LanPaint (Thinking mode Inpaint)

Unlock precise inpainting without additional training. LanPaint lets the model "think" through multiple iterations before denoising, enabling you to invest more computation time for superior quality.

This is the official implementation of "Lanpaint: Training-Free Diffusion Inpainting with Exact and Fast Conditional Inference".

Features

• Universal Compatibility – Works instantly with almost any model (SD 1.5, XL, 3.5, Flux, HiDream, or custom LoRAs) and ControlNet.
• No Training Needed – Works out of the box with your existing model.
• Easy to Use – Same workflow as standard ComfyUI KSampler.
• Flexible Masking – Supports any mask shape, size, or position for inpainting/outpainting.
• No Workarounds – Generates 100% new content (no blending or smoothing) without relying on partial denoising.
• Beyond Inpainting – You can even use it as a simple way to generate consistent characters.

How It Works

LanPaint uses Langevin Dynamics as "thinking" steps, which digs deeper into the diffusion process and allows the model to generate more consistent results.

LanPaint introduces "BIG score" that creates a two-way alignment between masked and unmasked areas. It continuously evaluates:

• "Does the new content make sense with the existing elements?"
• "Do the existing elements support the new creation?"
  Based on this evaluation, LanPaint iteratively updates the noise in both the masked and unmasked regions.

LanPaint also implements an accurate, robust, and fast Langevin dynamics solver.

Quickstart

1. Install ComfyUI: Follow the official ComfyUI installation guide to set up ComfyUI on your system. Or ensure your ComfyUI version > 0.3.11.
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!

Updates

• 2025/06/04
  • Add more sampler support.
  • Add early stopping to advanced sampler.
• 2025/05/28
  • Major update on the Langevin solver. It is now much faster and more stable.
  • Greatly simplified the parameters for advanced sampler.
  • Fix performance issue on Flux and SD 3.5
• 2025/04/16
  • Added Primary HiDream support
• 2025/03/22
  • Added Primary Flux support
  • Added Tease Mode
• 2025/03/10
  • LanPaint has received a major update! All examples now use the LanPaint K Sampler, offering a simplified interface with enhanced performance and stability.

Examples

All examples use a random seed 0 to generate batch of 4 images for fair comparison. (Warning: Generating 4 images may exceed your GPU memory; adjust batch size as needed.)

Example HiDream: InPaint(LanPaint K Sampler, 5 steps of thinking)

View Workflow & Masks

You need to follow the ComfyUI version of HiDream workflow to download and install the model.

Example SD 3.5: InPaint(LanPaint K Sampler, 5 steps of thinking)

View Workflow & Masks

You need to follow the ComfyUI version of SD 3.5 workflow to download and install the model.

Example Flux: InPaint(LanPaint K Sampler, 5 steps of thinking)

View Workflow & Masks Model Used in This Example (Note: Prompt First mode is disabled on Flux. As it does not use CFG guidance.)

Example SDXL 0: Character Consistency (Side View Generation) (LanPaint K Sampler, 5 steps of thinking)

View Workflow & Masks Model Used in This Example

(Tricks 1: You can emphasize the character by copy it's image multiple times with Photoshop. Here I have made one extra copy.)

(Tricks 2: Use prompts like multiple views, multiple angles, clone, turnaround. Use LanPaint's Prompt first mode (does not support Flux))

(Tricks 3: Remeber LanPaint can in-paint: Mask non-consistent regions and try again!)

Example SDXL 1: Basket to Basket Ball (LanPaint K Sampler, 2 steps of thinking).

View Workflow & Masks Model Used in This Example

Example SDXL 2: White Shirt to Blue Shirt (LanPaint K Sampler, 5 steps of thinking)

View Workflow & Masks Model Used in This Example

Example SDXL 3: Smile to Sad (LanPaint K Sampler, 5 steps of thinking)

View Workflow & Masks Model Used in This Example

Example SDXL 4: Damage Restoration (LanPaint K Sampler, 5 steps of thinking)

View Workflow & Masks Model Used in This Example

Example SDXL 5: Huge Damage Restoration (LanPaint K Sampler, 20 steps of thinking)

View Workflow & Masks Model Used in This Example

Check more for use cases like inpaint on fine tuned models and face swapping, thanks to Amazon90.

How to Use These Examples:

1. Navigate to the example folder (i.e example_1) by clicking View Workflow & Masks, download all pictures.
2. Drag InPainted_Drag_Me_to_ComfyUI.png into ComfyUI to load the workflow.
3. Download the required model from Civitai by clicking Model Used in This Example.
4. Load the model into the "Load Checkpoint" node.
5. Upload Original_No_Mask.png to the "Load image" node in the "Original Image" group (far left).
6. Upload Masked_Load_Me_in_Loader.png to the "Load image" node in the "Mask image for inpainting" group (second from left).
7. Queue the task, you will get inpainted results from three methods:
   • VAE Encode for Inpainting (middle),
   • Set Latent Noise Mask (second from right),
   • LanPaint (far right).
8. You also get an output from "masked blend" node, which copy the original image and paste onto the unmasked part of output. It is useful if you want unmasked region to match original picture pixel perfectly.

Compare and explore the results from each method!

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: The most basic and easy to use sampler for inpainting.
• LanPaint KSampler (Advanced): Full control of all parameters.

LanPaint KSampler

Simplified interface with recommended defaults:

• Steps: 20 - 50. More steps will give more "thinking" and better results.
• LanPaint NumSteps: The turns of thinking before denoising. Recommend 5 for most of tasks ( which means 5 times slower than sampling without thinking). Use 10 for more challenging tasks.
• LanPaint Prompt mode: Image First mode and Prompt First mode. Image First mode focuses on the image, inpaint based on image context (maybe ignore prompt), while Prompt First mode focuses more on the prompt. Use Prompt First mode for tasks like character consistency. (Technically, it Prompt First mode change CFG scale to negative value in the BIG score to emphasis prompt, which will costs image quality.)

LanPaint KSampler (Advanced)

Full parameter control: Key Parameters

| Parameter | Range | Description | |-----------|-------|-------------| | Steps | 0-100 | Total steps of diffusion sampling. Higher means better inpainting. Recommend 20-50. | | LanPaint_NumSteps | 0-20 | Reasoning iterations per denoising step ("thinking depth"). Easy task: 2-5. Hard task: 5-10 | | LanPaint_Lambda | 0.1-50 | Content alignment strength (higher = stricter). Recommend 4.0 - 10.0 | | LanPaint_StepSize | 0.1-1.0 | The StepSize of each thinking step. Recommend 0.1-0.5. | | LanPaint_Beta | 0.1-2.0 | The StepSize ratio between masked / unmasked region. Small value can compensate high lambda values. Recommend 1.0 | | LanPaint_Friction | 0.0-100.0 | The friction of Langevin dynamics. Higher means more slow but stable, lower means fast but unstable. Recommend 10.0 - 20.0| | LanPaint_EarlyStop | 0-10 | Stop LanPaint iteration before the final sampling step. Helps to remove artifacts in some cases. Recommend 1-5| | LanPaint_PromptMode | Image First / Prompt First | Image First mode focuses on the image context, maybe ignore prompt. Prompt First mode focuses more on the prompt. |

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

LanPaint Mask Blend

This node blends the original image with the inpainted image based on the mask. It is useful if you want the unmasked region to match the original image pixel perfectly.

LanPaint KSampler (Advanced) Tuning Guide

For challenging inpainting tasks:

1️⃣ Boost Quality Increase total number of sampling steps (very important!), LanPaint_NumSteps (thinking iterations) or LanPaint_Lambda if the inpainted result does not meet your expectations.

2️⃣ Boost Speed If you want better results but still need fewer steps, consider: - Increasing LanPaint_StepSize to speed up the thinking process. - Decreasing LanPaint_Friction to make the Langevin dynamics converges more faster.

3️⃣ Fix Unstability:
If you find the results have wired texture, try

• Reduce LanPaint_Friction to make the Langevin dynamics more stable.
• Reduce LanPaint_StepSize to use smaller step size.
• Reduce LanPaint_Beta if you are using a high lambda value.

⚠️ Notes:

• For effective tuning, fix the seed and adjust parameters incrementally while observing the results. This helps isolate the impact of each setting. Better to do it with a batche of images to avoid overfitting on a single image.

ToDo

• Try Implement Detailer
• Provide inference code on without GUI.

Contribute

• 2025/03/06: Bug Fix for str not callable error and unpack error. Big thanks to jamesWalker55 and EricBCoding.

Citation

@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}, 
}