sglang/python/sglang/multimodal_gen/README.md

sgl-diffusion is an inference framework for accelerated image/video generation.

sgl-diffusion features an end-to-end unified pipeline for accelerating diffusion models. It is designed to be modular and extensible, allowing users to easily add new optimizations and techniques.

Key Features

sgl-diffusion has the following features:

• State-of-the-art performance optimizations for inference
  • Video Sparse Attention
  • Sliding Tile Attention
  • TeaCache
  • Sage Attention
  • USP
  • CFG Parallel
• Diverse hardware and OS support
  • Supported hardware: H100, H200, A100, B200, 4090
  • Supported OS: Linux, Windows, MacOS

Getting Started

uv pip install sglang[.diffusion] --prerelease=allow

For more information, check the docs.

Inference

Here's a minimal example to generate a video using the default settings:

from sglang.multimodal_gen import DiffGenerator

def main():
    # Create a diff generator from a pre-trained model
    generator = DiffGenerator.from_pretrained(
        model_path="Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
        num_gpus=1,  # Adjust based on your hardware
    )

    # Provide a prompt for your video
    prompt = "A curious raccoon peers through a vibrant field of yellow sunflowers, its eyes wide with interest."

    # Generate the video
    video = generator.generate(
        prompt,
        return_frames=True,  # Also return frames from this call (defaults to False)
        output_path="my_videos/",  # Controls where videos are saved
        save_output=True
    )

if __name__ == '__main__':
    main()

Or, more simply, with the CLI:

sglang generate --model-path Wan-AI/Wan2.1-T2V-1.3B-Diffusers \
    --text-encoder-cpu-offload --pin-cpu-memory \
    --prompt "A curious raccoon" \
    --save-output

For more information, check the docs.

Contributing

All contributions are welcome.

Acknowledgement

We learnt and reused code from the following projects:

• FastVideo. The major components of this repo are based on a fork of FastVide on Sept. 24, 2025.
• xDiT. We used the parallelism library from it.
• diffusers We used the pipeline design from it.