ComfyUI LTS

A vanilla, up-to-date fork of ComfyUI intended for long term support (LTS) from AppMana and Hidden Switch.

Used By

Used in production by Scopely, a game studio, Livepeer and Nunchaku Tech. Used by innovators at Ferrero Group, Hyundai and Nike.

If you need to develop an application or plugin around ComfyUI, this fork stays compatible and up-to-date with upstream, fixing numerous bugs and adding features. It also packages tacit knowledge about running diffusion models and art workflows, distributed inference, deployment on Kubernetes, and other production tasks that Claude and Gemini cannot do.

Key Features and Differences

This LTS fork adds development, embedding, automated testing, LLM and distributed inference features to ComfyUI, but maintains compatibility with custom nodes from the ecosystem.

• Pip and UV Installable: Install via pip or uv directly from GitHub. No manual cloning required for users.
• Automatic Model Downloading: Missing models (e.g., Stable Diffusion, FLUX, LLMs) are downloaded on-demand from Hugging Face or CivitAI.
• Docker and Containers: First-class support for Docker and Kubernetes with optimized containers for NVIDIA and AMD.
• Distributed Inference: Run scalable inference clusters with multiple workers and frontends using RabbitMQ.
• Embedded / Library: Use ComfyUI as a Python library (import comfy) inside your own applications without the web server. Runs like diffusers.
• Vanilla Custom Nodes: Fully compatible with existing ComfyUI custom nodes (ComfyUI-Manager, WanVideoWrapper, KJNodes, etc.). Clone into custom_nodes/ and install dependencies into your venv.
• LTS Custom Nodes: A curated set of "Installable" custom nodes (ControlNet, AnimateDiff, IPAdapter) optimized for this fork.
• LLM Support: Native support for Large Language Models (LLaMA, Phi-3, etc.) and multi-modal workflows.
• API and Configuration: Enhanced API endpoints and extensive configuration options via CLI args, env vars, and config files.
• Tests: Automated test suite ensuring stability for new features.

Quickstart (Linux)

UI Users

For users who want to run ComfyUI for generating images and videos.

1. Install uv:

   curl -LsSf https://astral.sh/uv/install.sh | sh

2. Create a Workspace:

   mkdir comfyui-workspace
   cd comfyui-workspace

3. Install and Run:

   # Create a virtual environment
   uv venv --python 3.12
   
   # Install ComfyUI LTS
   # --torch-backend=auto installs the correct torch, torchvision and torchaudio for your platform.
   # Omit --torch-backend if you want to keep your currently installed PyTorch.
   uv pip install --torch-backend=auto "comfyui@git+https://github.com/hiddenswitch/ComfyUI.git"
   
   # Run
   uv run --no-sync comfyui

Developers

For developers contributing to the codebase or building on top of it.

1. Clone the Repository:

   git clone https://github.com/hiddenswitch/ComfyUI.git
   cd ComfyUI

2. Setup Environment:

   # Create virtual environment
   uv venv --python 3.12
   source .venv/bin/activate
   
   # Install in editable mode with dev dependencies
   uv pip install -e .[dev]

3. Run:

   uv run --no-sync comfyui

Using ComfyUI as a Library

ComfyUI can run embedded inside your own Python application. No server is started, no subprocesses are used. Use the Comfy async context manager to execute workflows directly:

from comfy.client.embedded_comfy_client import Comfy

async with Comfy() as client:
    outputs = await client.queue_prompt(workflow_dict)
    # All models unloaded and VRAM released on exit

Build workflows programmatically with GraphBuilder, or paste API-format JSON from the web UI. Stream previews during inference with queue_with_progress.

See Embedded / Library Usage for complete examples.

Documentation

Full documentation is available in docs/index.md.

Core

• Installation & Getting Started
• Hardware Compatibility
• Configuration
• Troubleshooting

Features & Workflows

• Large Language Models
• Video Workflows (AnimateDiff, SageAttention, etc.)
• Other Features (SVG, Ideogram)

Extending ComfyUI

• Custom Nodes (Installing & Authoring)
• Embedded / Library Usage (Python, GraphBuilder, Streaming)
• Testing Workflows (pytest, Image Snapshots)
• API Usage (REST, WebSocket)

Deployment

• Distributed / Multi-GPU
• Docker & Containers

Development

• Linting
• Merging Upstream