charles2530/Wan2.2-NVFP4-Sparse

🎬 Wan2.2-NVFP4-Sparse

An extremely efficient Wan 2.2 14B variant: NVFP4 Quantization-Aware Step Distillation with Sparse Attention for Blackwell Architecture

 

📋 Table of Contents

• —✨ Features
• —🚀 Quick Start
• —🎬 Generation Results
• —⚡ Performance Comparison
• —⚠️ Notes
• —🤝 Community

✨ Features

• —⚡ 4-Step Inference: Two high-noise expert steps followed by two low-noise expert steps, enabling extremely fast Wan2.2 MoE generation on a single Blackwell GPU.
• —🎯 NVFP4 Quantization: Quantization-aware step distillation reduces memory traffic and compute cost while targeting Blackwell architecture.
• —🧩 Sparse Attention: Accelerates the costly O(n²) self-attention workload with sparse attention, reducing end-to-end latency for high-resolution video generation.
• —🔧 LightX2V Integration: Recommended runtime stack for stable deployment and best performance.
• —🚀 High-Quality Generation: Preserves the visual quality of Wan2.2-T2V-14B while dramatically improving inference speed.

🚀 Quick Start

We strongly recommend using the official LightX2V Docker image for the cleanest environment and best reproducibility.

Option A: Docker Recommended

# 1. Pull LightX2V Docker image
docker pull lightx2v/lightx2v:26052801-cu130-5090

# 2. Run inference
bash scripts/wan22/distill/run_wan22_moe_t2v_extreme.sh

Option B: Manual Installation

If Docker is not available, install the environment manually:

# 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
bash scripts/wan22/distill/run_wan22_moe_t2v_extreme.sh

Script: run_wan22_moe_t2v_extreme.sh

🎬 Generation Results

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⚡ Performance Comparison

Test Environment: RTX 5090 Single GPU | LightX2V Framework | End-to-End Latency

⚠️ Notes

System Requirements

• —Required Hardware: NVIDIA RTX 50-series GPUs or other Blackwell architecture GPUs.
• —Recommended Runtime: lightx2v/lightx2v:26052801-cu130-5090.

Dependencies

• —Prepare Wan2.2 T5 / VAE components following the standard LightX2V Wan2.2 model structure.
• —Use Blackwell + NVFP4 kernels for optimal speed and memory efficiency.

Performance Tips

• —Use the provided extreme inference script for the 4-step high-noise / low-noise expert schedule.
• —Sparse attention is most beneficial at higher resolutions where self-attention dominates latency.
• —Enable CPU offload only when GPU memory is limited, since offload can reduce throughput.

🤝 Community

• —🐛 Issues: GitHub Issues
• —🤗 Models: HuggingFace Hub
• —📖 Documentation: LightX2V Docs

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If you find this project helpful, please give us a ⭐ on [GitHub](https://github.com/ModelTC/LightX2V)

For questions or issues, please open an issue on LightX2V or contact lvchengtao0319@gmail.com.

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