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update docker docs

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mauwii 2022-10-25 02:50:04 +02:00 committed by Lincoln Stein
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docs/installation/INSTALL_DOCKER.md
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@ -36,20 +36,6 @@ another environment with NVIDIA GPUs on-premises or in the cloud.
### Prerequisites
#### Get the data files
Go to
[Hugging Face](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original),
and click "Access repository" to Download the model file `sd-v1-4.ckpt` (~4 GB)
to `~/Downloads`. You'll need to create an account but it's quick and free.
Also download the face restoration model.
```Shell
cd ~/Downloads
wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth
```
#### Install [Docker](https://github.com/santisbon/guides#docker)
On the Docker Desktop app, go to Preferences, Resources, Advanced. Increase the
@ -57,86 +43,61 @@ CPUs and Memory to avoid this
[Issue](https://github.com/invoke-ai/InvokeAI/issues/342). You may need to
increase Swap and Disk image size too.
#### Get a Huggingface-Token
Go to [Hugging Face](https://huggingface.co/settings/tokens), create a token and
temporary place it somewhere like a open texteditor window (but dont save it!,
only keep it open, we need it in the next step)
### Setup
Set the fork you want to use and other variables.
```Shell
TAG_STABLE_DIFFUSION="santisbon/stable-diffusion"
PLATFORM="linux/arm64"
GITHUB_STABLE_DIFFUSION="-b orig-gfpgan https://github.com/santisbon/stable-diffusion.git"
REQS_STABLE_DIFFUSION="requirements-linux-arm64.txt"
CONDA_SUBDIR="osx-arm64"
!!! tip
echo $TAG_STABLE_DIFFUSION
echo $PLATFORM
echo $GITHUB_STABLE_DIFFUSION
echo $REQS_STABLE_DIFFUSION
echo $CONDA_SUBDIR
I preffer to save my env vars
in the repository root in a `.env` (or `.envrc`) file to automatically re-apply
them when I come back.
The build- and run- scripts contain default values for almost everything,
besides the [Hugging Face Token](https://huggingface.co/settings/tokens) you
created in the last step.
Some Suggestions of variables you may want to change besides the Token:
| Environment-Variable | Description |
| ------------------------------------------------------------------- | ------------------------------------------------------------------------ |
| `HUGGINGFACE_TOKEN="hg_aewirhghlawrgkjbarug2"` | This is the only required variable, without you can't get the checkpoint |
| `ARCH=aarch64` | if you are using a ARM based CPU |
| `INVOKEAI_TAG=yourname/invokeai:latest` | the Container Repository / Tag which will be used |
| `INVOKEAI_CONDA_ENV_FILE=environment-linux-aarch64.yml` | since environment.yml wouldn't work with aarch |
| `INVOKEAI_GIT="-b branchname https://github.com/username/reponame"` | if you want to use your own fork |
#### Build the Image
I provided a build script, which is located in `docker-build/build.sh` but still
needs to be executed from the Repository root.
```bash
docker-build/build.sh
```
Create a Docker volume for the downloaded model files.
The build Script not only builds the container, but also creates the docker
volume if not existing yet, or if empty it will just download the models. When
it is done you can run the container via the run script
```Shell
docker volume create my-vol
```bash
docker-build/run.sh
```
Copy the data files to the Docker volume using a lightweight Linux container.
We'll need the models at run time. You just need to create the container with
the mountpoint; no need to run this dummy container.
When used without arguments, the container will start the website and provide
you the link to open it. But if you want to use some other parameters you can
also do so.
```Shell
cd ~/Downloads # or wherever you saved the files
!!! warning "Deprecated"
docker create --platform $PLATFORM --name dummy --mount source=my-vol,target=/data alpine
docker cp sd-v1-4.ckpt dummy:/data
docker cp GFPGANv1.4.pth dummy:/data
```
Get the repo and download the Miniconda installer (we'll need it at build time).
Replace the URL with the version matching your container OS and the architecture
it will run on.
```Shell
cd ~
git clone $GITHUB_STABLE_DIFFUSION
cd stable-diffusion/docker-build
chmod +x entrypoint.sh
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-aarch64.sh -O anaconda.sh && chmod +x anaconda.sh
```
Build the Docker image. Give it any tag `-t` that you want.
Choose the Linux container's host platform: x86-64/Intel is `amd64`. Apple
silicon is `arm64`. If deploying the container to the cloud to leverage powerful
GPU instances you'll be on amd64 hardware but if you're just trying this out
locally on Apple silicon choose arm64.
The application uses libraries that need to match the host environment so use
the appropriate requirements file.
Tip: Check that your shell session has the env variables set above.
```Shell
docker build -t $TAG_STABLE_DIFFUSION \
--platform $PLATFORM \
--build-arg gsd=$GITHUB_STABLE_DIFFUSION \
--build-arg rsd=$REQS_STABLE_DIFFUSION \
--build-arg cs=$CONDA_SUBDIR \
.
```
Run a container using your built image.
Tip: Make sure you've created and populated the Docker volume (above).
```Shell
docker run -it \
--rm \
--platform $PLATFORM \
--name stable-diffusion \
--hostname stable-diffusion \
--mount source=my-vol,target=/data \
$TAG_STABLE_DIFFUSION
```
From here on it is the rest of the previous Docker-Docs, which will still
provide usefull informations for one or the other.
## Usage (time to have fun)
@ -240,7 +201,8 @@ server with:
python3 scripts/invoke.py --full_precision --web
```
If it's running on your Mac point your Mac web browser to http://127.0.0.1:9090
If it's running on your Mac point your Mac web browser to
<http://127.0.0.1:9090>
Press Control-C at the command line to stop the web server.