LanPaint (Thinking mode Inpaint)

Unlock precise inpainting without additional training. LanPaint lets the model "think" through multiple iterations before denoising, aiming for seamless and accurate results. This is the 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 Model Used in This Example

Example 2: (LanPaint K Sampler (Advanced))

View Workflow & Masks Model Used in This Example

Example 3: (LanPaint K Sampler (Advanced))

View Workflow & Masks Model Used in This Example

Each example includes:

• Original masked image
• Full ComfyUI workflow

Quickstart

1. Install ComfyUI: Follow the official ComfyUI installation guide to set up ComfyUI on your system.
2. Install ComfyUI-Manager: Add the ComfyUI-Manager for easy extension management.
3. 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.
4. 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 requires binary masks (values of 0 or 1) without opacity or smoothing. To ensure compatibility, set the mask's opacity and hardness to maximum in your mask editor. During inpainting, any mask with smoothing or gradients will automatically be converted to a binary mask.
• LanPaint relies heavily on your text prompts to guide inpainting - explicitly describe the content you want generated in the masked area. If results show artifacts or mismatched elements, counteract them with targeted negative prompts.

Basic Sampler

LanPaint KSampler
Simplified interface with recommended defaults:

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

LanPaint KSampler (Advanced)
Full parameter control:

Key Parameters

Parameter	Range	Description
Steps	0-100	Total steps of diffusion sampling. Higher means better inpainting. Recommend 50.
LanPaint_NumSteps	0-20	Reasoning iterations per denoising step ("thinking depth")
LanPaint_Lambda	0.1-50	Content alignment strength (higher = stricter)
LanPaint_cfg_BIG	0-20	CFG scale used when aligning masked and unmasked region (higher = better alignment).

For detailed descriptions of each parameter, simply hover your mouse over the corresponding input field to view tooltips with additional information.

LanPaint KSampler (Advanced) Tuning Guide

For challenging inpainting tasks:

1️⃣ Primary Adjustments:

• Increase steps, LanPaint_NumSteps (thinking iterations), and LanPaint_cfg_BIG (guidance scale).

2️⃣ Secondary Tweaks:

• Boost LanPaint_Lambda (spatial constraint strength) or LanPaint_StepSize (denoising aggressiveness).

3️⃣ Balance Speed vs Stability:

• Reduce LanPaint_Friction to prioritize faster results with fewer "thinking" steps (may risk instability).
• Increase LanPaint_Tamed (noise normalization onto a sphere) or LanPaint_Alpha (constraint the friction of underdamped Langevin dynamics) to suppress artifacts.

⚠️ Notes:

• Optimal parameters vary depending on the model and the size of the inpainting area.
• For effective tuning, fix the seed and adjust parameters incrementally while observing the results. This helps isolate the impact of each setting.

Contribute

Help us improve LanPaint! 🚀 Report bugs, share example cases, or contribute your personal parameter settings to benefit the community.

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}, 
}