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sisniha/LightWan2.2-A14B

🤗 On Hugging Faceapache-2.067 GBother✓ HF checksums availableupdated today
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🎬 LightWan2.2-A14B

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

![GitHub](https://github.com/ModelTC/LightX2V)

![HuggingFace](https://huggingface.co/lightx2v/LightWan2.2-A14B)

![Blog](https://light-ai.top/LightX2V-BLOG/posts/LightWan22-A14B/)

📋 Table of Contents

✨ 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/I2V-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 single-GPU inference
# Text-to-video
bash scripts/wan22/extreme/run_wan22_moe_t2v_extreme.sh
# Image-to-video
bash scripts/wan22/extreme/run_wan22_moe_i2v_extreme.sh

# 3. Run multi-GPU sequence-parallel inference
# Text-to-video
bash scripts/wan22/extreme/run_wan22_moe_t2v_extreme_sp_parallel.sh
# Image-to-video
bash scripts/wan22/extreme/run_wan22_moe_i2v_extreme_sp_parallel.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 single-GPU inference
# Text-to-video
bash scripts/wan22/extreme/run_wan22_moe_t2v_extreme.sh
# Image-to-video
bash scripts/wan22/extreme/run_wan22_moe_i2v_extreme.sh

# 4. Run multi-GPU sequence-parallel inference
# Text-to-video
bash scripts/wan22/extreme/run_wan22_moe_t2v_extreme_sp_parallel.sh
# Image-to-video
bash scripts/wan22/extreme/run_wan22_moe_i2v_extreme_sp_parallel.sh

Single-GPU Scripts:

Multi-GPU Scripts:

🎬 Generation Results

"Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage"

| Resolution | Wan2.2-T2V-14B | LightWan2.2-A14B |

| --- | --- | --- |

| 480p | | |

| 720p | | |

⚡ Performance Comparison

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

| Method | Task | GPU Number | Resolution | NFE | E2E Latency | Speedup |

| --- | :---: | ---: | ---: | ---: | ---: | ---: |

| Wan2.2-T2V-14B | T2V | 1 | 480p | 40 | 734.0s | 1.0x |

| LightWan2.2-A14B | T2V | 1 | 480p | 4 | 9.1s | 80.7x |

| Wan2.2-T2V-14B | T2V | 1 | 720p | 40 | 2668.0s | 1.0x |

| LightWan2.2-A14B | T2V | 1 | 720p | 4 | 22.5s | 118.7x |

| Wan2.2-I2V-14B | I2V | 1 | 480p | 40 | 787.0s | 1.0x |

| LightWan2.2-A14B | I2V | 1 | 480p | 4 | 10.7s | 73.9x |

| Wan2.2-I2V-14B | I2V | 1 | 720p | 40 | 2685.0s | 1.0x |

| LightWan2.2-A14B | I2V | 1 | 720p | 4 | 26.7s | 100.5x |

⚠️ 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.
  • For I2V, also prepare the required image encoder components and input image according to the LightX2V Wan2.2 I2V script.
  • 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

If you find this project helpful, please give us a ⭐ on GitHub

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