Fooocus on Docker

The docker image is based on NVIDIA CUDA 12.4 and PyTorch 2.1, see Dockerfile and requirements_docker.txt for details.

Requirements

• A computer with specs good enough to run Fooocus, and proprietary Nvidia drivers
• Docker, Docker Compose, or Podman

Quick start

More information in the notes.

Running with Docker Compose

1. Clone this repository
2. Run the docker container with docker compose up.

Running with Docker

docker run -p 7865:7865 -v fooocus-data:/content/data -it \
--gpus all \
-e CMDARGS=--listen \
-e DATADIR=/content/data \
-e config_path=/content/data/config.txt \
-e config_example_path=/content/data/config_modification_tutorial.txt \
-e path_checkpoints=/content/data/models/checkpoints/ \
-e path_loras=/content/data/models/loras/ \
-e path_embeddings=/content/data/models/embeddings/ \
-e path_vae_approx=/content/data/models/vae_approx/ \
-e path_upscale_models=/content/data/models/upscale_models/ \
-e path_inpaint=/content/data/models/inpaint/ \
-e path_controlnet=/content/data/models/controlnet/ \
-e path_clip_vision=/content/data/models/clip_vision/ \
-e path_fooocus_expansion=/content/data/models/prompt_expansion/fooocus_expansion/ \
-e path_outputs=/content/app/outputs/ \
ghcr.io/lllyasviel/fooocus

Running with Podman

podman run -p 7865:7865 -v fooocus-data:/content/data -it \
--security-opt=no-new-privileges --cap-drop=ALL --security-opt label=type:nvidia_container_t --device=nvidia.com/gpu=all \
-e CMDARGS=--listen \
-e DATADIR=/content/data \
-e config_path=/content/data/config.txt \
-e config_example_path=/content/data/config_modification_tutorial.txt \
-e path_checkpoints=/content/data/models/checkpoints/ \
-e path_loras=/content/data/models/loras/ \
-e path_embeddings=/content/data/models/embeddings/ \
-e path_vae_approx=/content/data/models/vae_approx/ \
-e path_upscale_models=/content/data/models/upscale_models/ \
-e path_inpaint=/content/data/models/inpaint/ \
-e path_controlnet=/content/data/models/controlnet/ \
-e path_clip_vision=/content/data/models/clip_vision/ \
-e path_fooocus_expansion=/content/data/models/prompt_expansion/fooocus_expansion/ \
-e path_outputs=/content/app/outputs/ \
ghcr.io/lllyasviel/fooocus

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/ (or ~/.local/share/containers/storage/volumes/ when using podman).

Building the container locally

Clone the repository first, and open a terminal in the folder.

Build with docker:

docker build . -t fooocus

Build with podman:

podman build . -t fooocus

Details

Update the container manually (docker compose)

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 add the following bind mounts in the docker-compose.yml or your preferred method of running the container:

#- ./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 the container, 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 the container without the above mounts.

Paths inside the container

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'

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.