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".
We encourage you to try it out and share your feedback through issues or discussions, as your input will help us enhance the algorithm's performance and stability.
- 🎨 Zero-Training Inpainting - Works immediately with ANY SD model (with/without ControlNet), and Flux model! even custom models you've trained yourself
- 🛠️ Simple Integration - Same workflow as standard ComfyUI KSampler
- 🎯 True Blank-Slate Generation - No need to set default denoise at 0.7 (preserving 30% original pixels in masks) used in conventional methods: 100% new content creation, No "painting over" existing content.
- 🌈 Not only inpaint: You can even use it as a simple way to generate consistent characters.
LanPaint introduces 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.
- 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.
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.)
You need to install ComfyUI GGUF in order to load the models. Make sure you have the latest (nightly at 2025/04/16) comfyui installed. The following models are needed for Hidream:
View Workflow & Masks
Model Used in This Example
View Workflow & Masks
Model Used in This Example
View Workflow & Masks
Model Used in This Example
View Workflow & Masks
Model Used in This Example
View Workflow & Masks
Model Used in This Example
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.)
(Tricks 3: Remeber LanPaint can in-paint: Mask non-consistent regions and try again!)
View Workflow & Masks
Model Used in This Example
(Note: Use CFG scale 1.0 for Flux as it don't use CFG. LanPaint_cfg_BIG is also disabled on Flux)
- Navigate to the example folder (i.e example_1) by clicking View Workflow & Masks, download all pictures.
- Drag InPainted_Drag_Me_to_ComfyUI.png into ComfyUI to load the workflow.
- Download the required model from Civitai by clicking Model Used in This Example.
- Load the model into the "Load Checkpoint" node.
- Upload Original_No_Mask.png to the "Load image" node in the "Original Image" group (far left).
- Upload Masked_Load_Me_in_Loader.png to the "Load image" node in the "Mask image for inpainting" group (second from left).
- 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).
Compare and explore the results from each method!
- Install ComfyUI: Follow the official ComfyUI installation guide to set up ComfyUI on your system. Or ensure your ComfyUI version > 0.3.11.
- 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:
Alternatively, clone this repository into the
https://github.com/scraed/LanPaint.gitComfyUI/custom_nodesfolder.
- 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!
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.
Simplified interface with recommended defaults:
- Steps: 50+ recommended
- LanPaint NumSteps: The turns of thinking before denoising. Recommend 5 for most of tasks.
- LanPaint EndSigma: The noise level below which thinking is disabled. Recommend 0.6 for realistic style (tested on Juggernaut-xl), 3.0 for anime style (tested on Animagine XL 4.0)
The default settings are tested on Animagine XL 4.0 and Juggernaut-xl. Other model might need some paramter tuning. Please raise issue or share your own setting if it doesn't work on your model.
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"). Easy task: 1-2. Hard task: 5-10 |
LanPaint_Lambda |
0.1-50 | Content alignment strength (higher = stricter). Recommend 8.0 |
LanPaint_StepSize |
0.1-1.0 | The StepSize of each thinking step. Recommend 0.5. |
LanPaint_EndSigma |
0.0-20.0 | The noise level below which thinking is disabled. recommend 0.3 - 3. High value is faster, but may damage quality. Low value gives more thinking but might make the output blurry. |
LanPaint_cfg_BIG |
-20-20 | CFG scale used when aligning masked and unmasked region (positive value tends to ignores promts, negative value enhances prompts.). Recommend 8 for seamless inpaint (i.e limbs, faces) when prompt is not important. -0.5 when prompt is important, like character consistency (i.e multiple view) |
For detailed descriptions of each parameter, simply hover your mouse over the corresponding input field to view tooltips with additional information.
For challenging inpainting tasks:
1️⃣ Primary Adjustments:
- Decrease LanPaint_endsigma increase LanPaint_NumSteps (thinking iterations) if the inpainted area is not seamless.
2️⃣ Secondary Tweaks:
- Boost LanPaint_Lambda (bidirectional guidance scale) will force the masked/unmasked region to align more closely.
- If the output is blurry, increase LanPaint_endsigma to turn off thinking at the end of denoising. OR decrease LanPaint_StepSize to decrease thinking step size.
- If prompt is not that important, try increase LanPaint_cfg_BIG(cfg scale used for unmasked region, default -0.5 ) to 8 for better inpainting.
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 like blurry/wired texture.
- 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. Better to do it with a batche of images to avoid overfitting on a single image.
- Fix compatibility issue with Flux Guidance that causing performance degradation on Flux models.
- Fix SD 3.5 compatibility problems
- Try Implement Detailer
- 2025/03/06: Bug Fix for str not callable error and unpack error. Big thanks to jamesWalker55 and EricBCoding.
Help us improve LanPaint! 🚀 Report bugs, share example cases, or contribute your personal parameter settings to benefit the community.
@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},
}