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Table of Contents
Step 1 - Get the Model
Go to Hugging Face, and click "Access repository" to Download sd-v1-4.ckpt (~4 GB) to ~/Downloads.
You'll need to create an account but it's quick and free.
Step 2 - Installation
On a Linux container
Why containers?
They provide a flexible, reliable way to build and deploy Stable Diffusion. We also use a Docker volume to store the largest model file and image outputs as a first step in decoupling storage and compute. Future enhancements will do this for other model files and assets. See Processes under the Twelve-Factor App methodology for details on why running applications in such a stateless fashion is important.
This example uses a Mac M1/M2 (arm64) but you can specify the platform and architecture as parameters when building the image and running the container. You'll also need to specify the Stable Diffusion requirements file that matches your OS and architecture e.g. Linux on an arm64 chip if running a Linux container on Apple silicon.
The steps would be the same on an amd64 machine with NVIDIA GPUs as for an arm64 Mac; the platform is configurable. You can't access the Mac M1/M2 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 an environment with NVIDIA GPUs on-premises or in the cloud.
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.
Create a Docker volume for the downloaded model file
docker volume create my-vol
Copy the model file (we'll need it at run time) to the Docker volume using a lightweight Linux container. You just need to create the container with the mountpoint; no need to run it.
docker create --platform linux/arm64 --name dummy --mount source=my-vol,target=/data alpine
cd ~/Downloads # or wherever you saved sd-v1-4.ckpt
docker cp sd-v1-4.ckpt dummy:/data
Setup
Set the fork you want to use.
Download the Miniconda installer (we'll need it at build time). Replace the URL with the version matching your system.
GITHUB_STABLE_DIFFUSION="https://github.com/santisbon/stable-diffusion.git"
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.
Tip: Check that your shell session has the env variable set (above) with echo $GITHUB_STABLE_DIFFUSION.
condaarch will restrict the conda environment to the right architecture when installing packages. It can take on: linux-64, osx-64, osx-arm64.
docker build -t santisbon/stable-diffusion \
--platform linux/arm64 \
--build-arg condaarch="osx-arm64" \
--build-arg gsd=$GITHUB_STABLE_DIFFUSION \
--build-arg sdreq="requirements-linux-arm64.txt" \
.
Run a container using your built image e.g.
docker run -it \
--rm \
--platform linux/arm64 \
--name stable-diffusion \
--hostname stable-diffusion \
--mount source=my-vol,target=/data \
--expose 9090 \
--publish 9090:9090 \
santisbon/stable-diffusion
Tip: Make sure you've created the Docker volume (above)
Step 3 - Usage (time to have fun)
Startup
If you're on a Linux container the dream 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 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/.
python3 scripts/dream.py --full_precision
You'll get the script's prompt. You can see available options or quit.
dream> -h
dream> 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.
dream> The hulk fighting with sheldon cooper -s5 -n1
dream> "woman closeup highly detailed" -s 150
# Reuse previous seed and apply face restoration (if you installed GFPGAN)
dream> "woman closeup highly detailed" --steps 150 --seed -1 -G 0.75
You'll need to experiment to see if face restoration is making it better or worse for your specific prompt.
The -U option for upscaling has an Issue.
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.
On your host Mac (you can use the name of any container that mounted the volume):
docker cp dummy:/data/000001.928403745.png /Users/<your-user>/Pictures
Image to Image
You can also do text-guided image-to-image translation. For example, turning a sketch into a detailed drawing.
strength is a value between 0.0 and 1.0 that controls the amount of noise that is added to the input image. Values that approach 1.0 allow for lots of variations but will also produce images that are not semantically consistent with the input. 0.0 preserves image exactly, 1.0 replaces it completely.
Make sure your input image size dimensions are multiples of 64 e.g. 512x512. Otherwise you'll get Error: product of dimension sizes > 2**31'. If you still get the error try a different size like 512x256.
If you're on a Docker container, copy your input image into the Docker volume
docker cp /Users/<your-user>/Pictures/sketch-mountains-input.jpg dummy:/data/
Try it out generating an image (or more). The dream script needs absolute paths to find the image so don't use ~.
If you're on your Mac
dream> "A fantasy landscape, trending on artstation" -I /Users/<your-user>/Pictures/sketch-mountains-input.jpg --strength 0.75 --steps 100 -n4
If you're on a Linux container on your Mac
dream> "A fantasy landscape, trending on artstation" -I /data/sketch-mountains-input.jpg --strength 0.75 --steps 50 -n1
Web Interface
You can use the dream script with a graphical web interface. Start the web server with:
python3 scripts/dream.py --full_precision --web
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.
Notes
Some text you can add at the end of the prompt to make it very pretty:
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
The original scripts should work as well.
python3 scripts/orig_scripts/txt2img.py --help
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"
python3 scripts/orig_scripts/txt2img.py --ddim_steps 5 --n_iter 1 --n_samples 1 --plms --prompt "ocean" # or --klms