InvokeAI/docker/README.md

# InvokeAI Containerized

All commands should be run within the `docker` directory: `cd docker`

## Quickstart :rocket:

On a known working Linux+Docker+CUDA (Nvidia) system, execute `./run.sh` in this directory. It will take a few minutes - depending on your internet speed - to install the core models. Once the application starts up, open `http://localhost:9090` in your browser to Invoke!

For more configuration options (using an AMD GPU, custom root directory location, etc): read on.

## Detailed setup

#### Linux

1. Ensure builkit is enabled in the Docker daemon settings (`/etc/docker/daemon.json`)
2. Install the `docker compose` plugin using your package manager, or follow a [tutorial](https://docs.docker.com/compose/install/linux/#install-using-the-repository).
    - The deprecated `docker-compose` (hyphenated) CLI continues to work for now.
3. Ensure docker daemon is able to access the GPU.
    - You may need to install [nvidia-container-toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html)

#### macOS

1. Ensure Docker has at least 16GB RAM
2. Enable VirtioFS for file sharing
3. Enable `docker compose` V2 support

This is done via Docker Desktop preferences

### Configure Invoke environment

1. Make a copy of `.env.sample` and name it `.env` (`cp .env.sample .env` (Mac/Linux) or `copy example.env .env` (Windows)). Make changes as necessary. Set `INVOKEAI_ROOT` to an absolute path to:
    a. the desired location of the InvokeAI runtime directory, or
    b. an existing, v3.0.0 compatible runtime directory.
1. Execute `run.sh`

The image will be built automatically if needed.

The runtime directory (holding models and outputs) will be created in the location specified by `INVOKEAI_ROOT`. The default location is `~/invokeai`. The runtime directory will be populated with the base configs and models necessary to start generating.

### Use a GPU

- Linux is *recommended* for GPU support in Docker.
- WSL2 is *required* for Windows.
- only `x86_64` architecture is supported.

The Docker daemon on the system must be already set up to use the GPU. In case of Linux, this involves installing `nvidia-docker-runtime` and configuring the `nvidia` runtime as default. Steps will be different for AMD. Please see Docker documentation for the most up-to-date instructions for using your GPU with Docker.

To use an AMD GPU, set `GPU_DRIVER=rocm` in your `.env` file.

## Customize

Check the `.env.sample` file. It contains some environment variables for running in Docker. Copy it, name it `.env`, and fill it in with your own values. Next time you run `run.sh`, your custom values will be used.

You can also set these values in `docker-compose.yml` directly, but `.env` will help avoid conflicts when code is updated.

Values are optional, but setting `INVOKEAI_ROOT` is highly recommended. The default is `~/invokeai`. Example:

```bash
INVOKEAI_ROOT=/Volumes/WorkDrive/invokeai
HUGGINGFACE_TOKEN=the_actual_token
CONTAINER_UID=1000
GPU_DRIVER=nvidia
```

Any environment variables supported by InvokeAI can be set here - please see the [Configuration docs](https://invoke-ai.github.io/InvokeAI/features/CONFIGURATION/) for further detail.

## Even More Customizing!

See the `docker-compose.yml` file. The `command` instruction can be uncommented and used to run arbitrary startup commands. Some examples below.

### Reconfigure the runtime directory

Can be used to download additional models from the supported model list

In conjunction with `INVOKEAI_ROOT` can be also used to initialize a runtime directory

```yaml
command:
  - invokeai-configure
  - --yes
```

Or install models:

```yaml
command:
  - invokeai-model-install
```