The easiest way to run Stable Diffusion WebUI is to use the prebuilt image from Docker Hub.
```bash
docker pull hlky/sd-webui:runpod
```
This image has all the necessary models baked in. It is quite large but streamlines the process of managing the various models and simplifies the user experience.
Alternatively you can pull:
```bash
docker pull hlky/sd-webui:latest
```
This image includes the babrebones environment to run the Web UI. The models will be downloaded during the installation process. You will have to take care of the volume for the `sd/models` directory.
It is recommended that you run the `runpod` version.
You can run the image using the following command:
```bash
docker container run --rm -d -p 8501:8501 -e STREAMLIT_SERVER_HEADLESS=true -e "WEBUI_SCRIPT=webui_streamlit.py" -e "VALIDATE_MODELS=false" -v "${PWD}/outputs:/sd/outputs" --gpus all hlky/sd-webui:runpod
```
> Note: if you are running it on runpod it only supports one volume mount which is used for your outputs.
> Note: if you are running it on your local machine the output directory will be created in the current directory from where you run this command.
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)
* See [Microsoft WSL2 Installation Guide for Windows 10] (https://learn.microsoft.com/en-us/windows/wsl/) for more information on installing.
* Ubuntu (Default) for WSL2 is recommended for Windows users
* Host computer has Docker, or compatible container runtime
* Docker Compose (v1.29+) or later
* See [Install Docker Engine] (https://docs.docker.com/engine/install/#supported-platforms) to learn more about installing Docker on your Linux operating system
* 10+ GB Free Disk Space (used by Docker base image, the Stable Diffusion WebUI Docker image for dependencies, model files/weights)
Additional Requirements:
* Host computer is equipped with a CUDA-compatible GPU (e.g. Nvidia RTX 2xxx, 3000x)
* NVIDIA Container Toolkit is installed
* See [NVIDIA Container Toolkit Installation Guide] (https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#supported-platforms "Official NVIDIA Installation Guide") for more information on installing.
Other Notes:
* "Optional" packages commonly used with Stable Diffusion WebUI workflows such as, RealESRGAN, GFPGAN, will be installed by default.
> 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.
* 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
* 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.
> Note: Depending on your version of Docker/Docker Compose installed, the command may be `docker-compose` (older versions) or `docker compose` (newer versions)
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:
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.
* 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:
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:
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
