Hugging Face's logo

charrywhite
/
LanPaint

Image-to-Image
English
comfyui-extension
comfyui
inpainting
stable-diffusion
image-generation
computer-vision
Model card Files
xet
Community
LanPaint / model-card.md
charrywhite's picture
charrywhite
Upload folder using huggingface_hub
acb46ea verified
preview code
|
raw
history blame contribute delete
3.55 kB
metadata
language:
  - en
tags:
  - comfyui
  - inpainting
  - stable-diffusion
  - image-generation
  - computer-vision
  - diffusion
  - image-editing
license: gpl-3.0
library_name: comfyui-extension
pipeline_tag: image-to-image
datasets:
  - stable-diffusion
  - stable-diffusion-xl
  - stable-diffusion-3
  - qwen-image
  - hidream
  - flux
metrics:
  - image-quality
  - inpainting-accuracy
  - consistency

LanPaint: Universal Inpainting Sampler

Model Description

LanPaint is a universal inpainting sampler that works with any Stable Diffusion model without requiring specialized training. It introduces a "think mode" that allows models to process multiple iterations before denoising, resulting in superior inpainting quality.

Intended Use

LanPaint is designed for:

  • Image Inpainting: Fill in masked areas of images with contextually appropriate content
  • Image Outpainting: Extend images beyond their original boundaries
  • Character Consistency: Generate consistent character views and variations
  • Universal Compatibility: Work with any Stable Diffusion model (SD 1.5, XL, 3.5, Flux, HiDream, Qwen-Image)

How to Use

Installation

  1. Install ComfyUI (version > 0.3.11)
  2. Install ComfyUI-Manager
  3. Install LanPaint via ComfyUI-Manager or clone to custom_nodes folder
  4. Restart ComfyUI

Basic Usage

  • Use LanPaint nodes in the "sampling" category
  • Same workflow as standard ComfyUI KSampler
  • Supports any mask shape, size, or position
  • Configurable "thinking" steps for quality vs. speed trade-off

Example Workflows

  • Qwen Image inpainting/outpainting
  • HiDream character consistency
  • SD 3.5 high-quality inpainting
  • Flux model inpainting (with low guidance)

Limitations

  • Distillation Models: Reduced performance on models like Flux.dev (use low guidance 1.0-2.0)
  • Computation Time: More thinking steps require more processing time
  • Memory Usage: May require significant GPU memory for high-resolution images

Training and Technical Details

  • Training-free: Works out-of-the-box with existing models
  • No fine-tuning required: Universal compatibility across model architectures
  • Exact conditional inference: Mathematically sound approach to inpainting
  • Fast implementation: Optimized for real-world usage

Evaluation

LanPaint has been benchmarked against traditional inpainting methods:

  • VAE Encode for Inpainting
  • Set Latent Noise Mask
  • Standard KSampler approaches

Results show significant improvements in:

  • Content consistency
  • Mask boundary handling
  • Overall image quality
  • Character preservation

Citation 

@article{lanpaint2024,
  title={LanPaint: Training-Free Diffusion Inpainting with Exact and Fast Conditional Inference},
  author={LanPaint Team},
  journal={arXiv preprint arXiv:2502.03491},
  year={2024}
}

Additional Resources

License

This project is licensed under the GNU General Public License v3.0 - see the LICENSE file for details.