sglang/docs_new/sglang-diffusion/intro.mdx

---
title: SGLang Diffusion
description: Accelerated image and video generation with diffusion models.
---

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.

## 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)

## Quick start

1. **Install SGLang Diffusion**

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

See the [installation guide](../docs/sglang-diffusion/installation) for more installation methods and ROCm-specific instructions.

2. **Run a one-off generation**

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

3. **Serve with the OpenAI-compatible API**

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

## CLI quick reference

### Generate (one-off generation)

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

### Serve (HTTP server)

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

### Enable Cache-DiT acceleration

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

## References

* [SGLang GitHub](https://github.com/sgl-project/sglang)
* [Cache-DiT](https://github.com/vipshop/cache-dit)
* [FastVideo](https://github.com/hao-ai-lab/FastVideo)
* [xDiT](https://github.com/xdit-project/xDiT)
* [Diffusers](https://github.com/huggingface/diffusers)