diff --git a/docs/installation/040_INSTALL_DOCKER.md b/docs/installation/040_INSTALL_DOCKER.md
index c7c2d6adae..5896adf302 100644
--- a/docs/installation/040_INSTALL_DOCKER.md
+++ b/docs/installation/040_INSTALL_DOCKER.md
@@ -16,10 +16,6 @@ title: Installing with Docker
For general use, install locally to leverage your machine's GPU.
-!!! tip "For running on a cloud instance/service"
-
- Check out the [Running InvokeAI in the cloud with Docker](#running-invokeai-in-the-cloud-with-docker) section below
-
## Why containers?
They provide a flexible, reliable way to build and deploy InvokeAI. You'll also
@@ -78,16 +74,18 @@ Some Suggestions of variables you may want to change besides the Token:
-| Environment-Variable | Default value | Description |
-| -------------------- | ----------------------------- | -------------------------------------------------------------------------------------------- |
-| `HUGGINGFACE_TOKEN` | No default, but **required**! | This is the only **required** variable, without it you can't download the huggingface models |
-| `REPOSITORY_NAME` | The Basename of the Repo folder | This name will used as the container repository/image name |
-| `VOLUMENAME` | `${REPOSITORY_NAME,,}_data` | Name of the Docker Volume where model files will be stored |
-| `ARCH` | arch of the build machine | can be changed if you want to build the image for another arch |
-| `INVOKEAI_TAG` | latest | the Container Repository / Tag which will be used |
-| `PIP_REQUIREMENTS` | `requirements-lin-cuda.txt` | the requirements file to use (from `environments-and-requirements`) |
-| `CONTAINER_FLAVOR` | cuda | the flavor of the image, which can be changed if you build f.e. with amd requirements file. |
-| `INVOKE_DOCKERFILE` | `docker-build/Dockerfile` | the Dockerfile which should be built, handy for development |
+| Environment-Variable | Default value | Description |
+| --------------------- | ------------------------------- | -------------------------------------------------------------------------------------------- |
+| `HUGGINGFACE_TOKEN` | No default, but **required**! | This is the only **required** variable, without it you can't download the huggingface models |
+| `REPOSITORY_NAME` | The Basename of the Repo folder | This name will used as the container repository/image name |
+| `VOLUMENAME` | `${REPOSITORY_NAME,,}_data` | Name of the Docker Volume where model files will be stored |
+| `ARCH` | arch of the build machine | can be changed if you want to build the image for another arch |
+| `INVOKEAI_TAG` | latest | the Container Repository / Tag which will be used |
+| `PIP_REQUIREMENTS` | `requirements-lin-cuda.txt` | the requirements file to use (from `environments-and-requirements`) |
+| `CONTAINER_FLAVOR` | cuda | the flavor of the image, which can be changed if you build f.e. with amd requirements file. |
+| `INVOKE_DOCKERFILE` | `docker-build/Dockerfile` | the Dockerfile which should be built, handy for development |
+| `CONTAINER_FLAVOR` | | the flavor of the image to built, available options are `cuda`, `rocm` and `cpu` |
+| `PIP_EXTRA_INDEX_URL` | | if you want to use a custom pip-extra-index-url |
@@ -130,8 +128,9 @@ also do so.
## Running the container on your GPU
-If you have an Nvidia GPU, you can enable InvokeAI to run on the GPU by running the container with an extra
-environment variable to enable GPU usage and have the process run much faster:
+If you have an Nvidia GPU, you can enable InvokeAI to run on the GPU by running
+the container with an extra environment variable to enable GPU usage and have
+the process run much faster:
```bash
GPU_FLAGS=all ./docker-build/run.sh
@@ -139,7 +138,8 @@ GPU_FLAGS=all ./docker-build/run.sh
This passes the `--gpus all` to docker and uses the GPU.
-If you don't have a GPU (or your host is not yet setup to use it) you will see a message like this:
+If you don't have a GPU (or your host is not yet setup to use it) you will see a
+message like this:
`docker: Error response from daemon: could not select device driver "" with capabilities: [[gpu]].`
@@ -147,84 +147,8 @@ You can use the full set of GPU combinations documented here:
https://docs.docker.com/config/containers/resource_constraints/#gpu
-For example, use `GPU_FLAGS=device=GPU-3a23c669-1f69-c64e-cf85-44e9b07e7a2a` to choose a specific device identified by a UUID.
-
-## Running InvokeAI in the cloud with Docker
-
-We offer an optimized Ubuntu-based image that has been well-tested in cloud deployments. Note: it also works well locally on Linux x86_64 systems with an Nvidia GPU. It *may* also work on Windows under WSL2 and on Intel Mac (not tested).
-
-An advantage of this method is that it does not need any local setup or additional dependencies.
-
-See the `docker-build/Dockerfile.cloud` file to familizarize yourself with the image's content.
-
-### Prerequisites
-
-- a `docker` runtime
-- `make` (optional but helps for convenience)
-- Huggingface token to download models, or an existing InvokeAI runtime directory from a previous installation
-
- Neither local Python nor any dependencies are required. If you don't have `make` (part of `build-essentials` on Ubuntu), or do not wish to install it, the commands from the `docker-build/Makefile` are readily adaptable to be executed directly.
-
-### Building and running the image locally
-
-1. Clone this repo and `cd docker-build`
-1. `make build` - this will build the image. (This does *not* require a GPU-capable system).
-1. _(skip this step if you already have a complete InvokeAI runtime directory)_
- - `make configure` (This does *not* require a GPU-capable system)
- - this will create a local cache of models and configs (a.k.a the _runtime dir_)
- - enter your Huggingface token when prompted
-1. `make web`
-1. Open the `http://localhost:9090` URL in your browser, and enjoy the banana sushi!
-
-To use InvokeAI on the cli, run `make cli`. To open a Bash shell in the container for arbitraty advanced use, `make shell`.
-
-#### Building and running without `make`
-
-(Feel free to adapt paths such as `${HOME}/invokeai` to your liking, and modify the CLI arguments as necessary).
-
-!!! example "Build the image and configure the runtime directory"
- ```Shell
- cd docker-build
-
- DOCKER_BUILDKIT=1 docker build -t local/invokeai:latest -f Dockerfile.cloud ..
-
- docker run --rm -it -v ${HOME}/invokeai:/mnt/invokeai local/invokeai:latest -c "python scripts/configure_invokeai.py"
- ```
-
-!!! example "Run the web server"
- ```Shell
- docker run --runtime=nvidia --gpus=all --rm -it -v ${HOME}/invokeai:/mnt/invokeai -p9090:9090 local/invokeai:latest
- ```
-
- Access the Web UI at http://localhost:9090
-
-!!! example "Run the InvokeAI interactive CLI"
- ```
- docker run --runtime=nvidia --gpus=all --rm -it -v ${HOME}/invokeai:/mnt/invokeai local/invokeai:latest -c "python scripts/invoke.py"
- ```
-
-### Running the image in the cloud
-
-This image works anywhere you can run a container with a mounted Docker volume. You may either build this image on a cloud instance, or build and push it to your Docker registry. To manually run this on a cloud instance (such as AWS EC2, GCP or Azure VM):
-
-1. build this image either in the cloud (you'll need to pull the repo), or locally
-1. `docker tag` it as `your-registry/invokeai` and push to your registry (i.e. Dockerhub)
-1. `docker pull` it on your cloud instance
-1. configure the runtime directory as per above example, using `docker run ... configure_invokeai.py` script
-1. use either one of the `docker run` commands above, substituting the image name for your own image.
-
-To run this on Runpod, please refer to the following Runpod template: https://www.runpod.io/console/gpu-secure-cloud?template=vm19ukkycf (you need a Runpod subscription). When launching the template, feel free to set the image to pull your own build.
-
-The template's `README` provides ample detail, but at a high level, the process is as follows:
-
-1. create a pod using this Docker image
-1. ensure the pod has an `INVOKEAI_ROOT=` environment variable, and that it corresponds to the path to your pod's persistent volume mount
-1. Run the pod with `sleep infinity` as the Docker command
-1. Use Runpod basic SSH to connect to the pod, and run `python scripts/configure_invokeai.py` script
-1. Stop the pod, and change the Docker command to `python scripts/invoke.py --web --host 0.0.0.0`
-1. Run the pod again, connect to your pod on HTTP port 9090, and enjoy the banana sushi!
-
-Running on other cloud providers such as Vast.ai will likely work in a similar fashion.
+For example, use `GPU_FLAGS=device=GPU-3a23c669-1f69-c64e-cf85-44e9b07e7a2a` to
+choose a specific device identified by a UUID.
---
@@ -240,13 +164,12 @@ Running on other cloud providers such as Vast.ai will likely work in a similar f
If you're on a **Linux container** the `invoke` script is **automatically
started** and the output dir set to the Docker volume you created earlier.
-If you're **directly on macOS follow these startup instructions**.
-With the Conda environment activated (`conda activate ldm`), run the interactive
+If you're **directly on macOS follow these startup instructions**. With the
+Conda environment activated (`conda activate ldm`), run the interactive
interface that combines the functionality of the original scripts `txt2img` and
-`img2img`:
-Use the more accurate but VRAM-intensive full precision math because
-half-precision requires autocast and won't work.
-By default the images are saved in `outputs/img-samples/`.
+`img2img`: Use the more accurate but VRAM-intensive full precision math because
+half-precision requires autocast and won't work. By default the images are saved
+in `outputs/img-samples/`.
```Shell
python3 scripts/invoke.py --full_precision
@@ -262,9 +185,9 @@ invoke> q
### Text to Image
For quick (but bad) image results test with 5 steps (default 50) and 1 sample
-image. This will let you know that everything is set up correctly.
-Then increase steps to 100 or more for good (but slower) results.
-The prompt can be in quotes or not.
+image. This will let you know that everything is set up correctly. Then increase
+steps to 100 or more for good (but slower) results. The prompt can be in quotes
+or not.
```Shell
invoke> The hulk fighting with sheldon cooper -s5 -n1
@@ -277,10 +200,9 @@ You'll need to experiment to see if face restoration is making it better or
worse for your specific prompt.
If you're on a container the output is set to the Docker volume. You can copy it
-wherever you want.
-You can download it from the Docker Desktop app, Volumes, my-vol, data.
-Or you can copy it from your Mac terminal. Keep in mind `docker cp` can't expand
-`*.png` so you'll need to specify the image file name.
+wherever you want. You can download it from the Docker Desktop app, Volumes,
+my-vol, data. Or you can copy it from your Mac terminal. Keep in mind
+`docker cp` can't expand `*.png` so you'll need to specify the image file name.
On your host Mac (you can use the name of any container that mounted the
volume):