Lama Cleaner
Lama Cleaner is an open-source image inpainting tool built around the LaMa (Large Mask Inpainting) model, designed for removing unwanted objects, watermarks, text overlays, and blemishes from photographs with minimal effort. Developed by Sanster as an accessible desktop application, it provides a user-friendly brush-based interface where users simply paint over the area they want removed, and the AI fills the region with contextually appropriate content that blends seamlessly with the surrounding image. The underlying LaMa model uses a fast Fourier convolution-based architecture that excels at handling large masked areas, a common weakness in traditional inpainting approaches. Unlike many AI tools that require cloud processing, Lama Cleaner runs entirely locally on the user's machine, ensuring privacy and eliminating subscription costs. The tool supports multiple inpainting backends beyond LaMa, including LDM, ZITS, MAT, and Stable Diffusion-based models, giving users flexibility to choose the best engine for their specific task. It handles various image formats and can process both photographs and illustrations effectively. Common use cases include cleaning up travel photos by removing tourists, erasing power lines or signage from architectural shots, removing date stamps from scanned photographs, and eliminating skin blemishes in portraits. The tool is available as a Python package installable via pip and also offers a web-based interface for browser access. Its combination of powerful AI-driven inpainting, local processing, and zero cost makes it an essential utility for photographers, designers, and content creators who need quick object removal capabilities.
Key Highlights
Multiple AI Model Support
Supports multiple inpainting models including LaMa, Stable Diffusion inpainting, MAT and other community models, allowing selection of the best model for each scenario
Simple Web-Based Interface
An intuitive web-based interface where users can easily edit images by painting over areas they want to remove
Fully Local and Private
All processing occurs on local hardware, no images are sent to servers and user privacy is fully preserved
Free Photoshop Alternative
Provides professional-quality inpainting as a free and open-source alternative to Photoshop's Content-Aware Fill feature
About
Lama Cleaner (now rebranded as IOPaint) is an open-source AI-powered image inpainting and editing tool designed for removing unwanted objects, repairing damaged areas, and filling in missing image regions. Initially developed by Sanghyun Son with community contributions, the tool started with the LaMa (Large Mask Inpainting) model as its foundation and has evolved into a comprehensive image editing platform supporting multiple model backends and diverse editing capabilities. Its combination of ease of use and privacy-focused architecture gives it a unique position among image editing tools.
On the technical side, Lama Cleaner offers multiple model backends tailored to different inpainting needs and content types. The default LaMa model utilizes Fourier convolutions and a wide receptive field architecture that excels at filling large masked areas with coherent content. Additionally, alternative models including Stable Diffusion inpainting, MAT (Mask-Aware Transformer), ZITS, FcF, and manga-specific inpainting are supported. Each model demonstrates superior performance in different scenarios: LaMa excels at large area cleanup, Stable Diffusion at creative content replacement guided by text prompts, and MAT at preserving high-resolution details. Users can select the most appropriate model based on their specific task requirements and content type, switching between models seamlessly.
The user interface operates as a browser-based application running on a local server, ensuring that images never leave the user's machine and privacy is maintained throughout the entire editing process. Features include brush-based mask drawing, Segment Anything Model (SAM) integration for automatic object selection, and interactive preview capabilities for real-time feedback. Objects can be selected by drawing bounding boxes or clicking on them directly for precise targeting. Additional processing modules including Remove Background, Face Restoration, and Super Resolution are integrated, enabling comprehensive image editing workflows within a single tool. The interface design is intuitive, enabling effective results without requiring technical expertise.
Application domains encompass both personal and professional scenarios across a broad spectrum. Removing unwanted people or objects from photographs, repairing scratches and stains in vintage photos, cleaning unwanted items from real estate photography, removing background elements from product photos, and visual editing for social media represent the most common use cases. For the e-commerce sector, command-line interface and API support enable batch image processing at production scale. Institutional applications including digital archive restoration, museum digitization, and historical document repair are also effectively supported by the platform.
Lama Cleaner's privacy-focused architecture serves as a key differentiator that sets it apart from cloud-based competitors. All processing occurs locally on the user's machine, with no requirement to upload images to any cloud server whatsoever. This characteristic makes it ideal for corporate environments, healthcare applications, legal document editing, and other privacy-sensitive contexts where image data must remain under strict local control. Docker container support provides easy installation and isolated runtime environments for enterprise deployment scenarios, simplifying integration into corporate IT infrastructure.
In terms of performance and accessibility, Lama Cleaner operates in both CPU and GPU modes to accommodate different hardware configurations. GPU mode with NVIDIA CUDA significantly reduces processing times for production workloads, while CPU mode ensures accessibility for users without dedicated GPU hardware. Easy installation via pip, Docker support, and comprehensive API documentation make it developer-friendly for integration into custom workflows and automated pipelines. Released under the Apache 2.0 license, the tool is freely available for all types of use including commercial projects. Lama Cleaner maintains its position as the most comprehensive and user-friendly option among open-source image inpainting tools, providing a holistic solution for image editing needs across diverse domains.
Use Cases
Object Removal
Quickly removing unwanted objects, people, watermarks or text elements from photographs
Photo Cleanup
Cleaning up skin blemishes, spots and scratches to prepare professional-looking portraits
Old Photo Repair
Repairing torn, scratched or damaged old photos by filling missing areas with AI
Content Preparation
Preparing professional visuals by cleaning distracting elements from images for social media and web content
Pros & Cons
Pros
- Strong object removal — fills large areas naturally with LaMa model
- Free and open source — can run locally
- Multiple inpainting model support — LaMa, MAT, ZITS, and more
- Easy masking with brush tool via web-based interface
- Output preserving EXIF data
Cons
- Texture repetition can occur in complex backgrounds
- Context loss in removal of large objects
- Slow processing without GPU
- Limited batch processing support
Technical Details
Parameters
N/A
Architecture
Multi-model framework supporting LaMa, MAT, ZITS and Stable Diffusion inpainting
Training Data
N/A (uses pre-trained models)
License
Apache 2.0
Features
- LaMa Large Mask Inpainting
- Stable Diffusion Text-Guided Inpainting
- MAT Mask-Aware Transformer
- Web-Based Brush Interface
- CPU and GPU Processing Support
- Batch Image Processing
Benchmark Results
| Metric | Value | Compared To | Source |
|---|---|---|---|
| FID Score (Places2, 512x512) | 10.3 | CoModGAN: 11.5 | LaMa Paper (WACV 2022) |
| İşleme Hızı (512x512, GPU) | ~0.2s | SD Inpainting: ~3-5s | LaMa GitHub Repository |
| LPIPS Score (Places2) | 0.074 | CoModGAN: 0.082 | LaMa Paper (WACV 2022) |
| Max Çözünürlük | 2K+ (otomatik crop ile) | — | LaMa Cleaner GitHub |
Available Platforms
Frequently Asked Questions
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