{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-invoke-ai--invokeai","slug":"invoke-ai--invokeai","name":"InvokeAI","type":"repo","url":"https://invoke-ai.github.io/InvokeAI/","page_url":"https://unfragile.ai/invoke-ai--invokeai","categories":["image-generation"],"tags":["ai-art","artificial-intelligence","generative-art","image-generation","img2img","inpainting","latent-diffusion","linux","macos","outpainting","stable-diffusion","txt2img","windows"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-invoke-ai--invokeai__cap_0","uri":"capability://image.visual.text.to.image.generation.with.diffusion.model.inference","name":"text-to-image generation with diffusion model inference","description":"Generates images from natural language prompts by executing a multi-stage diffusion pipeline that progressively denoises latent representations. The system integrates Stable Diffusion models (SD1.5, SD2.0, SDXL, FLUX) through a unified invocation graph that manages model loading, conditioning, and iterative sampling with configurable schedulers and guidance scales. The backend FastAPI service orchestrates the pipeline through a node-based execution system that decouples model inference from UI concerns.","intents":["Generate high-quality images from text descriptions without manual art skills","Batch generate multiple variations of a prompt with different seeds and parameters","Integrate text-to-image generation into custom workflows via node-based composition"],"best_for":["Digital artists and designers prototyping visual concepts","Creative professionals building custom generation pipelines","Developers embedding image generation into applications"],"limitations":["VRAM requirements scale with model size (SDXL requires 8GB+, FLUX requires 12GB+)","Generation latency ranges 5-30 seconds depending on model and hardware","Quality depends on prompt engineering and model training data biases","No native support for generating text within images or precise spatial control without inpainting"],"requires":["Python 3.9+","CUDA 11.8+ or compatible GPU (or CPU fallback with severe performance penalty)","4GB+ VRAM minimum for SD1.5, 8GB+ for SDXL","Model weights downloaded (2-7GB per model depending on format)"],"input_types":["text (prompt string)","structured parameters (steps, guidance_scale, scheduler, seed, dimensions)"],"output_types":["PNG/JPEG image files","metadata JSON with generation parameters"],"categories":["image-visual","generative-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-invoke-ai--invokeai__cap_1","uri":"capability://image.visual.image.to.image.generation.with.structural.preservation","name":"image-to-image generation with structural preservation","description":"Transforms existing images by injecting them into the diffusion process at a configurable noise level (strength parameter), allowing controlled modification while preserving structural elements. The system encodes input images into latent space, applies noise based on the strength parameter, then denoises with the provided prompt to guide the transformation. This enables style transfer, content modification, and creative reinterpretation while maintaining spatial coherence from the original image.","intents":["Apply artistic styles to existing photographs or artwork","Modify specific aspects of an image while preserving overall composition","Generate variations of an image with different prompts and strength levels"],"best_for":["Concept artists iterating on existing designs","Photographers enhancing or reimagining shots","Designers exploring style variations without starting from scratch"],"limitations":["Strength parameter (0-1) is coarse-grained; fine-tuning requires multiple iterations","Structural preservation decreases as strength increases, with diminishing returns above 0.8","Cannot reliably preserve fine details like faces or text without inpainting","Latent space encoding may introduce artifacts for images with extreme aspect ratios"],"requires":["Input image file (PNG, JPEG, WebP)","Python 3.9+","CUDA 11.8+ or compatible GPU","Same model weights as txt2img"],"input_types":["image file (PNG/JPEG/WebP)","text prompt","strength parameter (0.0-1.0)","other generation parameters (steps, guidance_scale, seed)"],"output_types":["PNG/JPEG image file","metadata JSON with input image reference and transformation parameters"],"categories":["image-visual","generative-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-invoke-ai--invokeai__cap_10","uri":"capability://automation.workflow.batch.processing.and.parameter.variation.with.job.queuing","name":"batch processing and parameter variation with job queuing","description":"Enables batch processing of images through workflows with systematic parameter variation (seed ranges, prompt variations, model selection). The system queues jobs and executes them sequentially or with configurable parallelism, tracking progress and results. Users can define parameter grids (e.g., 5 seeds × 3 prompts = 15 jobs) and execute them as a single batch operation. The backend maintains a job queue with status tracking, error handling, and result aggregation.","intents":["Generate multiple variations of a prompt with different seeds for comparison","Batch process images through a workflow with parameter sweeps","Explore parameter spaces systematically (e.g., different guidance scales)","Automate repetitive generation tasks without manual intervention"],"best_for":["Researchers exploring parameter sensitivity","Artists generating large variation sets for selection","Teams running overnight batch jobs for production output"],"limitations":["Sequential execution limits throughput; parallelism requires multiple GPUs","No built-in result filtering or ranking; requires manual review of outputs","Job queue is not persisted; server restart loses queued jobs","No built-in cost estimation or resource planning for large batches"],"requires":["Python 3.9+","FastAPI backend with job queue implementation","Disk space for batch output (varies by batch size)"],"input_types":["workflow JSON","parameter grid specification (ranges, lists, combinations)","batch size and execution parameters"],"output_types":["batch result JSON with all generated images and parameters","image files organized by parameter combination","execution statistics (total time, success rate, errors)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-invoke-ai--invokeai__cap_11","uri":"capability://text.generation.language.internationalization.and.multi.language.ui.support","name":"internationalization and multi-language ui support","description":"Provides a complete internationalization (i18n) system for the React frontend, supporting multiple languages through a translation file system. The system uses a key-based translation approach where UI strings are mapped to translation keys, and language-specific JSON files provide translations. The frontend detects user locale and loads appropriate translations at startup, with fallback to English for missing translations. Users can switch languages at runtime without page reload.","intents":["Support users in their native language without requiring separate builds","Contribute translations for new languages through community contributions","Maintain consistent terminology across the UI through centralized translation files","Switch languages at runtime for testing or user preference"],"best_for":["Global teams using InvokeAI in multiple languages","Community contributors translating the UI","Users preferring non-English interfaces"],"limitations":["Translation completeness varies by language; incomplete translations fall back to English","Right-to-left (RTL) languages require additional CSS and layout adjustments","Translation updates require rebuilding the frontend; no runtime translation loading","No built-in translation management UI; translations are edited as JSON files"],"requires":["React 18+","i18n library (react-i18next or similar)","Translation JSON files for each supported language"],"input_types":["translation key (string identifier)","interpolation variables (for dynamic content)"],"output_types":["translated UI string in user's selected language","fallback to English if translation missing"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-invoke-ai--invokeai__cap_12","uri":"capability://automation.workflow.configuration.management.with.environment.based.settings","name":"configuration management with environment-based settings","description":"Manages application configuration through environment variables, configuration files, and runtime settings. The system supports multiple configuration sources (environment variables, YAML files, command-line arguments) with a precedence order. Configuration is validated at startup and provides sensible defaults for all settings. The backend exposes configuration endpoints that allow the frontend to query supported models, features, and system capabilities without hardcoding.","intents":["Configure InvokeAI for different deployment environments (development, staging, production)","Customize model paths, cache sizes, and hardware settings without code changes","Query available models and features from the frontend dynamically","Enable/disable features based on deployment configuration"],"best_for":["DevOps teams deploying InvokeAI to multiple environments","Users customizing InvokeAI for specific hardware configurations","Developers building custom deployments with feature flags"],"limitations":["Configuration changes require server restart; no hot-reload support","No built-in configuration validation UI; errors only appear at startup","Configuration precedence can be confusing with multiple sources","Sensitive configuration (API keys) requires careful handling to avoid exposure"],"requires":["Python 3.9+","Environment variables or configuration files","YAML parser (PyYAML) for file-based configuration"],"input_types":["environment variables (KEY=VALUE)","YAML configuration files","command-line arguments"],"output_types":["validated configuration object","configuration JSON exposed via API","startup logs with configuration summary"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-invoke-ai--invokeai__cap_13","uri":"capability://safety.moderation.error.handling.and.recovery.with.detailed.logging","name":"error handling and recovery with detailed logging","description":"Implements comprehensive error handling throughout the application with detailed logging for debugging. The system captures errors at multiple levels (API, service, model inference) and provides meaningful error messages to users. Long-running operations include recovery mechanisms (e.g., model reload on CUDA out-of-memory) and graceful degradation. Logs are structured with timestamps, severity levels, and context information, enabling post-mortem analysis of failures.","intents":["Diagnose generation failures through detailed error messages and logs","Recover from transient failures (CUDA OOM, network timeouts) automatically","Track system health and performance through structured logging","Debug issues in production deployments without access to the system"],"best_for":["System administrators monitoring production deployments","Developers debugging generation failures","Teams troubleshooting hardware or configuration issues"],"limitations":["Log verbosity can be overwhelming; requires filtering to find relevant information","No built-in log aggregation or analysis; requires external tools for large deployments","Error recovery is limited to specific known failure modes","Sensitive information (file paths, model names) may be exposed in logs"],"requires":["Python 3.9+","Logging configuration (Python logging module)","Log storage (file system or external service)"],"input_types":["error events from various system components","logging configuration (level, format, output)"],"output_types":["structured log entries with timestamp, level, context","error messages displayed to users","recovery actions (model reload, retry, fallback)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-invoke-ai--invokeai__cap_2","uri":"capability://image.visual.inpainting.and.outpainting.with.mask.guided.generation","name":"inpainting and outpainting with mask-guided generation","description":"Enables selective image editing by generating content only within masked regions (inpainting) or extending images beyond original boundaries (outpainting). The system accepts a mask image where white regions indicate areas to regenerate and black regions are preserved. The masked regions are encoded into latent space with noise, while unmasked regions remain frozen, allowing the diffusion process to generate contextually appropriate content that blends seamlessly with preserved areas. Outpainting extends this by automatically generating extended canvas regions.","intents":["Remove unwanted objects or people from images while maintaining background coherence","Extend images beyond original boundaries with contextually appropriate content","Perform non-destructive edits on specific image regions guided by text prompts","Create seamless composites by regenerating transition regions between images"],"best_for":["Photo editors and retouchers performing non-destructive edits","Concept artists extending compositions or removing distracting elements","Content creators generating variations of specific image regions"],"limitations":["Mask quality directly impacts output quality; soft edges or anti-aliasing can cause artifacts","Seamless blending at mask boundaries requires careful feathering and multiple iterations","Outpainting quality degrades at extreme extension ratios (>2x original dimensions)","Cannot reliably preserve fine details (faces, hands, text) in masked regions without additional guidance"],"requires":["Input image file (PNG, JPEG, WebP)","Mask image (grayscale PNG, same dimensions as input)","Python 3.9+","CUDA 11.8+ or compatible GPU","Model weights supporting inpainting (requires specific model variants)"],"input_types":["image file (PNG/JPEG/WebP)","mask image (grayscale PNG)","text prompt","generation parameters (steps, guidance_scale, seed, strength)"],"output_types":["PNG/JPEG image file with inpainted/outpainted regions","metadata JSON with mask reference and generation parameters"],"categories":["image-visual","generative-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-invoke-ai--invokeai__cap_3","uri":"capability://planning.reasoning.node.based.workflow.composition.and.execution","name":"node-based workflow composition and execution","description":"Enables users to construct custom image generation pipelines by visually connecting nodes representing discrete operations (conditioning, sampling, post-processing, upscaling, etc.) in a directed acyclic graph. Each node has a schema-driven interface with type-safe inputs/outputs validated at composition time. The backend executes the graph through a topological sort, passing outputs from upstream nodes as inputs to downstream nodes, enabling complex multi-stage workflows without code. The system serializes workflows as JSON for persistence and sharing.","intents":["Create reusable generation pipelines combining multiple operations (e.g., txt2img → upscale → inpaint)","Batch process images through custom workflows with parameter variation","Share and version control generation recipes as portable JSON files","Compose operations from different model families (e.g., SDXL for generation, RealESRGAN for upscaling)"],"best_for":["Advanced users and technical artists building production pipelines","Teams standardizing generation workflows across projects","Developers integrating InvokeAI into larger creative systems"],"limitations":["Graph execution is sequential; no native parallelization across independent branches","Debugging complex graphs requires manual inspection of intermediate outputs","No built-in version control or diff visualization for workflow changes","Performance scales linearly with graph complexity; deeply nested workflows incur cumulative latency"],"requires":["Python 3.9+","FastAPI backend running","Node schema definitions for all operations in the workflow","Model weights for all referenced models"],"input_types":["workflow JSON (schema-validated)","node parameters (type-safe, validated against node schema)","image/text inputs as specified by workflow entry nodes"],"output_types":["image files (PNG/JPEG) from terminal nodes","execution metadata (timing, node outputs, errors)","workflow JSON (for persistence/sharing)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-invoke-ai--invokeai__cap_4","uri":"capability://image.visual.unified.canvas.with.real.time.brush.based.editing","name":"unified canvas with real-time brush-based editing","description":"Provides an interactive canvas built with Konva.js that enables real-time brush-based mask creation, layer management, and visual composition. The canvas supports multiple control layers (base image, mask, brush strokes) with non-destructive editing through layer composition. Users can paint masks directly on the canvas, adjust brush size/hardness, and preview generation results in real-time. The system maintains separate layer stacks for different editing modes (inpainting, outpainting, brush refinement) and synchronizes canvas state with the backend through WebSocket updates.","intents":["Interactively create and refine masks for inpainting without external image editors","Visualize generation results in real-time on the canvas without switching applications","Manage multiple editing layers and non-destructively iterate on compositions","Perform brush-based edits with immediate visual feedback"],"best_for":["Digital artists preferring integrated editing workflows","Users without external image editing software","Teams requiring real-time collaboration on canvas edits"],"limitations":["Canvas rendering performance degrades with very large images (>4K) or complex layer stacks","Brush responsiveness depends on network latency for WebSocket updates","No advanced layer blending modes beyond basic alpha composition","Undo/redo stack is limited to recent operations; deep history requires manual state management"],"requires":["Modern web browser with WebGL support (Chrome 90+, Firefox 88+, Safari 15+)","WebSocket connection to FastAPI backend","React 18+ and Redux for state management","Konva.js 9.0+ for canvas rendering"],"input_types":["image file (PNG/JPEG/WebP) for base canvas","brush parameters (size, hardness, opacity)","layer operations (add, delete, merge, reorder)"],"output_types":["mask image (PNG, grayscale)","canvas state JSON (layer composition, brush strokes)","generated image with canvas edits applied"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-invoke-ai--invokeai__cap_5","uri":"capability://data.processing.analysis.model.management.with.format.conversion.and.caching","name":"model management with format conversion and caching","description":"Manages the lifecycle of diffusion models including discovery, download, format conversion, and in-memory caching with intelligent eviction. The system supports multiple model formats (safetensors, ckpt, diffusers) and automatically converts between formats on import. Models are cached in VRAM with LRU eviction when memory constraints are exceeded, minimizing reload latency for frequently-used models. The backend maintains a model registry with metadata (size, format, compatibility) and provides APIs for model installation, deletion, and format conversion.","intents":["Discover and install models from HuggingFace, Civitai, and other repositories","Convert model formats (e.g., ckpt to safetensors) for compatibility or optimization","Manage limited VRAM by intelligently caching models and evicting least-recently-used models","Track model metadata and compatibility across different model families (SD1.5, SDXL, FLUX)"],"best_for":["Users managing large model libraries (10+ models)","Teams standardizing on specific model formats for reproducibility","Developers building model management infrastructure"],"limitations":["Format conversion adds 5-15 minutes per model depending on size and disk I/O","VRAM caching requires manual tuning of cache size; no automatic optimization","Model discovery requires manual URL entry or integration with specific repositories","No built-in deduplication for models with identical weights but different formats"],"requires":["Python 3.9+","Disk space for model storage (2-7GB per model)","VRAM for caching (configurable, typically 2-4GB)","Network access for model downloads"],"input_types":["model URL or file path","model metadata (name, description, tags)","format specification (safetensors, ckpt, diffusers)"],"output_types":["model registry JSON with metadata","converted model files in target format","cache statistics (hit rate, eviction count)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-invoke-ai--invokeai__cap_6","uri":"capability://data.processing.analysis.gallery.and.board.based.image.organization","name":"gallery and board-based image organization","description":"Organizes generated images into boards (collections) with metadata tagging, search, and filtering capabilities. The system stores images with associated generation parameters, prompts, and custom metadata in a database-backed gallery. Users can create boards to organize images by project, style, or iteration, and perform full-text search across prompts and tags. The gallery supports batch operations (delete, move, export) and maintains image relationships (e.g., variations of the same prompt).","intents":["Organize generated images into projects or collections without manual file management","Search and filter images by prompt, parameters, or custom tags","Track generation history and reproduce results by storing complete parameter sets","Batch export or delete images based on search criteria"],"best_for":["Users generating large volumes of images (100+) requiring organization","Teams collaborating on projects and sharing image collections","Researchers tracking generation parameters for reproducibility"],"limitations":["Search performance degrades with very large galleries (10,000+ images) without indexing","No built-in image similarity search or deduplication","Metadata is stored in database; no automatic extraction from image EXIF","Board sharing requires manual export; no built-in collaboration features"],"requires":["SQLite or PostgreSQL database","Disk space for image storage (varies by volume)","Python 3.9+","FastAPI backend with database connection"],"input_types":["generated image files","generation metadata (prompt, parameters, model)","custom tags and board assignments"],"output_types":["gallery JSON with image metadata and relationships","search results (filtered image list with metadata)","batch export (ZIP file with images and metadata)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-invoke-ai--invokeai__cap_7","uri":"capability://tool.use.integration.rest.api.with.openapi.schema.generation.and.websocket.real.time.updates","name":"rest api with openapi schema generation and websocket real-time updates","description":"Exposes all InvokeAI functionality through a FastAPI REST API with automatically-generated OpenAPI (Swagger) documentation. The API uses schema-driven request/response validation through Pydantic models, enabling type-safe client generation. WebSocket connections provide real-time updates for long-running operations (image generation, model loading) without polling. The API supports both synchronous operations (model queries, gallery access) and asynchronous operations (generation, conversion) with job queuing and status tracking.","intents":["Integrate InvokeAI into external applications via REST API calls","Generate type-safe client libraries from OpenAPI schema","Monitor long-running operations in real-time via WebSocket subscriptions","Build custom frontends or CLIs on top of the InvokeAI backend"],"best_for":["Developers building applications that embed InvokeAI functionality","Teams building custom frontends or CLI tools","Integrators connecting InvokeAI to larger creative pipelines"],"limitations":["API rate limiting is not built-in; requires external proxy for production deployments","WebSocket connections are not persisted across server restarts","No built-in authentication; requires external auth layer for multi-user deployments","Large file uploads (>1GB) require chunking; no built-in resumable upload support"],"requires":["Python 3.9+","FastAPI 0.95+","HTTP client library (requests, httpx, etc.)","WebSocket client for real-time updates"],"input_types":["JSON request bodies with Pydantic-validated schemas","file uploads (multipart/form-data)","query parameters for filtering and pagination"],"output_types":["JSON response bodies with OpenAPI-documented schemas","binary image files (PNG/JPEG)","WebSocket messages (JSON events for generation progress, errors)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-invoke-ai--invokeai__cap_8","uri":"capability://image.visual.conditioning.and.control.layer.integration.for.guided.generation","name":"conditioning and control layer integration for guided generation","description":"Integrates external control signals (ControlNet, T2I-Adapter, IP-Adapter) that guide the diffusion process beyond text prompts. The system accepts control images (edge maps, depth maps, pose skeletons, etc.) and applies them as additional conditioning signals during sampling. Each control layer has configurable strength and can be combined with other controls for multi-modal guidance. The backend manages control model loading and caching separately from base models, enabling efficient composition of multiple control signals.","intents":["Guide image generation using structural information (edges, depth, pose) from reference images","Maintain spatial composition from reference images while changing content via text prompt","Combine multiple control signals (e.g., pose + depth) for fine-grained generation control","Apply style transfer while preserving structural elements from a reference image"],"best_for":["Concept artists requiring precise spatial control over generation","Animators maintaining character poses across frames","Designers composing complex scenes with multiple structural constraints"],"limitations":["Control signal quality directly impacts output quality; poor edge detection or depth estimation causes artifacts","Multiple control signals can conflict, requiring careful strength tuning","Control model inference adds 20-40% latency to generation","Not all control types are compatible with all base models (e.g., ControlNet v1 incompatible with SDXL)"],"requires":["Control model weights (separate from base model, 100MB-1GB each)","Control image or preprocessor to generate control signal from reference image","Python 3.9+","CUDA 11.8+ or compatible GPU"],"input_types":["control image (PNG/JPEG with control signal encoded)","control type specification (canny, depth, pose, etc.)","control strength (0.0-1.0)","text prompt and other generation parameters"],"output_types":["PNG/JPEG image generated with control guidance","metadata JSON with control signal reference and strength"],"categories":["image-visual","generative-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-invoke-ai--invokeai__cap_9","uri":"capability://image.visual.upscaling.and.enhancement.with.multiple.model.backends","name":"upscaling and enhancement with multiple model backends","description":"Enhances image resolution using specialized upscaling models (RealESRGAN, ESRGAN, Upscayl) that reconstruct high-frequency details. The system supports multiple upscaling backends with configurable scale factors (2x, 4x, 8x) and quality presets. Upscaling can be applied as a post-processing step in workflows or as a standalone operation. The backend manages upscaling model caching separately from diffusion models, enabling efficient composition with generation pipelines.","intents":["Increase image resolution for print or display without quality loss","Enhance details in generated images for professional output","Compose upscaling into generation workflows for end-to-end high-resolution output","Batch upscale multiple images with consistent parameters"],"best_for":["Designers and photographers requiring high-resolution output","Content creators preparing images for print or large displays","Teams building production pipelines with resolution requirements"],"limitations":["Upscaling quality plateaus at 4x; 8x upscaling introduces artifacts","Upscaling adds 5-30 seconds per image depending on scale factor and model","Cannot recover information lost in compression; works best on high-quality inputs","Different upscaling models have different quality characteristics; no automatic model selection"],"requires":["Upscaling model weights (100MB-500MB per model)","Python 3.9+","CUDA 11.8+ or compatible GPU (CPU fallback available but slow)"],"input_types":["image file (PNG/JPEG/WebP)","scale factor (2, 4, 8)","upscaling model selection (RealESRGAN, ESRGAN, etc.)"],"output_types":["PNG/JPEG image at higher resolution","metadata JSON with upscaling parameters"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-invoke-ai--invokeai__headline","uri":"capability://image.visual.ai.driven.creative.engine.for.visual.media.generation","name":"ai-driven creative engine for visual media generation","description":"InvokeAI is a powerful platform designed for generating stunning visual media using AI technologies, catering to professionals and enthusiasts alike with its intuitive web interface and advanced image generation capabilities.","intents":["best AI image generation tool","AI creative engine for artists","top platforms for visual media generation","best software for Stable Diffusion models","AI tools for generating art"],"best_for":["artists","designers","content creators"],"limitations":[],"requires":[],"input_types":["text prompts","image inputs"],"output_types":["generated images","edited images"],"categories":["image-visual"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":55,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","CUDA 11.8+ or compatible GPU (or CPU fallback with severe performance penalty)","4GB+ VRAM minimum for SD1.5, 8GB+ for SDXL","Model weights downloaded (2-7GB per model depending on format)","Input image file (PNG, JPEG, WebP)","CUDA 11.8+ or compatible GPU","Same model weights as txt2img","FastAPI backend with job queue implementation","Disk space for batch output (varies by batch size)","React 18+"],"failure_modes":["VRAM requirements scale with model size (SDXL requires 8GB+, FLUX requires 12GB+)","Generation latency ranges 5-30 seconds depending on model and hardware","Quality depends on prompt engineering and model training data biases","No native support for generating text within images or precise spatial control without inpainting","Strength parameter (0-1) is coarse-grained; fine-tuning requires multiple iterations","Structural preservation decreases as strength increases, with diminishing returns above 0.8","Cannot reliably preserve fine details like faces or text without inpainting","Latent space encoding may introduce artifacts for images with extreme aspect ratios","Sequential execution limits throughput; parallelism requires multiple GPUs","No built-in result filtering or ranking; requires manual review of outputs","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.764992635653925,"quality":0.6,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.550Z","last_scraped_at":"2026-05-03T13:58:42.318Z","last_commit":"2026-05-01T13:43:24Z"},"community":{"stars":27089,"forks":2816,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=invoke-ai--invokeai","compare_url":"https://unfragile.ai/compare?artifact=invoke-ai--invokeai"}},"signature":"uOHwP7mPln9ObI2bu0es4j8xD9++XmEK5w+a7Rmoli5EbKiaV/Yu8rsa1mDwzQgTrwDMmpTJ7T7C5cEY8kiyDw==","signedAt":"2026-06-21T07:13:23.451Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/invoke-ai--invokeai","artifact":"https://unfragile.ai/invoke-ai--invokeai","verify":"https://unfragile.ai/api/v1/verify?slug=invoke-ai--invokeai","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"}}