walkingwormfood / get_comfy

ComfyUI docker images

AI-Dock build of ComfyUI tweaked to be built in AWS Codebuild. This configuration builds a ComfyUI Docker image in ECR. Some assembly required in AWS.

ComfyUI Docker Image

Run ComfyUI in a docker container locally or in the cloud.

Note

These images do not bundle models or third-party configurations. You should use a provisioning script to automatically configure your container. You can find examples in config/provisioning.

Quick Start

Simply declare your environment variables and launch a container with docker compose or choose a pre-configured cloud template.

About ComfyUI

ComfyUI is a powerful and modular stable diffusion GUI and backend with a user-friendly interface that empowers users to effortlessly design and execute intricate Stable Diffusion pipelines. This innovative system employs a visual approach with nodes, flowcharts, and graphs, eliminating the need for manual coding.

Some features:

• Graphical Workflow Design: Create complex Stable Diffusion workflows through a user-friendly node-based interface, without requiring coding skills.

• Wide Compatibility: Fully supports SD1.x, SD2.x, and SDXL for comprehensive applicability.

• Efficient Queue System: Incorporates an asynchronous queue system for streamlined workflow execution.

• Selective Optimization: Optimizes execution by re-running only the parts of the workflow that have changed.

• CPU Support: Capable of functioning on CPUs with the --cpu option, although at a slower pace.

• Model Integration: Load ckpt, safetensors, diffusers models/checkpoints, standalone VAEs, CLIP models, and more.

• Diverse Capabilities: Handle embeddings, textual inversion, Loras (regular, locon, loha), hypernetworks, inpainting, control networks, upscale models, unCLIP models, GLIGEN, model merging, and latent previews with TAESD.

• Workflow Flexibility: Save and load workflows conveniently in JSON format, facilitating easy modification and reuse.

• Advanced Workflows: The node interface empowers the creation of intricate workflows, from high-resolution fixes to more advanced applications.

• Area Composition: Supports area composition techniques for enhanced creative control.

• Offline Functionality: Operates completely offline, ensuring data privacy and eliminating any need for downloads.

• Configuration Control: Utilize a configuration file to set specific search paths for models.

Pre-built Images

Docker images are built automatically through a GitHub Actions workflow and hosted at the GitHub Container Registry.

An incremental build process is used to avoid needing a huge cache - The following images are used to provide functionality:

• nvidia/cuda / ubuntu ↴
• ai-dock/base-image ↴
• ai-dock/python ↴
• ai-dock/pytorch / ai-dock/jupyter-pytorch ↴
• ai-dock/comfyui

Version Tags

The :latest tag points to :latest-cuda

Tags follow these patterns:

CUDA

• :pytorch-[pytorch-version]-py[python-version]-cuda-[x.x.x]-base-[ubuntu-version]

• :latest-cuda → :pytorch-2.0.1-py3.10-cuda-11.8.0-base-22.04

• :latest-cuda-jupyter → :jupyter-pytorch-2.0.1-py3.10-cuda-11.8.0-base-22.04

ROCm

• :pytorch-[pytorch-version]-py[python-version]-rocm-[x.x.x]-runtime-[ubuntu-version]

• :latest-rocm → :pytorch-2.0.1-py3.10-rocm-5.4.2-runtime-22.04

• :latest-rocm-jupyter → :jupyter-pytorch-2.0.1-py3.10-rocm-5.4.2-runtime-22.04

CPU

• :pytorch-[pytorch-version]-py[python-version]-ubuntu-[ubuntu-version]

• :latest-cpu → :pytorch-2.0.1-py3.10-cpu-22.04

• :latest-cpu-jupyter → :jupyter-pytorch-2.0.1-py3.10-cpu-22.04

Browse here for an image suitable for your target environment.

You can also self-build from source by editing .env and running docker compose build.

Supported Python versions: 3.10

Supported Pytorch versions: 2.0.1

Supported Platforms: NVIDIA CUDA, AMD ROCm, CPU

Run Locally

A 'feature-complete' docker-compose.yaml file is included for your convenience. All features of the image are included - Simply edit the environment variables in .env, save and then type docker compose up.

If you prefer to use the standard docker run syntax, the command to pass is init.sh.

Run in the Cloud

This image should be compatible with any GPU cloud platform. You simply need to pass environment variables at runtime.

Note

Please raise an issue on this repository if your provider cannot run the image.

Container Cloud

Container providers don't give you access to the docker host but are quick and easy to set up. They are often inexpensive when compared to a full VM or bare metal solution.

All images built for ai-dock are tested for compatibility with both vast.ai and runpod.io.

Images that include Jupyter are also tested to ensure compatibility with Paperspace Gradient

See a list of pre-configured templates here

Warning

Container cloud providers may offer both 'community' and 'secure' versions of their cloud. If your usecase involves storing sensitive information (eg. API keys, auth tokens) then you should always choose the secure option.

VM Cloud

Running docker images on a virtual machine/bare metal server is much like running locally.

You'll need to:

• Configure your server
• Set up docker
• Clone this repository
• Edit .envand docker-compose.yml
• Run docker compose up

Find a list of compatible VM providers here.

Connecting to Your Instance

All services listen for connections at 0.0.0.0. This gives you some flexibility in how you interact with your instance:

Expose the Ports

This is fine if you are working locally but can be dangerous for remote connections where data is passed in plaintext between your machine and the container over http.

SSH Tunnel

You will only need to expose port 22 (SSH) which can then be used with port forwarding to allow secure connections to your services.

If you are unfamiliar with port forwarding then you should read the guides here and here.

Cloudflare Tunnel

You can use the included cloudflared service to make secure connections without having to expose any ports to the public internet. See more below.

Environment Variables

Variable	Description
CF_TUNNEL_TOKEN	Cloudflare zero trust tunnel token - See documentation.
CF_QUICK_TUNNELS	Create ephemeral Cloudflare tunnels for web services (default false)
COMFYUI_BRANCH	ComfyUI branch/commit hash. Defaults to master
COMFYUI_FLAGS	Startup flags. eg. --gpu-only --highvram
COMFYUI_PORT	ComfyUI interface port (default 8188)
GPU_COUNT	Limit the number of available GPUs
PROVISIONING_SCRIPT	URL of a remote script to execute on init. See note.
RCLONE_*	Rclone configuration - See rclone documentation
SKIP_ACL	Set true to skip modifying workspace ACL
SSH_PORT	Set a non-standard port for SSH (default 22)
SSH_PUBKEY	Your public key for SSH
WORKSPACE	A volume path. Defaults to /workspace/

Environment variables can be specified by using any of the standard methods (docker-compose.yaml, docker run -e...). Additionally, environment variables can also be passed as parameters of init.sh.

Passing environment variables to init.sh is usually unnecessary, but is useful for some cloud environments where the full docker run command cannot be specified.

Example usage: docker run -e STANDARD_VAR1="this value" -e STANDARD_VAR2="that value" init.sh EXTRA_VAR="other value"

Provisioning script

It can be useful to perform certain actions when starting a container, such as creating directories and downloading files.

You can use the environment variable PROVISIONING_SCRIPT to specify the URL of a script you'd like to run.

The URL must point to a plain text file - GitHub Gists/Pastebin (raw) are suitable options.

If you are running locally you may instead opt to mount a script at /opt/ai-dock/bin/provisioning.sh.

Note

If configured, sshd, cloudflared, rclone, jupyter & logtail will be launched before provisioning; comfyui will launch after.

Warning

Only use scripts that you trust and which cannot be changed without your consent.

Software Management

A small software collection is installed by apt-get to provide basic utility.

All other software is installed into its own environment by micromamba, which is a drop-in replacement for conda/mamba. Read more about it here.

Micromamba environments are particularly useful where several software packages are required but their dependencies conflict.

Installed Micromamba Environments

Environment	Packages
base	micromamba's base environment
system	supervisord, openssh rclone
fastapi	logtail web UI, port redirector web UI
comfyui	ComfyUI and dependencies
python_[ver]	python

If you are extending this image or running an interactive session where additional software is required, you should almost certainly create a new environment first. See below for guidance.

Useful Micromamba Commands

Command	Function
micromamba env list	List available environments
micromamba activate [name]	Activate the named environment
micromamba deactivate	Close the active environment
micromamba run -n [name] [command]	Run a command in the named environment without activating

All ai-dock images create micromamba environments using the --always-softlink flag which can save disk space where multiple environments are available.

To create an additional micromamba environment, eg for python, you can use the following:

micromamba --always-softlink create -y -c conda-forge -c defaults -n [name] python=3.10

Volumes

Data inside docker containers is ephemeral - You'll lose all of it when the container is destroyed.

You may opt to mount a data volume at /workspace - This is a directory that ai-dock images will look for to make downloaded data available outside of the container for persistence.

When the runtime scripts detect a mounted workspace, the ComfyUI directory will be moved there from its original location in /opt. If the workspace is not mounted then a symlink will be created for convenience.

You can define an alternative path for the workspace directory by passing the environment variable WORKSPACE=/my/alternative/path/ and mounting your volume there. This feature will generally assist where cloud providers enforce their own mountpoint location for persistent storage.

The provided docker-compose.yaml will mount the local directory ./workspace at /workspace.

As docker containers generally run as the root user, new files created in /workspace will be owned by uid 0(root).

To ensure that the files remain accessible to the local user that owns the directory, the docker entrypoint will set a default ACL on the directory by executing the commamd setfacl -d -m u:${WORKSPACE_UID}:rwx /workspace.

If you do not want this, you can set the environment variable SKIP_ACL=true.

Running Services

This image will spawn multiple processes upon starting a container because some of our remote environments do not support more than one container per instance.

All processes are managed by supervisord and will restart upon failure until you either manually stop them or terminate the container.

Note

Some of the included services would not normally be found inside of a container. They are, however, necessary here as some cloud providers give no access to the host; Containers are deployed as if they were a virtual machine.

ComfyUI

The service will launch on port 8188 unless you have specified an override with COMFYUI_PORT.

ComfyUI will be updated to the latest version on container start. You can pin the version to a branch or commit hash by setting the COMFYUI_BRANCH variable.

You can set startup flags by using variable COMFYUI_FLAGS.

To manage this service you can use supervisorctl [start|stop|restart] comfyui.

Note

If you have enabled CF_QUICK_TUNNELS a secure https://[random-auto-generated-sub-domain].trycloudflare.com link will be created. You can find it at /var/log/supervisor/quicktunnel-comfyui.log

Jupyter (with tag jupyter only)

The jupyter server will launch a lab instance unless you specify JUPYTER_MODE=notebook.

Jupyter server will listen on port 8888 unless you have specified an alternative with the JUPYTER_PORT environment variable.

A python kernel will be installed coresponding with the python version of the image.

Jupyter's official documentation is available at https://jupyter.org/

Note

If you have enabled CF_QUICK_TUNNELS a secure https://[random-auto-generated-sub-domain].trycloudflare.com link will be created. You can find it at /var/log/supervisor/quicktunnel-jupyter.log

Port Redirector

This is a simple list of links to the web services available inside the container.

The service will bind to port 1111.

For each service, you will find a direct link and, if you have set CF_QUICK_TUNNELS=true, a link to the service via a fast and secure Cloudflare tunnel.

Note

This service will not show links to any pre-configured Cloudflare tunnels as the domains are static and already known to the user.

Log Viewer

The web based log viewer will start on port 1122.

It's a very lightweight websocket based stream of the latest updates in /var/log/logtail.log.

This service will also be accessible on any other exposed ports until the program designated to that port is ready to use.

Cloudflared

The Cloudflare tunnel daemon will start if you have provided a token with the CF_TUNNEL_TOKEN environment variable.

This service allows you to connect to your local services via https without exposing any ports.

You can also create a private network to enable remote connecions to the container at its local address (172.x.x.x) if your local machine is running a Cloudflare WARP client.

If you do not wish to provide a tunnel token, you could enable CF_QUICK_TUNNELS which will create a throwaway tunnel for your web services.

Full documentation for Cloudflare tunnels is here.

Note

Cloudflared is included so that secure networking is available in all cloud environments.

Warning

You should only provide tunnel tokens in secure cloud environments.

SSHD

A SSH server will be started if at least one valid public key is found inside the running container in the file /root/.ssh/authorized_keys

The server will bind to port 22 unless you specify variable SSH_PORT.

There are several ways to get your keys to the container.

• If using docker compose, you can paste your key in the local file config/authorized_keys before starting the container.

• You can pass the environment variable SSH_PUBKEY with your public key as the value.

• Cloud providers often have a built-in method to transfer your key into the container

If you choose not to provide a public key then the SSH server will not be started.

To make use of this service you should map port 22 to a port of your choice on the host operating system.

See this guide by DigitalOcean for an excellent introduction to working with SSH servers.

Note

SSHD is included because the end-user should be able to know the version prior to deloyment. Using a providers add-on, if available, does not guarantee this.

Rclone mount

Rclone allows you to access your cloud storage from within the container by configuring one or more remotes. If you are unfamiliar with the project you can find out more at the Rclone website.

Any Rclone remotes that you have specified, either through mounting the config directory or via setting environment variables will be mounted at /workspace/remote/[remote name]. For this service to start, the following conditions must be met:

• Fuse3 installed in the host operating system
• Kernel module fuse loaded in the host
• Host /etc/passwd mounted in the container
• Host /etc/group mounted in the container
• Host device /dev/fuse made available to the container
• Container must run with cap-add SYS_ADMIN
• Container must run with securiry-opt apparmor:unconfined
• At least one remote must be configured

The provided docker-compose.yaml includes a working configuration (add your own remotes).

In the event that the conditions listed cannot be met, rclone will still be available to use via the CLI - only mounts will be unavailable.

If you intend to use the rclone create command to interactively generate remote configurations you should ensure port 53682 is accessible. See https://rclone.org/remote_setup/ for further details.

Note

Rclone is included to give the end-user an opportunity to easily transfer files between the instance and their cloud storage provider.

Warning

You should only provide auth tokens in secure cloud environments.

Logtail

This script follows and prints the log files for each of the above services to stdout. This allows you to follow the progress of all running services through docker's own logging system.

If you are logged into the container you can follow the logs by running logtail.sh in your shell.

Open Ports

Some ports need to be exposed for the services to run or for certain features of the provided software to function

Open Port	Service / Description
22	SSH server
1111	Port redirector web UI
1122	Log viewer web UI
8188	ComfyUI Interface
8888	Jupyter
53682	Rclone interactive config

Pre-Configured Templates

Vast.​ai

• comfyui:latest

• comfyui:latest-jupyter

---

Runpod.​io

• comfyui:latest

• comfyui:latest-jupyter

---

Paperspace

• Create a new notebook with the Start from Scratch template.
• Select Advanced options
• In Container Name enter ghcr.io/ai-dock/comfyui:latest-jupyter
• In Registry Username enter x (Paperspace bug)
• In Command enter init.sh WORKSPACE=/notebooks PROVISIONING_SCRIPT="https://raw.githubusercontent.com/ai-dock/comfyui/main/config/provisioning/get-models-sd-official.sh" CF_QUICK_TUNNELS=true

You can use the web UI to do further configuration, or you can supply further environment variables as detailed above.

Note

The use of CF_QUICK_TUNNELS enables us to reach the web UI with a link supplied by Cloudflare. You can find the link in /var/log/supervisor/quicktunnel-comfyui.log

Warning

Do not attempt to use tunnels to circumvent Paperspace restrictions (eg. SSH & private networking) - You will lose your account.

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Note

These templates are configured to use the :latest tag but you are free to change to any of the available Pytorch CUDA tags listed here

Compatible VM Providers

Images that do not require a GPU will run anywhere - Use an image tagged :*-cpu-xx.xx

Where a GPU is required you will need either :*cuda* or :*rocm* depending on the underlying hardware.

A curated list of VM providers currently offering GPU instances:

• Akami/Linode
• Amazon Web Services
• Google Compute Engine
• Vultr

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The author (@robballantyne) may be compensated if you sign up to services linked in this document. Testing multiple variants of GPU images in many different environments is both costly and time-consuming; This helps to offset costs