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SGLang Diffusion is an inference framework for accelerated image and video generation using diffusion models. It provides an end-to-end unified pipeline with optimized kernels and an efficient scheduler loop.

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Key features

  • Broad model support: Wan series, FastWan series, Hunyuan, Qwen-Image, Qwen-Image-Edit, Flux, Z-Image, GLM-Image, and more
  • Fast inference: optimized kernels, efficient scheduler loop, and Cache-DiT acceleration
  • Ease of use: OpenAI-compatible API, CLI, and Python SDK
  • Multi-platform: NVIDIA GPUs (H100, H200, A100, B200, 4090), AMD GPUs (MI300X, MI325X), and Ascend NPU (A2, A3)

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Quick start

  1. Install SGLang Diffusion
uv pip install "sglang[diffusion]" --prerelease=allow
See the installation guide for more installation methods and ROCm-specific instructions.
  1. Run a one-off generation
sglang generate --model-path Qwen/Qwen-Image \
  --prompt "A beautiful sunset over the mountains" \
  --save-output
  1. Serve with the OpenAI-compatible API
sglang serve --model-path Qwen/Qwen-Image --port 30010

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CLI quick reference

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Generate (one-off generation)

sglang generate --model-path <MODEL> --prompt "<PROMPT>" --save-output

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Serve (HTTP server)

sglang serve --model-path <MODEL> --port 30010

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Enable Cache-DiT acceleration

SGLANG_CACHE_DIT_ENABLED=true sglang generate --model-path <MODEL> --prompt "<PROMPT>"

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References