SGLang Diffusion

SGLang Diffusion is a high-performance inference framework for image and video generation. It provides native SGLang pipelines, diffusers backend support, an OpenAI-compatible server, and an optimized kernel stack built on both precompiled sgl-kernel operators and JIT kernels for key inference paths.

Key Features

• Broad model support across Wan, Hunyuan, Qwen-Image, FLUX, Z-Image, GLM-Image, and more
• Fast inference with sgl-kernel, JIT kernels, scheduler improvements, and caching acceleration
• Multiple interfaces: sglang generate, sglang serve, and an OpenAI-compatible API
• Multi-platform support for NVIDIA, AMD, Intel XPU, Ascend, Apple Silicon, and Moore Threads

Quick Start

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

sglang generate --model-path Qwen/Qwen-Image \
  --prompt "A beautiful sunset over the mountains" \
  --save-output

sglang serve --model-path Qwen/Qwen-Image --port 30010

Start Here

• Installation: install SGLang Diffusion and platform dependencies
• Compatibility Matrix: check model, optimization, and component override support
• CLI: run one-off generation jobs or launch a persistent server
• OpenAI-Compatible API: send image and video requests to the HTTP server
• Attention Backends: choose the best backend for your model and hardware
• Caching Acceleration: use Cache-DiT or TeaCache to reduce denoising cost
• Quantization: load quantized transformer checkpoints
• Contributing: contribution workflow, adding new models, and CI perf baselines

Additional Documentation

• Post-Processing: frame interpolation and upscaling
• Performance Overview: overview of attention, caching, and profiling
• Environment Variables: platform, caching, storage, and debugging configuration
• Support New Models: implementation guide for new diffusion pipelines
• CI Performance: performance baseline generation

References

• SGLang GitHub
• Cache-DiT
• FastVideo
• xDiT
• Diffusers