Docker#

macOS and AMD GPU Users

We highly recommend to Install InvokeAI locally using these instructions, because Docker containers can not access the GPU on macOS.

AMD GPU Users

Container support for AMD GPUs has been reported to work by the community, but has not received extensive testing. Please make sure to set the GPU_DRIVER=rocm environment variable (see below), and use the build.sh script to build the image for this to take effect at build time.

Linux and Windows Users

For optimal performance, configure your Docker daemon to access your machine's GPU. Docker Desktop on Windows includes GPU support. Linux users should install and configure the NVIDIA Container Toolkit

Why containers?#

They provide a flexible, reliable way to build and deploy InvokeAI. See Processes under the Twelve-Factor App methodology for details on why running applications in such a stateless fashion is important.

The container is configured for CUDA by default, but can be built to support AMD GPUs by setting the GPU_DRIVER=rocm environment variable at Docker image build time.

Developers on Apple silicon (M1/M2/M3): You can't access your GPU cores from Docker containers and performance is reduced compared with running it directly on macOS but for development purposes it's fine. Once you're done with development tasks on your laptop you can build for the target platform and architecture and deploy to another environment with NVIDIA GPUs on-premises or in the cloud.

TL;DR#

This assumes properly configured Docker on Linux or Windows/WSL2. Read on for detailed customization options.

```bash
# docker compose commands should be run from the `docker` directory
cd docker
docker compose up
```

Installation in a Linux container (desktop)#

Prerequisites#

Install Docker #

On the Docker Desktop app, go to Preferences, Resources, Advanced. Increase the CPUs and Memory to avoid this Issue. You may need to increase Swap and Disk image size too.

Get a Huggingface-Token#

Besides the Docker Agent you will need an Account on huggingface.co.

After you succesfully registered your account, go to huggingface.co/settings/tokens, create a token and copy it, since you will need in for the next step.

Setup#

Set up your environmnent variables. In the docker directory, make a copy of .env.sample and name it .env. Make changes as necessary.

Any environment variables supported by InvokeAI can be set here - please see the CONFIGURATION for further detail.

At a minimum, you might want to set the INVOKEAI_ROOT environment variable to point to the location where you wish to store your InvokeAI models, configuration, and outputs.

Environment-Variable	Default value	Description
`INVOKEAI_ROOT`	`~/invokeai`	Required - the location of your InvokeAI root directory. It will be created if it does not exist.
`HUGGING_FACE_HUB_TOKEN`		InvokeAI will work without it, but some of the integrations with HuggingFace (like downloading from models from private repositories) may not work
`GPU_DRIVER`	`cuda`	Optionally change this to `rocm` to build the image for AMD GPUs. NOTE: Use the `build.sh` script to build the image for this to take effect.

Build the Image#

Use the standard docker compose build command from within the docker directory.

If using an AMD GPU: a: set the GPU_DRIVER=rocm environment variable in docker-compose.yml and continue using docker compose build as usual, or b: set GPU_DRIVER=rocm in the .env file and use the build.sh script, provided for convenience

Run the Container#

Use the standard docker compose up command, and generally the docker compose CLI as usual.

Once the container starts up (and configures the InvokeAI root directory if this is a new installation), you can access InvokeAI at http://localhost:9090

Troubleshooting / FAQ#

Q: I am running on Windows under WSL2, and am seeing a "no such file or directory" error.
A: Your docker-entrypoint.sh file likely has Windows (CRLF) as opposed to Unix (LF) line endings, and you may have cloned this repository before the issue was fixed. To solve this, please change the line endings in the docker-entrypoint.sh file to LF. You can do this in VSCode (Ctrl+P and search for "line endings"), or by using the dos2unix utility in WSL. Finally, you may delete docker-entrypoint.sh followed by git pull; git checkout docker/docker-entrypoint.sh to reset the file to its most recent version. For more information on this issue, please see the Docker Desktop documentation