What is LanPaint?

LanPaint is a high-quality image restoration tool for Stable Diffusion models, enabling precise image repair and replacement without additional training. It optimizes restoration effects through multi-round iterative inference, supporting seamless and accurate results. LanPaint offers easy integration, compatible with ComfyUI workflows, where users can replace the default sampler node to use it. It provides various parameter adjustments to adapt to different complexity levels of restoration tasks, such as adjusting inference steps and content alignment strength. LanPaint is suitable for various scenarios, from simple replacements to complex damage repairs, making it a powerful tool for enhancing image generation quality.

Main Features of LanPaint

• Zero-Training Image Restoration: No additional training required; seamlessly works with any Stable Diffusion model (including custom models) for high-quality image restoration.
• Simple Integration: Fully compatible with ComfyUI's KSampler workflow, allowing users to easily replace the default sampler node and get started quickly.
• High-Quality Restoration: Optimizes the connection between restored areas and the original image through multi-round iterative inference, achieving seamless and natural restoration effects.
• Flexible Parameter Adjustment: Offers various advanced parameters (e.g., inference steps, content alignment strength, noise mask) for fine-tuning based on task complexity.

Technical Principles of LanPaint

• Iterative Inference: Performs multiple rounds of iterative inference (controlled by the LanPaint_NumSteps parameter) before each denoising step, simulating the model's "thinking" process to gradually optimize the generated content of the restored area.
• Content Alignment and Constraints: Controls the alignment strength between restored and un-restored areas based on the LanPaint_Lambda parameter, ensuring a natural visual transition and avoiding noticeable seams.
• Dynamic Noise Mask Adjustment: Dynamically adjusts the noise mask strength (controlled by LanPaint_StepSize) during the iteration process to better guide the model in generating the restored content, preventing over-generation and distortion.
• Advanced Parameter Optimization: Adjusts parameters like LanPaint_cfg_BIG (CFG scale of the restored area) and LanPaint_Friction (friction coefficient) to optimize restoration effects, balancing quality and generation speed.
• Binary Mask Processing: Requires input masks to be binary (values of 0 or 1) to avoid generation issues caused by transparency or gradients, ensuring clear and well-defined boundaries for the restored area.

LanPaint Project Address

• GitHub Repository: https://github.com/scraed/LanPaint

Application Scenarios of LanPaint

• Image Restoration and Damage Recovery: Used for restoring old photos, repairing damaged images, or removing scratches and stains to restore image integrity and clarity.
• Content Replacement and Editing: Quickly replaces specific elements in images, such as changing clothing colors or replacing items in a scene, enabling creative image editing or visual effects optimization.
• Art Creation and Design: Modifies or refines local details in artworks or adjusts image content based on creative needs, helping artists and designers quickly realize their ideas.
• Advertising and Commercial Image Processing: Quickly adjusts background, props, or character elements in product display images for different marketing needs, enhancing visual appeal.
• Video Frame Restoration and Editing: Used for restoring key frames in videos, optimizing or repairing video content, such as removing distracting elements or repairing damaged frames.