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.
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
Related Models
Adobe Generative Fill
Adobe Generative Fill is a generative AI feature integrated directly into Adobe Photoshop, powered by Adobe's proprietary Firefly image generation model. Introduced in 2023, it enables users to add, modify, or remove content in images using natural language text prompts within the familiar Photoshop interface. The feature works by selecting a region with any Photoshop selection tool, typing a descriptive prompt in the contextual task bar, and receiving three AI-generated variations within seconds. Generated content is placed on a separate layer, preserving Photoshop's non-destructive editing workflow that professionals rely on. A key differentiator is Firefly's training data approach, which uses exclusively licensed Adobe Stock imagery, openly licensed content, and public domain materials, providing commercial safety and IP indemnification that competing solutions cannot match. Generative Fill automatically maintains coherence with surrounding color, lighting, perspective, and texture for seamless blending. The companion Generative Expand feature enables extending images beyond their original canvas boundaries. Professional applications span advertising campaign iteration, photography post-production, real estate staging, product photography background replacement, fashion color modification, and editorial visual preparation. The feature is accessible through Photoshop's Creative Cloud subscription with a monthly generative credits system, and also available through Adobe Express and the web-based Firefly application. Content Credentials metadata indicates when AI was used, supporting transparency standards. Adobe Generative Fill represents the most commercially safe and professionally integrated approach to AI-powered image editing available today.
DALL-E Inpainting
DALL-E Inpainting is OpenAI's proprietary image editing capability that allows users to modify specific regions of existing images through natural language prompts, available through both the DALL-E web interface and the OpenAI API. Building on the DALL-E image generation architecture, the inpainting feature enables users to select rectangular or custom-shaped regions of an image and describe what should appear in the masked area, with the AI generating contextually appropriate content that blends with the surrounding image. The system understands complex spatial relationships, lighting conditions, and artistic styles to produce edits that maintain visual coherence with the original image. Key capabilities include adding new objects to scenes, replacing backgrounds, modifying clothing or accessories on people, changing weather conditions or time of day in landscapes, and removing unwanted elements. The API provides programmatic access for building automated editing pipelines and integrating inpainting into custom applications, with options for controlling output resolution and the number of generated variations. Unlike open-source alternatives, DALL-E Inpainting operates entirely in the cloud with no local GPU requirements, making it accessible to users without specialized hardware. The model benefits from OpenAI's continuous improvements and safety filters that prevent generation of harmful content. Commercial usage is permitted under OpenAI's terms of service, with generated images belonging to the user. While it requires a paid API subscription or credits-based usage, its ease of integration, consistent quality, and the backing of OpenAI's infrastructure make it a reliable choice for developers and businesses requiring scalable AI-powered image editing capabilities.
FLUX Fill
FLUX Fill is the specialized inpainting and outpainting model within the FLUX model family developed by Black Forest Labs, designed for professional-grade region editing, content filling, and image extension. Built on the 12-billion parameter Diffusion Transformer architecture that powers all FLUX models, FLUX Fill takes an input image along with a binary mask indicating the region to be modified and generates seamlessly blended content that matches the surrounding context in style, lighting, perspective, and detail level. The model excels at both inpainting tasks where masked areas within an image are filled with contextually appropriate content and outpainting tasks where image boundaries are extended to create larger compositions. FLUX Fill leverages the superior prompt adherence of the FLUX architecture, allowing users to guide the generation with text descriptions of what should appear in the masked region, providing precise creative control over the output. The model handles complex scenarios including filling regions that span multiple materials and textures, maintaining structural continuity of architectural elements, and generating photorealistic human features in masked face areas. As a proprietary model, FLUX Fill is accessible through Black Forest Labs' API and partner platforms including Replicate and fal.ai, with usage-based pricing. Professional photographers use FLUX Fill for removing unwanted elements and extending compositions, e-commerce teams employ it for product background replacement, digital artists leverage it for creative compositing, and marketing professionals use it for adapting images to different aspect ratios and formats without losing content quality.
GPT Image 1
GPT Image 1 is OpenAI's latest image generation model that integrates natively within the GPT architecture, combining language understanding with visual generation in a unified autoregressive framework. Unlike diffusion-based competitors, GPT Image 1 generates images token by token through an autoregressive process similar to text generation, enabling a conversational interface where users iteratively refine outputs through dialogue. The model excels at text rendering within images, producing legible and accurately placed typography that has historically been a weakness of diffusion models. It supports both generation from text descriptions and editing through natural language instructions, allowing users to upload images and describe desired modifications. GPT Image 1 understands complex compositional prompts with multiple subjects, spatial relationships, and specific attributes, producing coherent scenes accurately reflecting described elements. It handles diverse styles from photorealism to illustration, painting, graphic design, and technical diagrams. Editing capabilities include inpainting, style transformation, background replacement, object addition or removal, and color adjustment, all through conversational input. The model is accessible through the OpenAI API for application integration and through ChatGPT for consumer use. Safety systems prevent harmful content generation. Generated images belong to the user with full commercial rights under OpenAI's terms. GPT Image 1 represents a significant step toward multimodal AI systems seamlessly blending language and visual capabilities, making AI image creation more intuitive through natural conversation.
Quick Info
Multi-model framework supporting LaMa, MAT, ZITS and Stable Diffusion inpainting