{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"invokeai","slug":"invokeai","name":"InvokeAI","type":"repo","url":"https://github.com/invoke-ai/InvokeAI","page_url":"https://unfragile.ai/invokeai","categories":["image-generation","automation"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"invokeai__cap_0","uri":"capability://planning.reasoning.node.based.workflow.graph.execution.with.visual.editor","name":"node-based workflow graph execution with visual editor","description":"Executes directed acyclic graphs (DAGs) of custom invocation nodes through a FastAPI-backed invocation system that serializes node definitions as OpenAPI schemas. The React frontend provides a visual node editor where users connect outputs to inputs, and the backend's BaseInvocation system deserializes and executes the graph sequentially or in parallel where dependencies allow. This enables non-linear, reusable generation pipelines without code.","intents":["Create complex multi-step image generation workflows by chaining nodes visually","Reuse and share custom node graphs across projects","Build conditional generation pipelines with branching logic","Integrate custom Python nodes into the generation pipeline"],"best_for":["Professional artists and VFX teams building repeatable workflows","Developers extending InvokeAI with custom invocation nodes","Teams automating batch image generation with complex conditioning"],"limitations":["Graph execution is single-threaded per invocation; parallel node execution requires explicit dependency management","No built-in loop constructs or dynamic branching based on runtime image analysis","Node schema generation relies on Python type hints; complex types require custom serialization"],"requires":["Python 3.9+","FastAPI backend running","React frontend with Redux state management","Node definitions must inherit from BaseInvocation class"],"input_types":["node graph JSON","image tensors","text prompts","numeric parameters","model identifiers"],"output_types":["generated images","image tensors","metadata JSON","execution logs"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"invokeai__cap_1","uri":"capability://image.visual.unified.canvas.with.inpainting.outpainting.and.brush.controls","name":"unified canvas with inpainting, outpainting, and brush controls","description":"Konva-based HTML5 canvas rendering system that manages multiple control layers (base image, mask, brush strokes, selection regions) with real-time compositing. The canvas supports inpainting (selective region regeneration) and outpainting (extending image boundaries) through mask-aware conditioning passed to the diffusion pipeline. Brush tools apply masks directly to the canvas layer system, which are then converted to conditioning tensors for the model.","intents":["Paint regions of an image to regenerate with new prompts","Extend image boundaries outward while maintaining coherence","Precisely control which areas the model modifies using brush masks","Layer multiple editing operations on top of a base image"],"best_for":["Digital artists performing iterative image refinement","Content creators removing or replacing objects in images","Teams automating inpainting workflows for batch processing"],"limitations":["Brush rendering performance degrades with very large canvases (>4K) due to Konva layer compositing overhead","Mask precision is limited by canvas resolution; sub-pixel accuracy not supported","Outpainting quality depends on model training; some models produce visible seams at boundaries"],"requires":["Modern browser with HTML5 Canvas support","React frontend with Konva library","Diffusion model with inpainting checkpoint (e.g., inpaint-v2.0 variant)","GPU with sufficient VRAM for inpainting (typically 6GB+)"],"input_types":["base image (PNG, JPG)","brush strokes (canvas coordinates)","mask regions (binary or grayscale)","text prompts for regeneration"],"output_types":["inpainted image","mask tensor","composited layers"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"invokeai__cap_10","uri":"capability://tool.use.integration.redux.based.state.management.with.rtk.query.for.api.caching","name":"redux-based state management with rtk query for api caching","description":"React frontend uses Redux for global state management (generation parameters, selected models, UI state) and RTK Query for automatic API response caching and synchronization. RTK Query handles cache invalidation when mutations occur (e.g., generating an image invalidates the gallery), reducing unnecessary API calls. The Redux store is persisted to localStorage, allowing the UI to restore state across browser sessions.","intents":["Maintain consistent UI state across multiple components","Cache API responses to reduce server load and improve responsiveness","Persist user preferences and generation parameters across sessions","Synchronize state between multiple browser tabs"],"best_for":["React developers building complex UIs with many interdependent components","Teams optimizing frontend performance by reducing API calls","Applications requiring offline-first behavior with eventual sync"],"limitations":["Redux boilerplate increases code complexity; simple apps may not benefit","localStorage persistence is limited to ~5MB; large state objects may exceed quota","RTK Query cache invalidation is coarse-grained; no fine-grained cache control per image"],"requires":["React 18+","Redux Toolkit","RTK Query","Browser localStorage support"],"input_types":["Redux actions","API responses"],"output_types":["Redux state","cached API data","persisted localStorage"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"invokeai__cap_11","uri":"capability://text.generation.language.internationalization.i18n.with.dynamic.language.switching","name":"internationalization (i18n) with dynamic language switching","description":"React frontend uses i18next library to manage translations across 10+ languages, with JSON translation files organized by feature. Language selection is stored in Redux state and localStorage, allowing users to switch languages without page reload. The system supports pluralization, interpolation, and context-specific translations. Missing translations fall back to English with a warning in development mode.","intents":["Provide UI in multiple languages for global user base","Allow users to switch languages dynamically without page reload","Maintain consistent terminology across UI components","Support right-to-left languages (Arabic, Hebrew) with CSS adjustments"],"best_for":["Global applications serving non-English-speaking users","Teams maintaining translations across multiple languages","Open-source projects accepting community translations"],"limitations":["Translation files must be manually maintained; no automatic translation","RTL language support requires CSS overrides; not all components may be RTL-ready","Missing translations fall back to English; no graceful degradation for partially-translated languages"],"requires":["i18next library","React 18+","Translation JSON files for each language","Browser localStorage for language preference"],"input_types":["language code (e.g., 'en', 'es', 'ja')","translation key (string)"],"output_types":["translated UI text","language preference"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"invokeai__cap_12","uri":"capability://tool.use.integration.configuration.management.with.environment.based.settings","name":"configuration management with environment-based settings","description":"Backend configuration system that reads settings from environment variables, YAML config files, and command-line arguments with a precedence order (CLI > env vars > config file > defaults). Configuration covers model paths, API settings, GPU memory limits, and feature flags. The system validates configuration at startup and provides helpful error messages for invalid settings. Configuration is exposed via REST API endpoint for frontend discovery.","intents":["Configure InvokeAI for different deployment environments (dev, staging, production)","Set GPU memory limits and model cache sizes","Enable/disable features via feature flags","Customize API endpoints and CORS settings"],"best_for":["DevOps teams deploying InvokeAI to multiple environments","Developers running InvokeAI with custom settings","Teams managing feature rollouts via feature flags"],"limitations":["Configuration changes require server restart; no hot-reload","YAML config files are not validated against schema; typos can cause silent failures","No built-in secrets management; sensitive values (API keys) must be handled via environment variables"],"requires":["Python 3.9+","YAML library for config file parsing","Environment variable support (all OSes)"],"input_types":["environment variables","YAML config file","command-line arguments"],"output_types":["configuration object","configuration JSON via API"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"invokeai__cap_2","uri":"capability://memory.knowledge.multi.model.management.with.format.conversion.and.caching","name":"multi-model management with format conversion and caching","description":"Centralized model registry that discovers, downloads, caches, and converts between diffusion model formats (safetensors, ckpt, diffusers). The system maintains a model index with metadata (architecture, size, quantization level) and implements LRU caching with configurable memory limits to keep frequently-used models in VRAM. Format conversion happens on-disk before loading, and the model loader uses PyTorch's state_dict utilities to handle architecture mismatches.","intents":["Switch between different Stable Diffusion model versions (1.5, 2.0, SDXL, FLUX) without restarting","Download and cache models from Hugging Face Hub automatically","Convert legacy checkpoint files to modern safetensors format for security","Manage limited GPU VRAM by intelligently unloading unused models"],"best_for":["Artists experimenting with multiple model architectures in a single session","Teams running inference servers that serve many concurrent model requests","Developers building model management infrastructure on top of InvokeAI"],"limitations":["Format conversion adds 30-120 seconds per model depending on size; no streaming conversion","LRU cache eviction is based on access time only; no cost-aware eviction for models of different sizes","Model discovery requires internet access for Hugging Face Hub; offline-only deployments need pre-downloaded models"],"requires":["Python 3.9+","PyTorch with appropriate CUDA/CPU backend","Hugging Face transformers library","Disk space for model cache (typically 5-50GB depending on models)","Internet access for initial model downloads"],"input_types":["model identifier (e.g., 'runwayml/stable-diffusion-v1-5')","model checkpoint file path","quantization parameters"],"output_types":["loaded model in VRAM","model metadata JSON","converted safetensors file"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"invokeai__cap_3","uri":"capability://image.visual.controlnet.integration.with.multi.layer.conditioning","name":"controlnet integration with multi-layer conditioning","description":"Pluggable conditioning system that chains multiple ControlNet models (edge detection, pose, depth, semantic segmentation) to guide diffusion generation. Each ControlNet is loaded as a separate model, processes input images through its encoder to produce conditioning tensors, and these tensors are concatenated and passed to the UNet's cross-attention layers. The system supports weighted blending of multiple ControlNets and dynamic ControlNet switching within a workflow.","intents":["Guide image generation using edge maps, pose skeletons, or depth maps","Combine multiple ControlNets (e.g., pose + depth) for fine-grained control","Apply consistent style or composition across batch generations","Implement style transfer by conditioning on reference image features"],"best_for":["Character animators using pose ControlNets for consistent character generation","Architectural visualization teams using depth/edge ControlNets","Content creators building style-consistent image batches"],"limitations":["Each ControlNet adds 500MB-1GB to VRAM usage; stacking 3+ ControlNets may exceed consumer GPU memory","ControlNet quality varies significantly by model; some produce artifacts or ignore conditioning at high guidance scales","Conditioning tensor generation adds 100-300ms per image; not suitable for real-time interactive applications"],"requires":["ControlNet model files (downloaded from Hugging Face or local)","Input image for conditioning (same resolution as generation target)","GPU with 8GB+ VRAM for multi-ControlNet stacking","Diffusion model compatible with ControlNet (SD 1.5, SDXL, etc.)"],"input_types":["reference image (PNG, JPG)","ControlNet type identifier","conditioning strength (0.0-1.0)","ControlNet weights for blending"],"output_types":["conditioning tensor","processed reference image","generation guided by conditioning"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"invokeai__cap_4","uri":"capability://automation.workflow.real.time.websocket.event.streaming.for.generation.progress","name":"real-time websocket event streaming for generation progress","description":"FastAPI WebSocket server that emits structured events (generation-started, step-completed, generation-finished, error) during image generation, allowing the React frontend to update progress bars, preview intermediate steps, and handle cancellation. Events are serialized as JSON and include metadata (step number, current image tensor, timing info). The backend maintains a queue of pending invocations and broadcasts events to all connected clients.","intents":["Display real-time generation progress to users without polling","Show intermediate diffusion steps as they complete","Enable cancellation of long-running generations","Implement multi-user scenarios where multiple clients monitor the same generation"],"best_for":["Web-based UI applications requiring real-time feedback","Teams building monitoring dashboards for batch generation servers","Interactive applications where users expect sub-second progress updates"],"limitations":["WebSocket connections are stateful; scaling to 100+ concurrent users requires connection pooling and message broker","Event serialization of image tensors adds 50-200ms per step; large batches may cause lag","No built-in event persistence; if client disconnects, event history is lost"],"requires":["FastAPI with WebSocket support","React frontend with WebSocket client library","Network connectivity between frontend and backend","Browser support for WebSocket protocol"],"input_types":["invocation request","client connection identifier"],"output_types":["JSON event stream","intermediate image tensors","progress metadata"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"invokeai__cap_5","uri":"capability://memory.knowledge.gallery.system.with.image.boards.and.metadata.management","name":"gallery system with image boards and metadata management","description":"Persistent image storage system that organizes generated images into user-created boards with tagging, filtering, and full-text search over metadata. Images are stored on disk with associated JSON metadata files containing generation parameters, model info, and custom tags. The React frontend provides a gallery UI with board navigation, and the FastAPI backend exposes REST endpoints for CRUD operations on images and boards with pagination support.","intents":["Organize generated images into projects or collections (boards)","Search images by generation parameters, model, or custom tags","Export image batches with their generation metadata","Track which models and prompts produced successful results"],"best_for":["Professional artists maintaining large image libraries (10k+ images)","Teams collaborating on image generation projects with shared boards","Researchers analyzing generation quality across model variations"],"limitations":["Full-text search performance degrades with 100k+ images; requires database indexing for production use","Metadata is stored as JSON files; no relational queries (e.g., 'find images generated with model X AND prompt containing Y')","Board sharing requires manual file system access; no built-in multi-user permission system"],"requires":["Disk space for image storage (1-10MB per image depending on resolution)","FastAPI backend with file system access","React frontend with gallery UI components","SQLite or PostgreSQL for metadata indexing (optional, for large galleries)"],"input_types":["generated image file","generation metadata JSON","board name","custom tags"],"output_types":["image file","metadata JSON","board listing","search results"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"invokeai__cap_6","uri":"capability://image.visual.stable.diffusion.pipeline.with.vae.encoding.decoding.and.scheduler.selection","name":"stable diffusion pipeline with vae encoding/decoding and scheduler selection","description":"Wraps the Hugging Face diffusers library's StableDiffusionPipeline to handle the full generation process: text tokenization, CLIP embedding, VAE encoding of reference images, UNet denoising iterations with configurable schedulers (DDIM, Euler, DPM++), and VAE decoding back to image space. The system supports both txt2img (text-to-image) and img2img (image-to-image) modes, with scheduler selection affecting generation quality and speed. Conditioning tensors from ControlNets and embeddings are injected into the UNet's cross-attention layers.","intents":["Generate images from text prompts using Stable Diffusion","Refine existing images with new prompts (img2img)","Control generation quality vs speed by selecting schedulers","Use custom embeddings (textual inversion) in prompts"],"best_for":["General-purpose image generation workflows","Teams needing deterministic generation (seed control)","Developers integrating Stable Diffusion into applications"],"limitations":["Generation speed varies 5-30x depending on scheduler and step count; no adaptive scheduling based on image content","VAE decoding introduces artifacts at extreme aspect ratios; best results with 512x512 or 768x768","Negative prompts add 50% overhead because they require separate conditioning pass"],"requires":["Stable Diffusion model (1.5, 2.0, SDXL, or FLUX variant)","GPU with 6GB+ VRAM (8GB+ recommended for SDXL)","Hugging Face diffusers library","CLIP tokenizer and text encoder"],"input_types":["text prompt","negative prompt","seed (integer)","guidance scale (float)","number of steps (integer)","scheduler type (string)","reference image (for img2img)"],"output_types":["generated image tensor","PIL Image","generation metadata"],"categories":["image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"invokeai__cap_7","uri":"capability://automation.workflow.batch.image.generation.with.queue.management.and.resource.pooling","name":"batch image generation with queue management and resource pooling","description":"Invocation queue system that accepts multiple generation requests, serializes them with priority levels, and executes them sequentially or in parallel depending on available GPU memory. The backend maintains a queue of pending invocations, tracks execution state (queued, in-progress, completed, failed), and exposes queue status via REST API. Resource pooling ensures that only one model is loaded in VRAM at a time, with automatic unloading when switching models.","intents":["Submit 100+ image generation jobs and let the system process them automatically","Prioritize urgent generations over background batch jobs","Monitor queue status and cancel pending jobs","Optimize GPU utilization by batching similar generations"],"best_for":["Content creation teams running overnight batch jobs","Inference servers handling multiple concurrent user requests","Researchers generating large datasets for model evaluation"],"limitations":["Queue is in-memory only; if server crashes, pending jobs are lost (no persistence)","No cost-aware scheduling; all jobs are treated equally regardless of resource requirements","Parallel execution is limited by GPU VRAM; typically only 1-2 generations can run simultaneously on consumer GPUs"],"requires":["FastAPI backend with invocation queue","GPU with sufficient VRAM for largest model in queue","Disk space for output images","Optional: Redis or database for persistent queue (not built-in)"],"input_types":["invocation request JSON","priority level (integer)","batch size (integer)"],"output_types":["queue status JSON","generated images","execution logs"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"invokeai__cap_8","uri":"capability://image.visual.embedding.and.lora.integration.for.prompt.customization","name":"embedding and lora integration for prompt customization","description":"Support for textual inversion embeddings and LoRA (Low-Rank Adaptation) modules that modify model behavior without full fine-tuning. Embeddings are loaded as token replacements in the CLIP text encoder, allowing prompts like '<my-style>' to reference custom embeddings. LoRAs are loaded as weight modifications to the UNet and text encoder, applied during generation with configurable strength (0.0-1.0). The system discovers embeddings and LoRAs from disk and makes them available in the node system.","intents":["Use custom art styles by loading textual inversion embeddings","Apply LoRA modules to achieve specific visual effects or character consistency","Combine multiple LoRAs with different strengths for fine-grained control","Share embeddings and LoRAs across projects without model retraining"],"best_for":["Artists building personal style libraries with embeddings","Teams using LoRAs for consistent character or product generation","Developers building customization layers on top of base models"],"limitations":["Embedding quality varies widely; poorly-trained embeddings can degrade generation quality","LoRA strength is global; no per-layer control of LoRA application","Loading many LoRAs (5+) adds significant latency (100-500ms) due to weight merging"],"requires":["Embedding files (.pt or .safetensors format)","LoRA files (.safetensors format)","Disk space for embedding/LoRA library (typically 100MB-1GB)","CLIP tokenizer that supports custom token injection"],"input_types":["embedding identifier (string)","LoRA identifier (string)","LoRA strength (float 0.0-1.0)","prompt with embedding tokens"],"output_types":["modified CLIP embeddings","modified UNet weights","generated image with applied style"],"categories":["image-visual","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"invokeai__cap_9","uri":"capability://tool.use.integration.rest.api.with.openapi.schema.generation.and.automatic.validation","name":"rest api with openapi schema generation and automatic validation","description":"FastAPI-based REST API that automatically generates OpenAPI (Swagger) schemas from Python type hints on invocation nodes and service methods. Request/response validation is handled by Pydantic models, ensuring type safety and providing detailed error messages. The API exposes endpoints for image generation, model management, gallery operations, and queue status, with automatic documentation available at /docs. CORS is configured to allow cross-origin requests from the React frontend.","intents":["Build custom frontends or integrations by consuming the REST API","Automate image generation workflows via HTTP requests","Monitor server status and queue health programmatically","Generate API documentation automatically from code"],"best_for":["Developers integrating InvokeAI into larger applications","Teams building custom UIs on top of the API","Automation engineers building CI/CD pipelines for image generation"],"limitations":["OpenAPI schema generation adds startup overhead (1-5 seconds); large node registries can slow schema generation","No built-in API authentication; requires external reverse proxy (nginx, Caddy) for production security","Request/response serialization adds 10-50ms per request; not suitable for real-time applications"],"requires":["FastAPI 0.95+","Pydantic for model validation","Python 3.9+","HTTP client library (requests, httpx, etc.)"],"input_types":["JSON request body","URL path parameters","query string parameters"],"output_types":["JSON response","image file (binary)","OpenAPI schema"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"invokeai__headline","uri":"capability://image.visual.ai.driven.creative.engine.for.image.generation","name":"ai-driven creative engine for image generation","description":"InvokeAI is a professional-grade open-source creative engine designed for generating stunning visual media using AI technologies, featuring a node-based workflow editor and comprehensive model management.","intents":["best AI image generation tool","AI creative engine for artists","top open-source image generation software","image generation platform with node-based workflows","best tool for Stable Diffusion image creation"],"best_for":["artists","graphic designers","creative professionals"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["image-visual"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","FastAPI backend running","React frontend with Redux state management","Node definitions must inherit from BaseInvocation class","Modern browser with HTML5 Canvas support","React frontend with Konva library","Diffusion model with inpainting checkpoint (e.g., inpaint-v2.0 variant)","GPU with sufficient VRAM for inpainting (typically 6GB+)","React 18+","Redux Toolkit"],"failure_modes":["Graph execution is single-threaded per invocation; parallel node execution requires explicit dependency management","No built-in loop constructs or dynamic branching based on runtime image analysis","Node schema generation relies on Python type hints; complex types require custom serialization","Brush rendering performance degrades with very large canvases (>4K) due to Konva layer compositing overhead","Mask precision is limited by canvas resolution; sub-pixel accuracy not supported","Outpainting quality depends on model training; some models produce visible seams at boundaries","Redux boilerplate increases code complexity; simple apps may not benefit","localStorage persistence is limited to ~5MB; large state objects may exceed quota","RTK Query cache invalidation is coarse-grained; no fine-grained cache control per image","Translation files must be manually maintained; no automatic translation","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.49999999999999994,"match_graph":0.25,"freshness":0.52,"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":"2026-06-17T09:51:04.692Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=invokeai","compare_url":"https://unfragile.ai/compare?artifact=invokeai"}},"signature":"4aWmHJQ2fe+KHuxxmdt6uYWwq9bVenGfGebcI1xnQx8NDfPQq1IXiKmnoYbT7VtvrX2qQ/+msP7I2xYh1QN1Bg==","signedAt":"2026-06-20T14:39:43.735Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/invokeai","artifact":"https://unfragile.ai/invokeai","verify":"https://unfragile.ai/api/v1/verify?slug=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"}}