scraed-LanPaint/README.md

LanPaint

Powerful Training Free Inpainting Tool Works for Every SD Model. Official Implementation of "Lanpaint: Training-Free Diffusion Inpainting with Exact and Fast Conditional Inference".

Features

• 🎨 Zero-Training Inpainting - Works immediately with ANY SD model, even custom models you've trained yourself
• 🛠️ Simple Integration - Same workflow as standard ComfyUI KSampler
• 🚀 Quality Enhancements - High quality and seamless inpainting

Example Results

Example 1 (LanPaint K Sampler)

View Workflow & Masks

Example 2: (LanPaint K Sampler (Advanced))

View Workflow & Masks

Example 3: (LanPaint K Sampler (Advanced))

View Workflow & Masks

Each example includes:

• Original masked image
• Full ComfyUI workflow

Quickstart

1. Install ComfyUI.
2. Install ComfyUI-Manager
3. Look up this extension in ComfyUI-Manager. If you are installing manually, clone this repository under ComfyUI/custom_nodes.
4. Restart ComfyUI.

Installation

1. Place LanPaint_Nodes.py in your ComfyUI/custom_nodes folder
2. Restart ComfyUI
3. Use like regular KSampler with inpainting workflows

Usage

Workflow Setup
Same as default ComfyUI KSampler - simply replace with LanPaint KSampler nodes. The inpainting workflow is the same as the SetLatentNoiseMask inpainting workflow.

Note LanPaint only support binary mask (0,1) with no smoothing. Any mask with smooting will be converted to binary mask during inpainting.

Advanced Options (Optional)

Fine-tune results with these key parameters:

Parameter	Typical Range	Effect
NumSteps	1-10	Thinking iterations per step
Lambda	4-8	Content preservation strength
StepSize	0.05-0.2	Detail refinement intensity

Citation

If you use LanPaint in your research or projects, please cite our work:

@misc{zheng2025lanpainttrainingfreediffusioninpainting,
      title={Lanpaint: Training-Free Diffusion Inpainting with Exact and Fast Conditional Inference}, 
      author={Candi Zheng and Yuan Lan and Yang Wang},
      year={2025},
      eprint={2502.03491},
      archivePrefix={arXiv},
      primaryClass={eess.IV},
      url={https://arxiv.org/abs/2502.03491}, 
}

Publishing to Registry

If you wish to share this custom node with others in the community, you can publish it to the registry. We've already auto-populated some fields in pyproject.toml under tool.comfy, but please double-check that they are correct.

You need to make an account on https://registry.comfy.org and create an API key token.

• Go to the registry. Login and create a publisher id (everything after the @ sign on your registry profile).
• Add the publisher id into the pyproject.toml file.
• Create an api key on the Registry for publishing from Github. Instructions.
• Add it to your Github Repository Secrets as REGISTRY_ACCESS_TOKEN.

A Github action will run on every git push. You can also run the Github action manually. Full instructions here. Join our discord if you have any questions!