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

Each example includes:

Original masked image
Full ComfyUI workflow

Quickstart

Install ComfyUI: Follow the official ComfyUI installation guide to set up ComfyUI on your system.
Install ComfyUI-Manager: Add the ComfyUI-Manager for easy extension management.
Install LanPaint Nodes:
- Via ComfyUI-Manager: Search for "LanPaint" in the manager and install it directly.
- Manually: Click "Install via Git URL" in ComfyUI-Manager and input the GitHub repository link:
```
https://github.com/scraed/LanPaint.git
```
  Alternatively, clone this repository into the ComfyUI/custom_nodes folder.
Restart ComfyUI: Restart ComfyUI to load the LanPaint nodes.

Once installed, you'll find the LanPaint nodes under the "sampling" category in ComfyUI. Use them just like the default KSampler for high-quality inpainting!

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.

Basic Sampler

LanPaint KSampler
Simplified interface with recommended defaults:

Steps: 50+ recommended
LanPaint NumSteps: 1-10 (complexity of edits)
Built-in parameter presets

Advanced Sampler

LanPaint KSampler (Advanced)
Full parameter control:

Key Parameters

Parameter	Range	Description
`LanPaint_NumSteps`	0-20	Reasoning iterations per denoising step ("thinking depth")
`LanPaint_Lambda`	0.1-50	Content preservation strength (higher = stricter)
`LanPaint_StepSize`	0.0001-0.5	Convergence speed (higher = faster but riskier)
`LanPaint_Beta`	0.0001-5	Novelty generation intensity
`LanPaint_Friction`	1-50	System stability (higher = more stable)
`LanPaint_Tamed`	0-20	Noise regularization strength

Usage Tips

Start Simple - Begin with basic sampler and default values
Increase Steps - 50-100 steps recommended for complex edits
Tune Gradually - Adjust parameters in 10-20% increments
Monitor Stability - If artifacts appear, increase Friction/Tamed

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}, 
}

README.md

LanPaint