mirror of https://github.com/comfyanonymous/ComfyUI.git synced 2026-04-15 04:52:31 +08:00

Fix Docker build failures and workflow publish bug; remove SageAttention

- Dockerfile: fix glibc 2.41 patch path (cuda-12.9 -> cuda-12.8 to match
  installed packages); remove SAGE_ATTENTION_AVAILABLE env var
- sync-build-release.yml: add always() to publish job condition so it runs
  even when build-self is skipped (the primary GitHub runner path succeeds),
  fixing releases never being created on normal builds
- entrypoint.sh: remove SageAttention compilation and GPU detection logic;
  simplify to permissions setup, ComfyUI-Manager sync, custom node install,
  and launch
- README: update CUDA version references from 12.9/cu129 to 12.8/cu128;
  remove SageAttention documentation; fix docker-compose GPU syntax

https://claude.ai/code/session_01WQc56fWdK329K11kRGnb5g

2026-03-27 12:31:01 +00:00

4.2 KiB

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ComfyUI-Docker

An automated Repo for ComfyUI Docker image builds, optimized for NVIDIA GPUs.

About • Features • Getting Started • Usage • License

About

This image packages upstream ComfyUI with CUDA-enabled PyTorch and an entrypoint that handles volume permissions and custom node setup.

The base image is python:3.12-slim (Debian trixie) with CUDA 12.8 developer libraries installed via apt and PyTorch installed from the cu128 wheel index.

It syncs with the upstream ComfyUI repository, builds a Docker image on new releases, and pushes it to GitHub Container Registry (GHCR).

I created this repo for myself as a simple way to stay up to date with the latest ComfyUI versions while having an easy-to-use Docker image.

Features

Daily checks for upstream releases, auto-merges changes, and builds/pushes Docker images.
CUDA-enabled PyTorch + Triton on Debian trixie with CUDA 12.8 dev libs so custom CUDA builds work at runtime.
Non-root runtime with PUID/PGID mapping handled by entrypoint for volume permissions.
ComfyUI-Manager auto-sync on startup; entrypoint scans custom_nodes and installs requirements when COMFY_AUTO_INSTALL=1.

Getting Started

Install NVIDIA Container Toolkit on the host, then use docker run --gpus all or Compose GPU reservations to pass GPUs through.
Expose the ComfyUI server on port 8188 (default) and map volumes for models, inputs, outputs, and custom_nodes.

Pulling the Image

The latest image is available on GHCR:

docker pull ghcr.io/clsferguson/comfyui-docker:latest

For a specific version (synced with upstream tags, starting at v0.3.59):

docker pull ghcr.io/clsferguson/comfyui-docker:vX.Y.Z

Docker Compose

For easier management, use this docker-compose.yml:

services:
  comfyui:
    image: ghcr.io/clsferguson/comfyui-docker:latest
    container_name: ComfyUI
    runtime: nvidia
    restart: unless-stopped
    ports:
      - 8188:8188
    environment:
      - TZ=America/Edmonton
      - PUID=1000
      - PGID=1000
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities: [gpu]
    volumes:
      - comfyui_data:/app/ComfyUI/user/default
      - comfyui_nodes:/app/ComfyUI/custom_nodes
      - /mnt/comfyui/models:/app/ComfyUI/models
      - /mnt/comfyui/input:/app/ComfyUI/input
      - /mnt/comfyui/output:/app/ComfyUI/output

Run with docker compose up -d.

Usage

Open http://localhost:8188 after the container is up; change the external port via -p HOST:8188.
To target specific GPUs, use Docker's GPU device selections or Compose device_ids in reservations.

Environment Variables

PUID/PGID: map container user to host UID/GID for volume write access.
COMFY_AUTO_INSTALL=1: auto-install Python requirements from custom_nodes on startup (default: 1).
COMFY_FORCE_INSTALL=1: force reinstall of custom_nodes requirements even after first run.
CM_*: seed ComfyUI-Manager config.ini keys on first start (e.g. CM_SKIP_UPDATE_CHECK=1).

License

Distributed under the MIT License (same as upstream ComfyUI). See LICENSE for more information.

Contact

Creator: clsferguson - GitHub
Project Link: https://github.com/clsferguson/ComfyUI-Docker

Built with ❤️ for easy AI workflows.

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