Fooocus on Docker

The docker image is based on NVIDIA CUDA 12.3 and PyTorch 2.0, see Dockerfile and requirements_docker.txt for details.

Quick start

This is just an easy way for testing. Please find more information in the notes.

Clone this repository
Build the image with docker compose build
Run the docker container with docker compose up. Building the image takes some time.

When you see the message Use the app with http://0.0.0.0:7865/ in the console, you can access the URL in your browser.

Your models and outputs are stored in the fooocus-data volume, which, depending on OS, is stored in /var/lib/docker/volumes.

Details

Update the container manually

When you are using docker compose up continuously, the container is not updated to the latest version of Fooocus automatically. Run git pull before executing docker compose build --no-cache to build an image with the latest Fooocus version. You can then start it with docker compose up

Import models, outputs

If you want to import files from models or the outputs folder, you can uncomment the following settings in the docker-compose.yml:

#- ./models:/import/models   # Once you import files, you don't need to mount again.
#- ./outputs:/import/outputs  # Once you import files, you don't need to mount again.

After running docker compose up, your files will be copied into /content/data/models and /content/data/outputs Since /content/data is a persistent volume folder, your files will be persisted even when you re-run docker compose up --build without above volume settings.

Paths inside the container

Path	Details
/content/app	The application stored folder
/content/app/models.org	Original 'models' folder. Files are copied to the '/content/app/models' which is symlinked to '/content/data/models' every time the container boots. (Existing files will not be overwritten.)
/content/data	Persistent volume mount point
/content/data/models	The folder is symlinked to '/content/app/models'
/content/data/outputs	The folder is symlinked to '/content/app/outputs'

Environments

You can change config.txt parameters by using environment variables. The priority of using the environments is higher than the values defined in config.txt, and they will be saved to the config_modification_tutorial.txt

Docker specified environments are there. They are used by 'entrypoint.sh'

Environment	Details
DATADIR	'/content/data' location.
CMDARGS	Arguments for entry_with_update.py which is called by entrypoint.sh
config_path	'config.txt' location
config_example_path	'config_modification_tutorial.txt' location

You can also use the same json key names and values explained in the 'config_modification_tutorial.txt' as the environments. See examples in the docker-compose.yml

Notes

Please keep 'path_outputs' under '/content/app'. Otherwise, you may get an error when you open the history log.
Docker on Mac/Windows still has issues in the form of slow volume access when you use "bind mount" volumes. Please refer to this article for not using "bind mount".
The MPS backend (Metal Performance Shaders, Apple Silicon M1/M2/etc.) is not yet supported in Docker, see https://github.com/pytorch/pytorch/issues/81224
You can also use docker compose up -d to start the container detached and connect to the logs with docker compose logs -f. This way you can also close the terminal and keep the container running.