/ModelTC/LightX2V.git
cd LightX2V
uv pip install -v .
# 2. Install NVFP4 Kernel
pip install scikit_build_core uv
git clone https://github.com/NVIDIA/cutlass.git
cd lightx2v_kernel
MAX_JOBS=$(nproc) CMAKE_BUILD_PARALLEL_LEVEL=$(nproc) \
uv build --wheel \
-Cbuild-dir=build . \
-Ccmake.define.CUTLASS_PATH=/path/to/cutlass \
--verbose --color=always --no-build-isolation
pip install dist/*whl --force-reinstall --no-deps
# 3. Run inference
cd examples/wan
python wan_i2v_nvfp4.py # Image-to-Video
python wan_t2v_nvfp4.py # Text-to-Video
```
## 🎬 Generation Results
"A cinematic, hyper-realistic 3D animation, in the somber and beautiful style of Sekiro: Shadows Die Twice. In a vast field of silvery-white pampas grass, under a luminous full moon, the shinobi Wolf stands ready for a final duel..."
| Input Image |
Wan2.1-I2V-14B-480P |
wan2.1_i2v_480p_nvfp4_lightx2v_4step |
|
|
|
"高对比度，高饱和度，短边构图，日落，中焦距，柔光，背光，暖色调，边缘光，中近景，日光，晴天光，一位外国白人女性的近景，她身穿黄色格子连衣裙，戴着耳环。随着仰拍镜头的上升，女子抬起头来，眼睛里含着泪水，看着前方说着话..."
| Wan2.1-T2V-1.3B | wan2.1_t2v_1_3b_nvfp4_lightx2v_4step |
| --- | --- |
| | |
## ⚡ Performance Comparison
**Test Environment**: RTX 5090 Single GPU | LightX2V Framework
📸 Image-to-Video (I2V-14B-480P)
| Metric |
Original Model |
Optimized Model |
Speedup |
| Single-step Denoising |
12.10s |
3.40s |
3.5x |
| End-to-End |
498.90s |
17.65s |
28x |
|
🎬 Text-to-Video (T2V-1.3B-480P)
| Metric |
Original Model |
Optimized Model |
Speedup |
| Single-step Denoising |
2.00s |
0.70s |
2.9x |
| End-to-End |
83.50s |
6.54s |
12.8x |
|
## ⚠️ Notes
### System Requirements
- **Required Hardware**: NVIDIA RTX 50-series GPUs (RTX 5090/5080/5070/5060) or other Blackwell architecture GPUs
### Dependencies
- Prepare T5 / CLIP / VAE components yourself (same as Wan2.x structure)
### Performance Tips
- Use Blackwell + NVFP4 for best performance
- Enable CPU offload for GPUs with limited memory
## 🤝 Community
- **🐛 Issues**: [GitHub Issues](https://github.com/ModelTC/LightX2V/issues)
- **🤗 Models**: [HuggingFace Hub](https://huggingface.co/lightx2v/)
- **📖 Documentation**: [LightX2V Docs](https://github.com/ModelTC/LightX2V)
---
**If you find this project helpful, please give us a ⭐ on [GitHub](https://github.com/ModelTC/LightX2V)**