{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-sanster--iopaint","slug":"sanster--iopaint","name":"IOPaint","type":"webapp","url":"https://www.iopaint.com/","page_url":"https://unfragile.ai/sanster--iopaint","categories":["image-generation"],"tags":["inpainting","lama","latent-diffusion","mat","pytorch","stable-diffusion","zits"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-sanster--iopaint__cap_0","uri":"capability://image.visual.unified.model.management.with.multi.backend.inpainting","name":"unified model management with multi-backend inpainting","description":"IOPaint's ModelManager class provides a unified interface to switch between and orchestrate different inpainting model implementations (LAMA, Stable Diffusion, BrushNet, PowerPaint, MAT, ZITS) through a single abstraction layer. The system dynamically loads model weights based on user selection and handles GPU/CPU/Apple Silicon device placement automatically, enabling seamless model switching without restarting the application.","intents":["Switch between different inpainting models without reloading the application","Support CPU, GPU, and Apple Silicon inference transparently","Initialize and manage model lifecycle across multiple model architectures","Provide consistent API for diverse model types with different input/output signatures"],"best_for":["Developers building multi-model image editing applications","Teams deploying IOPaint across heterogeneous hardware (CPU/GPU/Apple Silicon)"],"limitations":["Model switching requires brief inference latency (~1-3 seconds) as weights are loaded into memory","No built-in model quantization or pruning — full precision models consume significant VRAM","Limited to models with existing IOPaint implementations; custom models require extending ModelManager class"],"requires":["Python 3.8+","PyTorch 1.9+ with appropriate device support (CUDA/MPS)","Model weights downloaded or accessible via Hugging Face Hub"],"input_types":["PIL Image objects","NumPy arrays (uint8, shape HxWx3 or HxWx4)","Binary image data (JPEG, PNG)"],"output_types":["PIL Image objects","NumPy arrays (uint8, HxWx3 or HxWx4)"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sanster--iopaint__cap_1","uri":"capability://image.visual.interactive.mask.generation.with.plugin.based.segmentation","name":"interactive mask generation with plugin-based segmentation","description":"IOPaint's plugin system enables mask generation through modular, pluggable components that can perform interactive segmentation, background removal, and other mask-based operations. Plugins are loaded dynamically and can be chained together; the system distinguishes between mask-generating plugins (segmentation, background removal) and image-generating plugins (super-resolution, face restoration), allowing flexible composition of preprocessing and postprocessing steps.","intents":["Generate precise masks for inpainting without manual brush strokes","Remove backgrounds automatically before inpainting","Chain multiple mask operations (e.g., segment object, then refine edges)","Extend inpainting with custom segmentation algorithms via plugins"],"best_for":["Users performing batch object removal without manual masking","Developers building custom segmentation pipelines","Teams integrating specialized segmentation models (SAM, YOLO, etc.)"],"limitations":["Plugin execution is sequential, not parallel — chaining multiple plugins adds cumulative latency","No built-in plugin dependency management — circular dependencies or version conflicts must be handled manually","Plugin API is Python-only; integrating C++ or CUDA-optimized segmentation requires Python bindings"],"requires":["Python 3.8+","Plugin dependencies (e.g., rembg for background removal, specific segmentation model weights)","Sufficient VRAM if plugins use GPU acceleration"],"input_types":["PIL Image objects","NumPy arrays (uint8, HxWx3 or HxWx4)"],"output_types":["Binary masks (NumPy arrays, uint8, HxW)","Segmentation maps (NumPy arrays with class indices)"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sanster--iopaint__cap_10","uri":"capability://data.processing.analysis.multi.format.image.input.output.with.automatic.format.conversion","name":"multi-format image input/output with automatic format conversion","description":"IOPaint accepts and outputs images in multiple formats (JPEG, PNG, WebP, BMP) with automatic format detection and conversion. The system uses PIL (Python Imaging Library) for format handling, enabling seamless conversion between formats without explicit user configuration, and supports both 8-bit and 16-bit color depths.","intents":["Process images in any common format without manual conversion","Export results in preferred format (JPEG for compression, PNG for lossless)","Handle images with transparency (PNG, WebP with alpha channel)","Preserve image metadata (EXIF) during processing"],"best_for":["Users working with diverse image sources and formats","Batch processing workflows requiring format flexibility","Teams integrating IOPaint into heterogeneous image pipelines"],"limitations":["JPEG compression is lossy — inpainting results may show compression artifacts","EXIF metadata is not automatically preserved during inpainting","16-bit color depth is not fully supported — images are converted to 8-bit internally","Animated formats (GIF, APNG) are not supported — only first frame is processed"],"requires":["PIL/Pillow 8.0+","Input images in supported formats (JPEG, PNG, WebP, BMP)"],"input_types":["JPEG images","PNG images (with or without alpha channel)","WebP images","BMP images"],"output_types":["JPEG images (configurable quality)","PNG images (lossless)","WebP images"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sanster--iopaint__cap_11","uri":"capability://data.processing.analysis.gpu.memory.optimization.with.model.quantization.and.device.management","name":"gpu memory optimization with model quantization and device management","description":"IOPaint optimizes GPU memory usage through automatic device placement (CPU/GPU/Apple Silicon) and support for model quantization (fp16, int8) to reduce memory footprint. The system detects available hardware and automatically selects appropriate precision levels, enabling inference on devices with limited VRAM (e.g., 2GB on mobile GPUs) that would otherwise be infeasible with full-precision models.","intents":["Run inpainting on GPUs with limited VRAM (2-4GB)","Reduce model memory footprint through quantization","Automatically detect and utilize available hardware (CUDA, MPS, CPU)","Enable inference on Apple Silicon Macs without manual device configuration"],"best_for":["Users with limited GPU memory (laptop GPUs, mobile devices)","Teams deploying IOPaint on heterogeneous hardware","Developers optimizing for inference latency and memory efficiency"],"limitations":["Quantization (fp16, int8) reduces output quality slightly — artifacts may be visible in high-detail regions","Automatic device selection may not be optimal for all hardware configurations — manual override may be needed","No built-in memory profiling — users must estimate memory requirements manually","Quantized models may have slower inference on some hardware due to kernel overhead"],"requires":["PyTorch 1.9+ with quantization support","GPU with at least 2GB VRAM (for quantized models) or 6GB+ (for full-precision models)","CUDA 11.0+ (for NVIDIA GPUs) or MPS support (for Apple Silicon)"],"input_types":["Model precision selection (fp32, fp16, int8)","Device selection (auto, cuda, cpu, mps)"],"output_types":["Inpainted images with quality dependent on quantization level"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sanster--iopaint__cap_12","uri":"capability://image.visual.configurable.inference.parameters.with.guidance.scale.and.diffusion.steps","name":"configurable inference parameters with guidance scale and diffusion steps","description":"IOPaint exposes key diffusion inference parameters (guidance scale, diffusion steps, strength) as user-adjustable controls, enabling fine-grained control over inpainting quality and speed tradeoffs. Guidance scale controls how strongly the model adheres to the prompt, diffusion steps control inference quality (more steps = higher quality but slower), and strength controls how much the inpainting modifies the original image.","intents":["Adjust guidance scale to balance prompt adherence with image coherence","Trade off inference speed against quality by adjusting diffusion steps","Control inpainting intensity with strength parameter","Experiment with different parameter combinations to achieve desired results"],"best_for":["Advanced users fine-tuning inpainting results","Developers building parameter optimization workflows","Teams exploring parameter sensitivity for specific use cases"],"limitations":["Parameter effects are non-intuitive — users must experiment to understand optimal values","No built-in parameter recommendations based on image characteristics","Extreme parameter values (very high guidance scale, very low steps) can produce artifacts","Parameter effects vary significantly across different model architectures"],"requires":["IOPaint web UI or API access","Understanding of diffusion model parameters (optional but helpful)"],"input_types":["Numeric parameters: guidance_scale (1.0-20.0), steps (1-100), strength (0.0-1.0)"],"output_types":["Inpainted images with quality/speed tradeoff determined by parameters"],"categories":["image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sanster--iopaint__cap_2","uri":"capability://image.visual.stable.diffusion.based.object.replacement.and.outpainting","name":"stable diffusion-based object replacement and outpainting","description":"IOPaint integrates Stable Diffusion and its variants (including BrushNet and PowerPaint) to enable content-aware object replacement and outpainting (extending images beyond original boundaries). The system uses latent diffusion to generate new content conditioned on masked regions and optional text prompts, supporting both inpainting (replacing masked content) and outpainting (extending canvas) workflows through a unified diffusion interface.","intents":["Replace unwanted objects with AI-generated content matching the image context","Extend images beyond their original boundaries using diffusion-based outpainting","Generate text within images using AnyText or similar text-generation models","Fine-tune diffusion behavior with strength and guidance scale parameters"],"best_for":["Content creators performing creative image editing and composition","Developers building AI-powered image editing applications","Teams automating object removal and image extension workflows"],"limitations":["Diffusion inference is computationally expensive — typical inpainting takes 5-30 seconds on GPU, much longer on CPU","Generated content quality depends heavily on prompt quality and model fine-tuning; generic prompts produce generic results","Outpainting can produce artifacts at image boundaries if the original image has strong directional patterns","No built-in semantic understanding — the model cannot reason about object relationships or spatial constraints"],"requires":["Python 3.8+","PyTorch with CUDA/MPS support (CPU inference is impractical)","6GB+ VRAM for fp32 models, 3GB+ for fp16 quantized models","Stable Diffusion model weights (1.5-2GB per model variant)"],"input_types":["PIL Image objects","NumPy arrays (uint8, HxWx3)","Binary masks (uint8, HxW)","Text prompts (string)"],"output_types":["PIL Image objects","NumPy arrays (uint8, HxWx3)"],"categories":["image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sanster--iopaint__cap_3","uri":"capability://image.visual.traditional.inpainting.with.lama.mat.and.zits.models","name":"traditional inpainting with lama, mat, and zits models","description":"IOPaint integrates traditional non-diffusion inpainting models (LAMA, MAT, ZITS) that use convolutional neural networks and attention mechanisms to perform fast, deterministic object removal. These models are optimized for speed and produce consistent results without the stochasticity of diffusion models, making them suitable for real-time or batch processing workflows where inference latency is critical.","intents":["Remove unwanted objects quickly with deterministic, non-stochastic results","Perform real-time or near-real-time inpainting for interactive editing","Process images in batch without GPU memory constraints of diffusion models","Achieve consistent results across multiple runs without sampling variance"],"best_for":["Users prioritizing speed over creative content generation","Batch processing workflows requiring deterministic, repeatable results","Developers building real-time image editing applications with latency constraints","Teams with limited GPU memory or CPU-only deployment requirements"],"limitations":["Cannot generate creative content or replace objects with context-aware alternatives — only removes/inpaints with plausible textures","Performance degrades on large masks or complex object boundaries","LAMA and MAT are less effective on semantic objects (faces, text) compared to diffusion models","No text prompt support — inpainting is purely content-agnostic"],"requires":["Python 3.8+","PyTorch 1.9+","Model weights for LAMA, MAT, or ZITS (100-500MB each)","2GB+ VRAM for GPU acceleration (CPU inference is feasible but slow)"],"input_types":["PIL Image objects","NumPy arrays (uint8, HxWx3)","Binary masks (uint8, HxW)"],"output_types":["PIL Image objects","NumPy arrays (uint8, HxWx3)"],"categories":["image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sanster--iopaint__cap_4","uri":"capability://tool.use.integration.fastapi.based.rest.api.server.with.socket.io.real.time.progress","name":"fastapi-based rest api server with socket.io real-time progress","description":"IOPaint exposes a FastAPI-based HTTP API server that provides RESTful endpoints for image processing operations, complemented by a Socket.IO server for real-time progress updates and streaming results. The backend coordinates model management, plugin execution, and image processing through a unified API interface, enabling both synchronous HTTP requests and asynchronous WebSocket-based progress tracking.","intents":["Integrate IOPaint inpainting capabilities into external applications via REST API","Stream real-time progress updates during long-running inpainting operations","Build custom frontends or CLI tools that communicate with IOPaint backend","Deploy IOPaint as a microservice accessible over HTTP"],"best_for":["Developers building custom image editing applications that need inpainting as a service","Teams deploying IOPaint as a backend microservice in larger systems","Users building CLI tools or batch processing pipelines that interact with IOPaint"],"limitations":["API is synchronous for HTTP endpoints — long-running inpainting requests block until completion (no request queuing or job scheduling)","No built-in authentication or rate limiting — requires external reverse proxy (nginx, Caddy) for production security","Socket.IO progress updates are fire-and-forget; no guarantee of delivery if client disconnects","API schema is not formally versioned — breaking changes may occur between releases"],"requires":["Python 3.8+","FastAPI 0.68+","Python-socketio 5.0+","Network access to IOPaint server (default localhost:8080)"],"input_types":["JSON payloads with base64-encoded images","Multipart form data with image files","JSON configuration objects (model name, parameters)"],"output_types":["JSON responses with base64-encoded result images","WebSocket messages with progress updates (0-100%)","HTTP status codes and error messages"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sanster--iopaint__cap_5","uri":"capability://image.visual.web.ui.with.interactive.mask.drawing.and.parameter.tuning","name":"web ui with interactive mask drawing and parameter tuning","description":"IOPaint provides a web-based user interface built with modern frontend frameworks that enables interactive image editing through brush-based mask drawing, real-time parameter adjustment (guidance scale, strength, steps), and visual feedback. The UI communicates with the FastAPI backend via REST and WebSocket APIs, providing a responsive editing experience with live preview and undo/redo capabilities.","intents":["Draw masks interactively on images using brush tools with adjustable size and opacity","Adjust inpainting parameters (guidance scale, diffusion steps, strength) in real-time with live preview","Upload images and manage editing history with undo/redo","View real-time progress updates during inpainting operations"],"best_for":["Non-technical users performing interactive image editing","Content creators needing visual feedback during parameter tuning","Teams deploying IOPaint as a self-hosted web application"],"limitations":["Large images (>4K resolution) may cause browser performance issues due to canvas rendering overhead","Brush-based masking is less precise than algorithmic segmentation for complex object boundaries","No multi-user collaboration — each user session is isolated","Browser memory constraints limit image size and undo/redo history depth"],"requires":["Modern web browser (Chrome, Firefox, Safari, Edge)","JavaScript enabled","Network connectivity to IOPaint backend server"],"input_types":["Image files (JPEG, PNG, WebP) uploaded via file picker","Mouse/touch input for brush-based mask drawing","Numeric parameters (guidance scale, steps, strength)"],"output_types":["Rendered images displayed in browser canvas","Downloadable image files (PNG, JPEG)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sanster--iopaint__cap_6","uri":"capability://automation.workflow.batch.processing.and.cli.automation.for.headless.workflows","name":"batch processing and cli automation for headless workflows","description":"IOPaint provides a command-line interface (CLI) that enables batch processing of images without the web UI, supporting scripted automation workflows. The CLI accepts image paths, mask paths, model selection, and parameters as arguments, enabling integration into shell scripts, Python automation frameworks, and CI/CD pipelines for large-scale image processing tasks.","intents":["Process multiple images in batch without manual UI interaction","Integrate IOPaint into automated image processing pipelines","Run inpainting operations on headless servers without display output","Automate object removal or image extension as part of larger workflows"],"best_for":["DevOps teams automating image processing in CI/CD pipelines","Data scientists processing large image datasets","Developers building command-line tools that depend on inpainting"],"limitations":["No built-in job queuing or parallel processing — batch operations are sequential","CLI does not provide progress feedback for long-running operations","Error handling is basic — failures in one image do not automatically skip to the next","No support for dynamic parameter adjustment based on image characteristics"],"requires":["Python 3.8+","IOPaint installed via pip or from source","Image files accessible on the filesystem","Command-line shell (bash, zsh, PowerShell)"],"input_types":["File paths to input images (JPEG, PNG)","File paths to mask images (optional)","Command-line arguments (model name, parameters)"],"output_types":["Processed image files written to output directory","Exit codes indicating success or failure"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sanster--iopaint__cap_7","uri":"capability://automation.workflow.cross.platform.deployment.with.docker.pypi.and.native.installers","name":"cross-platform deployment with docker, pypi, and native installers","description":"IOPaint supports multiple deployment methods including PyPI package installation, Docker containers (with separate GPU and CPU variants), and native Windows installers, enabling deployment across Windows, macOS, and Linux with automatic dependency resolution. The system abstracts hardware-specific details (CUDA, MPS, CPU) through PyTorch, allowing the same codebase to run on diverse hardware without modification.","intents":["Deploy IOPaint on Windows, macOS, or Linux with minimal setup","Run IOPaint in Docker containers for reproducible, isolated environments","Install IOPaint via pip for quick local development","Distribute IOPaint to non-technical users via Windows installer"],"best_for":["DevOps teams deploying IOPaint in containerized environments","Individual developers setting up local development environments","Organizations distributing IOPaint to end users","Teams deploying across heterogeneous hardware (CPU/GPU/Apple Silicon)"],"limitations":["Docker images are large (2-4GB) due to bundled model weights and dependencies","PyPI installation requires manual CUDA/MPS setup for GPU acceleration — PyTorch CPU-only is installed by default","Windows installer is limited to Windows; macOS and Linux require alternative installation methods","No built-in update mechanism — users must manually upgrade via pip or Docker image pull"],"requires":["Python 3.8+ (for pip installation)","Docker 20.10+ (for Docker deployment)","Windows 10+ (for Windows installer)","Internet connectivity for downloading dependencies and model weights"],"input_types":["Installation commands (pip, Docker, Windows installer)","Configuration files (optional, for customizing deployment)"],"output_types":["Installed IOPaint application","Running Docker container","Executable Windows application"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sanster--iopaint__cap_8","uri":"capability://automation.workflow.state.management.and.undo.redo.for.interactive.editing.sessions","name":"state management and undo/redo for interactive editing sessions","description":"IOPaint maintains editing state across user interactions, enabling undo/redo functionality for mask drawing, parameter adjustments, and inpainting operations. The system tracks state changes in memory and provides UI controls to revert or replay operations, allowing users to experiment with different parameters and masks without losing previous work.","intents":["Undo mask drawing strokes without restarting the editing session","Redo previously undone operations","Revert inpainting results and try different parameters","Maintain editing history across multiple operations"],"best_for":["Interactive users performing exploratory image editing","Content creators iterating on multiple parameter combinations","Teams using IOPaint for collaborative editing workflows"],"limitations":["Undo/redo history is stored in browser memory — closing the browser tab clears history","No persistent session storage — editing sessions cannot be resumed after server restart","Large images with deep undo/redo history consume significant browser memory","No conflict resolution for concurrent edits if multiple users edit the same image"],"requires":["Web browser with sufficient memory for image buffers","IOPaint web UI running and accessible"],"input_types":["User interactions (brush strokes, parameter adjustments, inpainting requests)"],"output_types":["Updated UI state reflecting undo/redo operations","Rendered image preview"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sanster--iopaint__cap_9","uri":"capability://automation.workflow.file.browser.and.media.management.for.image.organization","name":"file browser and media management for image organization","description":"IOPaint includes a built-in file browser that enables users to navigate the filesystem, select images for editing, and organize processed results. The file manager integrates with the web UI, allowing users to upload, browse, and manage images without leaving the application, with support for common image formats (JPEG, PNG, WebP).","intents":["Browse and select images from the filesystem without external file managers","Upload images directly from the web UI","Organize and manage edited images within IOPaint","View image thumbnails and metadata"],"best_for":["Non-technical users preferring integrated file management","Teams managing large image collections for batch processing","Users working with images stored on the IOPaint server"],"limitations":["File browser is limited to the server's filesystem — cannot access remote storage (S3, cloud drives)","No built-in image organization features (tagging, collections, search)","Thumbnail generation adds latency for large image directories","File permissions are inherited from the IOPaint process — no per-user access control"],"requires":["Web browser with file upload support","IOPaint server with filesystem access"],"input_types":["Image files (JPEG, PNG, WebP) on the server filesystem","Uploaded image files via web UI"],"output_types":["File list with thumbnails","Selected image paths for processing"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","PyTorch 1.9+ with appropriate device support (CUDA/MPS)","Model weights downloaded or accessible via Hugging Face Hub","Plugin dependencies (e.g., rembg for background removal, specific segmentation model weights)","Sufficient VRAM if plugins use GPU acceleration","PIL/Pillow 8.0+","Input images in supported formats (JPEG, PNG, WebP, BMP)","PyTorch 1.9+ with quantization support","GPU with at least 2GB VRAM (for quantized models) or 6GB+ (for full-precision models)","CUDA 11.0+ (for NVIDIA GPUs) or MPS support (for Apple Silicon)"],"failure_modes":["Model switching requires brief inference latency (~1-3 seconds) as weights are loaded into memory","No built-in model quantization or pruning — full precision models consume significant VRAM","Limited to models with existing IOPaint implementations; custom models require extending ModelManager class","Plugin execution is sequential, not parallel — chaining multiple plugins adds cumulative latency","No built-in plugin dependency management — circular dependencies or version conflicts must be handled manually","Plugin API is Python-only; integrating C++ or CUDA-optimized segmentation requires Python bindings","JPEG compression is lossy — inpainting results may show compression artifacts","EXIF metadata is not automatically preserved during inpainting","16-bit color depth is not fully supported — images are converted to 8-bit internally","Animated formats (GIF, APNG) are not supported — only first frame is processed","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.39089708461446104,"quality":0.5,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.063Z","last_scraped_at":"2026-05-03T13:58:42.318Z","last_commit":"2025-04-29T02:13:17Z"},"community":{"stars":23042,"forks":2445,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=sanster--iopaint","compare_url":"https://unfragile.ai/compare?artifact=sanster--iopaint"}},"signature":"+vy1lqpodLYmitfgb/cf7wDHJvsy4eaAjwPWxbsE+6J0Gm518X8P/pinqKpluE/vMOaqYukA5foozI6V/iWSAA==","signedAt":"2026-06-22T08:30:12.625Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/sanster--iopaint","artifact":"https://unfragile.ai/sanster--iopaint","verify":"https://unfragile.ai/api/v1/verify?slug=sanster--iopaint","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}