SGLang Diffusion: Accelerating Video and Image Generation

by: The SGLang Diffusion Team, Nov 07, 2025

We are excited to introduce SGLang Diffusion, which brings SGLang's state-of-the-art performance to accelerate image and video generation for diffusion models. SGLang Diffusion supports major open-source video and image generation models (Wan, Hunyuan, Qwen-Image, Qwen-Image-Edit, Flux) while providing fast inference speeds and ease of use via multiple API entry points (OpenAI-compatible API, CLI, Python interface). SGLang Diffusion delivers 1.2x–5.9x speedup across diverse workloads. In collaboration with the FastVideo team, we provide a complete ecosystem for diffusion models, from post-training to production serving. The code is available here.

SGL Diffusion Performance Benchmark on an H100 GPU.

Why Diffusion in SGLang?

With diffusion models becoming the backbone for state-of-the-art image and video generation, we have heard strong community demand for bringing SGLang's signature performance and seamless user experience to these new modalities. We built SGLang Diffusion to answer this call, providing a unified, high-performance engine for both language and diffusion tasks.

This unified approach is crucial, as the future of generation lies in combining architectures. Pioneering models are already fusing the strengths of autoregressive (AR) and diffusion-based approaches—from models like ByteDance's Bagel and Meta's Transfusion that use a single transformer for both tasks, to NVIDIA's Fast-dLLM v2 which adapts AR models for parallel generation.

SGLang Diffusion is designed to be a future-proof, high-performance solution ready to power these innovative systems.

Architecture

SGLang Diffusion is engineered for both performance and flexibility, built upon SGLang's battle-tested serving architecture. It inherits the powerful SGLang scheduler and reuses highly-optimized sgl-kernel for maximum efficiency.

At its core, our architecture is designed to accommodate the diverse structures of modern diffusion models. We introduce ComposedPipelineBase, a flexible abstraction that orchestrates a series of modular PipelineStages. Each stage encapsulates a common diffusion function—such as the denoising loop in DenoisingStage or VAE decoding in DecodingStage—allowing developers to easily combine and reuse these components to construct complex, customized pipelines.

To achieve state-of-the-art speed, we integrate advanced parallelism techniques. It supports Unified Sequence Parallelism (USP)—a combination of Ulysses-SP and Ring-Attention—for the core transformer blocks, alongside CFG-parallelism and tensor parallelism (TP) for other model components.

To accelerate development and foster a powerful ecosystem, our system is built on an enhanced fork of FastVideo, and we are collaborating closely with their team. This partnership allows SGLang Diffusion to focus on delivering cutting-edge inference speed, while FastVideo provides comprehensive support for training-related tasks like model distillation.

Model Support

We support various popular open-source video & image generation models, including:

Video models: Wan-series, FastWan, Hunyuan
Image models: Qwen-Image, Qwen-Image-Edit, Flux

For full list of supported models, reference here.

Usage

For a seamless user experience, we provide a suite of familiar interfaces, including a CLI, a Python engine API, and an OpenAI-compatible API, allowing users to integrate diffusion generation into their workflows with minimal effort.

Install

SGLang Diffusion can be installed via multiple ways:

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

# from source
git clone https://github.com/sgl-project/sglang.git
cd sglang
uv pip install -e "python[diffusion]" --prerelease=allow

CLI

Launch a server and then send requests:

sglang serve --model-path black-forest-labs/FLUX.1-dev

curl -s -D >(grep -i x-request-id >&2) \
  -o >(jq -r '.data[0].b64_json' | base64 --decode > meme.png) \
  -X POST "$OPENAI_API_BASE/images/edits" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -F "model=Qwen/Qwen-Image-Edit" \
  -F "image[]=@example.jpg" \
  -F 'prompt=Create a meme based on image provide'

Or, Generate an image without launching a server:

sglang generate --model-path black-forest-labs/FLUX.1-dev \
  --prompt "A Logo With Bold Large Text: SGL Diffusion" \
  --save-output

Reference install guide and cli guide for more installation methods.

Demo

Text to Video: Wan-AI/Wan2.1

sglang generate --model-path Wan-AI/Wan2.1-T2V-1.3B-Diffusers \
    --prompt "A curious raccoon" \
    --save-output

Fallback link: Download the video

Image to Video: Wan-AI/Wan2.1-I2V

sglang generate --model-path=Wan-AI/Wan2.1-I2V-14B-480P-Diffusers  \
    --prompt="Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside." \
    --image-path="https://github.com/Wan-Video/Wan2.2/blob/990af50de458c19590c245151197326e208d7191/examples/i2v_input.JPG?raw=true" \
    --save-output --num-gpus 2 --enable-cfg-parallel

Fallback link: Download the video

Text to Image: FLUX

sglang generate --model-path black-forest-labs/FLUX.1-dev \
    --prompt "A Logo With Bold Large Text: SGL Diffusion" \
    --save-output

Text to Image: Qwen-Image

sglang generate --model-path=Qwen/Qwen-Image \
    --prompt='A curious raccoon' \
    --width=720 --height=720 \
    --save-output \

Image to Image: Qwen-Image-Edit

sglang generate \
   --prompt="Convert 2D style to 3D style" --image-path="https://github.com/lm-sys/lm-sys.github.io/releases/download/test/TI2I_Qwen_Image_Edit_Input.jpg" --model-path=Qwen/Qwen-Image-Edit \
   --width=1024 --height=1536 --save-output

Input

Output

Performance Benchmark

We benchmarked the performance of SGLang Diffusion against a popular open-source baseline Huggingface Diffuser. As benchmarked in the chart at the top of this post, SGLang Diffusion delivers state-of-the-art performance, significantly accelerating both image and video generation.

Roadmap and Diffusion Ecosystem

Our vision is to build a comprehensive diffusion ecosystem in collaboration with the FastVideo team, providing an end-to-end solution from model training to high-performance inference.

The SGLang Diffusion team is centered on continuous innovation in performance and model support:

Model support and optimizations
- Optimize Wan, FastWan, Hunyuan, Qwen-Image series, FLUX
- Support LongCat-Video
Kernel support and fusions
- Quantization kernels
- Rotary embedding kernels
- Flash Attention 4 integration in sgl-kernel for blackwell
More server features
- Configurable cloud storage upload of generated files
- Batching support
- More parallelism methods
- Quantization
General architecture:
- Simplify the effort of supporting new models
- Enhance cache and attention backend supports

Building this ecosystem is a community effort, and we welcome and encourage all forms of contribution. Join us in shaping the future of open-source diffusion generation.

Acknowledgment

SGLang Diffusion Team: Yuhao Yang, Xinyuan Tong, Yi Zhang, Bao Ke, Ji Li, Xi Chen, Laixin Xie, Yikai Zhu, Mick

FastVideo Team: Zhang Peiyuan, William Lin, BrianChen1129, kevin314, Edenzzzz, JerryZhou54, rlsu9, Eigensystem, foreverpiano, RandNMR73, PorridgeSwim, Gary-ChenJL, Hao Zhang

Special thanks to NVIDIA and Voltage Park for their compute support.

Learn more

Roadmap: Diffusion (2025 Q4)
Slack channel: #diffusion (join via slack.sglang.ai)