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You can install SGLang-Diffusion using one of the methods below. The standard installation already includes SGLang’s optimized kernel stack, including both sgl-kernel and JIT kernels used by diffusion workloads.

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Standard Installation (NVIDIA GPUs)

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Method 1: With pip or uv

It is recommended to use uv for a faster installation:
Command
pip install --upgrade pip
pip install uv
uv pip install "sglang[diffusion]" --prerelease=allow

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Method 2: From source

Command
# Use the latest release branch
git clone https://github.com/sgl-project/sglang.git
cd sglang

# Install the Python packages
pip install --upgrade pip
pip install -e "python[diffusion]"

# With uv
uv pip install -e "python[diffusion]" --prerelease=allow

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Method 3: Using Docker

The Docker images are available on Docker Hub at lmsysorg/sglang, built from the Dockerfile. Replace <secret> below with your HuggingFace Hub token.
Command
docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:dev \
    zsh -c '\
        echo "Installing diffusion dependencies..." && \
        pip install -e "python[diffusion]" && \
        echo "Starting SGLang-Diffusion..." && \
        sglang generate \
            --model-path black-forest-labs/FLUX.1-dev \
            --prompt "A logo With Bold Large text: SGL Diffusion" \
            --save-output \
    '

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Platform-Specific: ROCm (AMD GPUs)

For AMD Instinct GPUs (e.g., MI300X), use a ROCm-enabled Docker image from lmsysorg/sglang. The tag below is an example for ROCm 7.0 on MI300X and may lag the latest release tag:
Command
docker run --device=/dev/kfd --device=/dev/dri --ipc=host \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  --env HF_TOKEN=<secret> \
  lmsysorg/sglang:v0.5.5.post2-rocm700-mi30x \
  sglang generate --model-path black-forest-labs/FLUX.1-dev --prompt "A logo With Bold Large text: SGL Diffusion" --save-output
For detailed ROCm system configuration and installation from source, see AMD GPUs.

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Platform-Specific: MUSA (Moore Threads GPUs)

For Moore Threads GPUs (MTGPU) with the MUSA software stack, follow the platform guide first. If the source tree still requires the alternate platform pyproject fallback, keep a backup of the default file before switching:
Command
# Clone the repository
git clone https://github.com/sgl-project/sglang.git
cd sglang

# Install the Python packages
pip install --upgrade pip
mv python/pyproject.toml python/pyproject.toml.bak
cp python/pyproject_other.toml python/pyproject.toml
pip install -e "python[all_musa]"

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Platform-Specific: Intel XPU

For Intel Data Center GPU Max or Arc GPUs, follow the XPU installation guide to set up the base environment, then install diffusion dependencies:
Command
pip install -e "python[diffusion]"

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Platform-Specific: Ascend NPU

For Ascend NPU, please follow the NPU installation guide. Quick test:
Command
sglang generate --model-path black-forest-labs/FLUX.1-dev \
    --prompt "A logo With Bold Large text: SGL Diffusion" \
    --save-output

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Platform-Specific: Apple MPS

For Apple MPS, follow the instructions below to install from source. If the source tree still requires the alternate platform pyproject fallback, keep a backup of the default file before switching:
Command
# Install ffmpeg
brew install ffmpeg

# Install uv
brew install uv

# Clone the repository
git clone https://github.com/sgl-project/sglang.git
cd sglang

# Create and activate a virtual environment
uv venv -p 3.12 sglang-diffusion
source sglang-diffusion/bin/activate

# Install the Python packages
uv pip install --upgrade pip
mv python/pyproject.toml python/pyproject.toml.bak
cp python/pyproject_other.toml python/pyproject.toml
uv pip install -e "python[all_mps]"