sglang/python/sglang/multimodal_gen

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

SGLang-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 models and optimizations.

Key Features

SGLang Diffusion has the following features:

• Broad model support: Wan series, FastWan series, Hunyuan, Qwen-Image, Qwen-Image-Edit, Flux
• Fast inference speed: enpowered by highly optimized kernel from sgl-kernel and efficient scheduler loop
• Ease of use: OpenAI-compatible api, CLI, and python sdk support
• Diverse hardware support: H100, H200, A100, B200, 4090

Getting Started

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

For more installation methods (e.g. pypi, uv, docker), check install.md.

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 usage examples (e.g. OpenAI compatible API, server mode), check cli.md.

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.