---
title: Install SGLang Diffusion
description: Install SGLang Diffusion on NVIDIA, AMD, MUSA, and Ascend platforms.
---

You can install SGLang Diffusion using one of the methods below.

## Standard installation (NVIDIA GPUs)

**Platform:** NVIDIA GPUs (CUDA)

<Tabs>
  <Tab title="Pip or uv">
    Use `uv` for faster installation:

    ```bash
    pip install --upgrade pip
    pip install uv
    uv pip install "sglang[diffusion]" --prerelease=allow
    ```
  </Tab>

  <Tab title="From source">
    ```bash
    git clone https://github.com/sgl-project/sglang.git
    cd sglang
    pip install --upgrade pip
    pip install -e "python[diffusion]"
    ```

    Or with `uv`:

    ```bash
    uv pip install -e "python[diffusion]" --prerelease=allow
    ```
  </Tab>

  <Tab title="Docker">
    The Docker images are available on Docker Hub at [lmsysorg/sglang](https://hub.docker.com/r/lmsysorg/sglang/tags), built from the [Dockerfile](https://github.com/sgl-project/sglang/blob/main/docker/Dockerfile). Replace `<secret>` below with your HuggingFace Hub [token](https://huggingface.co/docs/hub/en/security-tokens).

    ```bash
    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 \
        '
    ```
  </Tab>
</Tabs>

## Platform-specific installs

Use the tab that matches your accelerator.

<Tabs>
  <Tab title="ROCm (AMD GPUs)">
    **Platform:** AMD Instinct GPUs (ROCm)

    For AMD Instinct GPUs (for example, MI300X), use the ROCm-enabled Docker image:

    ```bash
    docker run --device=/dev/kfd --device=/dev/dri --ipc=host \
      -v ~/.cache/huggingface:/root/.cache/huggingface \
      --env HF_TOKEN=<secret> \
      lmsysorg/sglang:v0.5.9-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](../hardware-platforms/amd-gpus).
  </Tab>

  <Tab title="MUSA (Moore Threads GPUs)">
    **Platform:** Moore Threads GPUs (MUSA)

    For Moore Threads GPUs (MTGPU) with the MUSA software stack:

    ```bash
    git clone https://github.com/sgl-project/sglang.git
    cd sglang
    pip install --upgrade pip
    rm -f python/pyproject.toml && mv python/pyproject_other.toml python/pyproject.toml
    pip install -e "python[all_musa]"
    ```
  </Tab>

  <Tab title="Ascend NPU">
    **Platform:** Ascend NPU

    For Ascend NPU, follow the [NPU installation guide](../hardware-platforms/ascend-npus/SGLang-installation-with-NPUs-support).

    Quick test:

    ```bash
    sglang generate --model-path black-forest-labs/FLUX.1-dev \
        --prompt "A logo With Bold Large text: SGL Diffusion" \
        --save-output
    ```
  </Tab>
</Tabs>