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For end users

We highly recommend to Install InvokeAI locally using these instructions

For developers

For container-related development tasks or for enabling easy deployment to other environments (on-premises or cloud), follow these instructions.

For general use, install locally to leverage your machine's GPU.

Why containers?#

They provide a flexible, reliable way to build and deploy InvokeAI. You'll also use a Docker volume to store the largest model files and image outputs as a first step in decoupling storage and compute. Future enhancements can do this for other assets. See Processes under the Twelve-Factor App methodology for details on why running applications in such a stateless fashion is important.

You can specify the target platform when building the image and running the container. You'll also need to specify the InvokeAI requirements file that matches the container's OS and the architecture it will run on.

Developers on Apple silicon (M1/M2): You can't access your 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 another environment with NVIDIA GPUs on-premises or in the cloud.

Installation in a Linux container (desktop)#


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.

Get a Huggingface-Token#

Besides the Docker Agent you will need an Account on

After you succesfully registered your account, go to, create a token and copy it, since you will need in for the next step.


Set the fork you want to use and other variables.


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 you created in the last step.

Some Suggestions of variables you may want to change besides the Token:

Environment-Variable Default value Description
HUGGING_FACE_HUB_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
CONTAINER_REGISTRY Name of the Container Registry to use for the full tag
CONTAINER_REPOSITORY $(whoami)/${REPOSITORY_NAME} Name of the Container Repository
CONTAINER_FLAVOR cuda The flavor of the image to built, available options are cuda, rocm and cpu. If you choose rocm or cpu, the extra-index-url will be selected automatically, unless you set one yourself.
CONTAINER_TAG ${INVOKEAI_BRANCH##*/}-${CONTAINER_FLAVOR} The Container Repository / Tag which will be used
INVOKE_DOCKERFILE Dockerfile The Dockerfile which should be built, handy for development
PIP_EXTRA_INDEX_URL If you want to use a custom pip-extra-index-url

Build the Image#

I provided a build script, which is located next to the Dockerfile in docker/ It can be executed from repository root like this:


The build Script not only builds the container, but also creates the docker volume if not existing yet.

Run the Container#

After the build process is done, you can run the container via the provided docker/ script


When used without arguments, the container will start the webserver and provide you the link to open it. But if you want to use some other parameters you can also do so.

run script example

./docker/ "banana sushi" -Ak_lms -S42 -s10

This would generate the legendary "banana sushi" with Seed 42, k_lms Sampler and 10 steps.

Find out more about available CLI-Parameters at features/

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:

GPU_FLAGS=all ./docker/

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:

docker: Error response from daemon: could not select device driver "" with capabilities: [[gpu]].

You can use the full set of GPU combinations documented here:

For example, use GPU_FLAGS=device=GPU-3a23c669-1f69-c64e-cf85-44e9b07e7a2a to choose a specific device identified by a UUID.


From here on you will find the the previous Docker-Docs, which will still provide some usefull informations.

Usage (time to have fun)#


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 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/ --full_precision

You'll get the script's prompt. You can see available options or quit.

invoke> -h
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.

invoke> The hulk fighting with sheldon cooper -s5 -n1
invoke> "woman closeup highly detailed"  -s 150
# Reuse previous seed and apply face restoration
invoke> "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.

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 invoke script needs absolute paths to find the image so don't use ~.

If you're on your Mac

invoke> "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

invoke> "A fantasy landscape, trending on artstation" -I /data/sketch-mountains-input.jpg --strength 0.75  --steps 50 -n1

Web Interface#

You can use the invoke script with a graphical web interface. Start the web server with:

python3 scripts/ --full_precision --web

If it's running on your Mac point your Mac web browser to

Press Control-C at the command line to stop the web server.


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/ --help
python3 scripts/orig_scripts/ --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/ --ddim_steps 5   --n_iter 1 --n_samples 1  --plms --prompt "ocean" # or --klms

Last update: February 5, 2023
Created: September 9, 2022