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1Panel Runtime Images

This repository contains runtime images used by 1Panel.

Current images in this repository:

• 1panel/comfyui
• 1panel/hermes-agent
• 1panel/openclaw
• 1panel/node
• 1panel/php
• 1panel/java

Images

• 1panel/comfyui: ComfyUI runtime image for NVIDIA GPUs, built from matching upstream stable releases
• 1panel/hermes-agent: Hermes Agent runtime image maintained by 1Panel with bundled messaging channel dependencies
• 1panel/openclaw: OpenClaw runtime image built from official source with 1Panel-oriented customizations
• 1panel/node: Node.js runtime image
• 1panel/php: PHP-FPM runtime images for multiple versions
• 1panel/java: Java JDK runtime images based on Eclipse Temurin

ComfyUI

The ComfyUI image supports linux/amd64 and linux/arm64 NVIDIA hosts and includes the same pinned PyTorch 2.13.0 CUDA 13.0 stack on both architectures. GPU operation requires a compatible NVIDIA driver (Linux 580.65.06 or newer for the current CUDA stack) and NVIDIA Container Toolkit. Each upstream stable release is published as one multi-platform image under its exact release tag plus a tag without the leading v (for example, 1panel/comfyui:v0.33.1 and 1panel/comfyui:0.33.1). Docker automatically selects the matching architecture.

Start the container with Docker:

docker run -d \
  --name comfyui \
  --gpus all \
  -p 8188:8188 \
  -v comfyui-data:/data \
  1panel/comfyui:v0.33.1

This example exposes port 8188, stores persistent data in the comfyui-data volume, and requests all NVIDIA GPUs. Change the image tag, host port, or volume name directly in the command when needed.

Models, custom nodes, inputs, outputs, user settings, and runtime-installed Python packages are stored under /data. ComfyUI-Manager dependencies are included; enable the manager when needed by appending --enable-manager to the command.

For CPU-only operation, omit --gpus all and append --cpu after the image name. CPU mode works on both architectures but is significantly slower.

The amd64 CUDA variant is intended primarily for RTX 20 series and newer GPUs. The arm64 variant targets NVIDIA's server/SBSA CUDA platforms; it must not be assumed to support every ARM device or Jetson model, which follows NVIDIA's JetPack-specific PyTorch installation path.