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8cd5d95b8a
- this required an update to the invoke-ai fork of gfpgan - simultaneously reverted consolidation of environment and requirements files, as their presence in a directory triggered setup.py to try to install a sub-package.
224 lines
9.1 KiB
Markdown
224 lines
9.1 KiB
Markdown
---
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title: Docker
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---
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# :fontawesome-brands-docker: Docker
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## Before you begin
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- For end users: Install Stable Diffusion locally using the instructions for
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your OS.
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- For developers: For container-related development tasks or for enabling easy
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deployment to other environments (on-premises or cloud), follow these
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instructions. For general use, install locally to leverage your machine's GPU.
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## Why containers?
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They provide a flexible, reliable way to build and deploy Stable Diffusion.
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You'll also use a Docker volume to store the largest model files and image
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outputs as a first step in decoupling storage and compute. Future enhancements
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can do this for other assets. See [Processes](https://12factor.net/processes)
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under the Twelve-Factor App methodology for details on why running applications
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in such a stateless fashion is important.
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You can specify the target platform when building the image and running the
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container. You'll also need to specify the Stable Diffusion requirements file
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that matches the container's OS and the architecture it will run on.
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Developers on Apple silicon (M1/M2): You
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[can't access your GPU cores from Docker containers](https://github.com/pytorch/pytorch/issues/81224)
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and performance is reduced compared with running it directly on macOS but for
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development purposes it's fine. Once you're done with development tasks on your
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laptop you can build for the target platform and architecture and deploy to
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another environment with NVIDIA GPUs on-premises or in the cloud.
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## Installation on a Linux container
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### Prerequisites
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#### Install [Docker](https://github.com/santisbon/guides#docker)
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On the Docker Desktop app, go to Preferences, Resources, Advanced. Increase the
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CPUs and Memory to avoid this
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[Issue](https://github.com/invoke-ai/InvokeAI/issues/342). You may need to
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increase Swap and Disk image size too.
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#### Get a Huggingface-Token
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Go to [Hugging Face](https://huggingface.co/settings/tokens), create a token and
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temporary place it somewhere like a open texteditor window (but dont save it!,
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only keep it open, we need it in the next step)
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### Setup
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Set the fork you want to use and other variables.
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!!! tip
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I preffer to save my env vars
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in the repository root in a `.env` (or `.envrc`) file to automatically re-apply
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them when I come back.
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The build- and run- scripts contain default values for almost everything,
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besides the [Hugging Face Token](https://huggingface.co/settings/tokens) you
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created in the last step.
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Some Suggestions of variables you may want to change besides the Token:
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| Environment-Variable | Description |
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| ------------------------------------------------------------------- | ------------------------------------------------------------------------ |
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| `HUGGINGFACE_TOKEN="hg_aewirhghlawrgkjbarug2"` | This is the only required variable, without you can't get the checkpoint |
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| `ARCH=aarch64` | if you are using a ARM based CPU |
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| `INVOKEAI_TAG=yourname/invokeai:latest` | the Container Repository / Tag which will be used |
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| `INVOKEAI_CONDA_ENV_FILE=environment-linux-aarch64.yml` | since environment.yml wouldn't work with aarch |
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| `INVOKEAI_GIT="-b branchname https://github.com/username/reponame"` | if you want to use your own fork |
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#### Build the Image
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I provided a build script, which is located in `docker-build/build.sh` but still
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needs to be executed from the Repository root.
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```bash
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docker-build/build.sh
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```
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The build Script not only builds the container, but also creates the docker
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volume if not existing yet, or if empty it will just download the models. When
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it is done you can run the container via the run script
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```bash
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docker-build/run.sh
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```
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When used without arguments, the container will start the website and provide
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you the link to open it. But if you want to use some other parameters you can
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also do so.
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!!! warning "Deprecated"
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From here on it is the rest of the previous Docker-Docs, which will still
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provide usefull informations for one or the other.
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## Usage (time to have fun)
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### Startup
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If you're on a **Linux container** the `invoke` script is **automatically
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started** and the output dir set to the Docker volume you created earlier.
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If you're **directly on macOS follow these startup instructions**.
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With the Conda environment activated (`conda activate ldm`), run the interactive
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interface that combines the functionality of the original scripts `txt2img` and
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`img2img`:
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Use the more accurate but VRAM-intensive full precision math because
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half-precision requires autocast and won't work.
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By default the images are saved in `outputs/img-samples/`.
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```Shell
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python3 scripts/invoke.py --full_precision
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```
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You'll get the script's prompt. You can see available options or quit.
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```Shell
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invoke> -h
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invoke> q
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```
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### Text to Image
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For quick (but bad) image results test with 5 steps (default 50) and 1 sample
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image. This will let you know that everything is set up correctly.
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Then increase steps to 100 or more for good (but slower) results.
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The prompt can be in quotes or not.
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```Shell
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invoke> The hulk fighting with sheldon cooper -s5 -n1
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invoke> "woman closeup highly detailed" -s 150
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# Reuse previous seed and apply face restoration
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invoke> "woman closeup highly detailed" --steps 150 --seed -1 -G 0.75
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```
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You'll need to experiment to see if face restoration is making it better or
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worse for your specific prompt.
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If you're on a container the output is set to the Docker volume. You can copy it
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wherever you want.
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You can download it from the Docker Desktop app, Volumes, my-vol, data.
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Or you can copy it from your Mac terminal. Keep in mind `docker cp` can't expand
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`*.png` so you'll need to specify the image file name.
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On your host Mac (you can use the name of any container that mounted the
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volume):
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```Shell
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docker cp dummy:/data/000001.928403745.png /Users/<your-user>/Pictures
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```
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### Image to Image
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You can also do text-guided image-to-image translation. For example, turning a
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sketch into a detailed drawing.
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`strength` is a value between 0.0 and 1.0 that controls the amount of noise that
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is added to the input image. Values that approach 1.0 allow for lots of
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variations but will also produce images that are not semantically consistent
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with the input. 0.0 preserves image exactly, 1.0 replaces it completely.
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Make sure your input image size dimensions are multiples of 64 e.g. 512x512.
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Otherwise you'll get `Error: product of dimension sizes > 2**31'`. If you still
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get the error
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[try a different size](https://support.apple.com/guide/preview/resize-rotate-or-flip-an-image-prvw2015/mac#:~:text=image's%20file%20size-,In%20the%20Preview%20app%20on%20your%20Mac%2C%20open%20the%20file,is%20shown%20at%20the%20bottom.)
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like 512x256.
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If you're on a Docker container, copy your input image into the Docker volume
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```Shell
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docker cp /Users/<your-user>/Pictures/sketch-mountains-input.jpg dummy:/data/
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```
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Try it out generating an image (or more). The `invoke` script needs absolute
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paths to find the image so don't use `~`.
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If you're on your Mac
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```Shell
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invoke> "A fantasy landscape, trending on artstation" -I /Users/<your-user>/Pictures/sketch-mountains-input.jpg --strength 0.75 --steps 100 -n4
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```
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If you're on a Linux container on your Mac
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```Shell
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invoke> "A fantasy landscape, trending on artstation" -I /data/sketch-mountains-input.jpg --strength 0.75 --steps 50 -n1
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```
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### Web Interface
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You can use the `invoke` script with a graphical web interface. Start the web
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server with:
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```Shell
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python3 scripts/invoke.py --full_precision --web
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```
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If it's running on your Mac point your Mac web browser to
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<http://127.0.0.1:9090>
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Press Control-C at the command line to stop the web server.
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### Notes
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Some text you can add at the end of the prompt to make it very pretty:
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```Shell
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cinematic photo, highly detailed, cinematic lighting, ultra-detailed, ultrarealistic, photorealism, Octane Rendering, cyberpunk lights, Hyper Detail, 8K, HD, Unreal Engine, V-Ray, full hd, cyberpunk, abstract, 3d octane render + 4k UHD + immense detail + dramatic lighting + well lit + black, purple, blue, pink, cerulean, teal, metallic colours, + fine details, ultra photoreal, photographic, concept art, cinematic composition, rule of thirds, mysterious, eerie, photorealism, breathtaking detailed, painting art deco pattern, by hsiao, ron cheng, john james audubon, bizarre compositions, exquisite detail, extremely moody lighting, painted by greg rutkowski makoto shinkai takashi takeuchi studio ghibli, akihiko yoshida
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```
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The original scripts should work as well.
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```Shell
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python3 scripts/orig_scripts/txt2img.py --help
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python3 scripts/orig_scripts/txt2img.py --ddim_steps 100 --n_iter 1 --n_samples 1 --plms --prompt "new born baby kitten. Hyper Detail, Octane Rendering, Unreal Engine, V-Ray"
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python3 scripts/orig_scripts/txt2img.py --ddim_steps 5 --n_iter 1 --n_samples 1 --plms --prompt "ocean" # or --klms
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```
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