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Running Stable Diffusion WebUI Using Docker

This Docker environment is intended to speed up development and testing of Stable Diffusion WebUI features. Use of a container image format allows for packaging and isolation of Stable Diffusion / WebUI's dependencies separate from the Host environment.

You can use this Dockerfile to build a Docker image and run Stable Diffusion WebUI locally.

Requirements:

  • Host computer is AMD64 architecture (e.g. Intel/AMD x86 64-bit CPUs)
  • Host computer operating system (Linux or Windows with WSL2 enabled)
  • Host computer has Docker, or compatible container runtime
  • 10+ GB Free Disk Space (used by Docker base image, the Stable Diffusion WebUI Docker image for dependencies, model files/weights)

Additional Requirements:

Other Notes:

First-Time Startup Instructions

Clone Repository

  • Clone this repository to your host machine:
    • git clone https://github.com/sd-webui/stable-diffusion-webui.git
  • If you plan to use Docker Compose to run the image in a container (most users), create an .env_docker file using the example file:
    • cp .env_docker.example .env_docker
    • Edit .env_docker using the text editor of your choice.
    • Ensure VALIDATE_MODELS is set to true

Options available in .env_docker allow you to control automatic model file checking/download during startup, and to select the Stable Diffusion WebUI implementation to run (Gradio vs Streamlit). You may the set VALIDATE_MODELS option to false after the initial run (once models are downloaded) to speed up startup time.

Create a Container Instance Using Docker Compose

The default docker-compose.yml file will create a Docker container instance named sd-webui

  • Create an instance of the Stable Diffusion WebUI image as a Docker container:
    • docker compose up
  • During the first run, the container image will be build containing all of the dependencies necessary to run Stable Diffusion. This build process will take several minutes to complete
    • After the image build has completed, you will have a docker image for running the Stable Diffusion WebUI tagged stable-diffusion-webui:dev

(Optional) Daemon mode:

  • You can start the container in "daemon" mode by applying the -d option: docker compose up -d. This will run the server in the background so you can close your console window without losing your work.
  • When running in daemon mode, you can view logging output from your container by running docker logs sd-webui

Note: Depending on your version of Docker/Docker Compose installed, the command may be docker-compose (older versions) or docker compose (newer versions)

Accessing your Stable Diffusion WebUI Instance

The container may take several minutes to start up if model weights/checkpoints need to be downloaded. You can view progress via docker compose ps to see the current status or by checking the logs using docker compose logs.

Depending on the WebUI implementation you selected in .env_docker, you can access the WebUI at the following URLs:

You can expose and access your WebUI to/from remote hosts by the machine's IP address: (note: This generally does not apply to Windows/WSL2 users due to WSL's implementation)

  • Gradio: http://<host-ip-address>:7860
  • Streamlit: http://<host-ip-address>:8501

Where is ___ stored?

By default, model weights/checkpoint files will be stored at the following path:

  • ./model_cache/

Output files generated by Stable Diffusion will be stored at the following path:

  • ./output/

The above paths will be accessible directly from your Docker container's host.

Shutting down your Docker container

You can stop your Docker container by pressing the CTRL+C key combination in the terminal where the container was started..

If you started the container using docker compose, you can stop the container with the command:

  • docker compose down

Using the default configuration, your Stable Diffusion output, cached model weights/files, etc will persist between Docker container starts.

Resetting your Docker environment

Should you need to do so, the included docker-reset.sh script will remove all docker images, stopped containers, and cached model weights/checkpoints.

You will need to re-download all associated model files/weights used by Stable Diffusion WebUI, which total to several gigabytes of data. This will occur automatically upon the next startup.

Misc Related How-to

  • You can obtain shell access to a running Stable Diffusion WebUI container started with Docker Compose with either of the following commands:
    • docker exec -it st-webui /bin/bash
    • docker compose exec stable-diffusion bash
  • To start a container using the Stable Diffusion WebUI Docker image without Docker Compose, you can do so with the following command:
    • docker run --rm -it --entrypoint /bin/bash stable-diffusion-webui:dev
  • To start a container, with mapped ports, GPU resource access, and a local directory bound as a container volume, you can do so with the following command:
    • docker run --rm -it -p 8501:8501 -p 7860:7860 --gpus all -v $(pwd):/sd --entrypoint /bin/bash stable-diffusion-webui:dev

Dockerfile Implementation Notes

Compared to base Stable Diffusion distribution, Conda-based package management was removed.

The Pytorch base image with Nvidia CUDA support is used as the base Docker image to simplify dependencies.

Python dependency requirements for various packages used by Stable Diffusion WebUI have been separated into different groups. During the container image build process, requirements are installed in the following order:

  1. Stable Diffusion (core) requirements (sd_requirements.txt)
  2. General Requirements (requirements.txt)
  3. External optional packages requirements (ext_requirements.txt)
  4. WebUI requirements (ui_requirements.txt)

Python package dependencies have been version-pinned where possible.

Developers: When developing new features or making changes to the environment that require dependency changes, please update and make notes in the appropriate file to help us better track and manage dependencies.

Other Notes

  • The root_profile Docker Volume
    • The huggingface/transformers package will download files to a cache located at /root/.cache/huggingface/transformers totalling nearly ~1.6 GB