{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-acly--krita-ai-diffusion","slug":"acly--krita-ai-diffusion","name":"krita-ai-diffusion","type":"extension","url":"https://www.interstice.cloud","page_url":"https://unfragile.ai/acly--krita-ai-diffusion","categories":["image-generation"],"tags":["generative-ai","krita-plugin","stable-diffusion"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-acly--krita-ai-diffusion__cap_0","uri":"capability://image.visual.selection.constrained.inpainting.with.optional.text.prompts","name":"selection-constrained inpainting with optional text prompts","description":"Generates or modifies image content within Krita selections using diffusion models, with optional natural language prompts to guide generation. The plugin extracts the selection mask, encodes it as a conditioning signal, and passes it to the diffusion backend alongside the prompt embedding, enabling precise control over generation boundaries without manual masking workflows.","intents":["I want to fill a selected area of my painting with AI-generated content matching my text description","I need to regenerate part of an image without affecting the rest of the canvas","I want to inpaint without writing detailed prompts — just let the model infer from context"],"best_for":["digital artists using Krita for illustration and concept art","game asset creators needing rapid iteration on character/environment details","non-technical users who want AI assistance without prompt engineering"],"limitations":["Inpainting quality degrades at very small selection sizes (<64px) due to diffusion model receptive field constraints","Selection-based conditioning requires active Krita selection layer; no fallback to free-form painting without selection","Prompt-free mode relies on model's ability to infer context; results unpredictable for ambiguous or complex scenes"],"requires":["Krita 5.0+","Python 3.9+","Stable Diffusion 1.5, SDXL, Illustrious, or Flux model weights (local or cloud)","Active selection in Krita document"],"input_types":["image (Krita canvas layer)","selection mask (Krita selection object)","text (optional natural language prompt)","model parameters (steps, guidance scale, sampler)"],"output_types":["image (inpainted region as new Krita layer)","preview (real-time generation preview in canvas)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-acly--krita-ai-diffusion__cap_1","uri":"capability://image.visual.outpainting.with.automatic.canvas.extension","name":"outpainting with automatic canvas extension","description":"Extends image boundaries beyond the current canvas by generating new content in specified directions (up, down, left, right). The plugin detects canvas edges, creates temporary extended canvases with padding, applies diffusion conditioning to preserve edge coherence, and seamlessly merges generated content back into the original document. Supports multi-directional expansion in a single operation.","intents":["I want to extend my painting in one or more directions without manually resizing the canvas","I need to generate background or foreground elements that extend beyond the current composition","I want to expand a portrait or landscape composition while maintaining visual continuity at edges"],"best_for":["concept artists expanding compositions during ideation","illustrators needing to adjust framing after initial composition","background painters extending environments for animation or game assets"],"limitations":["Edge coherence depends on model training data; models trained on centered subjects may struggle with natural edge transitions","Outpainting beyond 512px per direction requires tiling/stitching logic which can introduce visible seams at tile boundaries","Memory usage scales with canvas size; very large outpaints (>2048px total) may require resolution downsampling"],"requires":["Krita 5.0+","Python 3.9+","Diffusion model with inpainting capability (SDXL or Flux recommended for coherence)","Sufficient VRAM (8GB+ for 1024px outpaints, 16GB+ for 2048px)"],"input_types":["image (current Krita canvas)","direction parameters (up, down, left, right pixels to extend)","text prompt (optional, to guide generation style)","edge preservation mode (hard boundary vs soft blend)"],"output_types":["image (extended canvas with generated content as new layer group)","metadata (outpaint operation history for undo/redo)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-acly--krita-ai-diffusion__cap_10","uri":"capability://tool.use.integration.server.management.with.local.and.cloud.backend.support","name":"server management with local and cloud backend support","description":"Abstracts backend infrastructure (local diffusion server, cloud API, or hybrid) behind a unified client interface, enabling users to switch between local and cloud execution without code changes. The plugin manages server lifecycle (installation, startup, shutdown), handles connection pooling and request routing, and provides fallback logic (e.g., fall back to cloud if local server unavailable). Supports both self-hosted backends (ComfyUI, Invoke) and cloud services (Replicate, RunwayML).","intents":["I want to run generation locally for privacy but fall back to cloud if my GPU is busy","I need to switch between local and cloud backends depending on generation complexity","I want to use a cloud service for quick start without managing local infrastructure"],"best_for":["users valuing privacy and control (local-first approach)","teams with heterogeneous infrastructure (some local, some cloud)","developers building multi-backend generation pipelines"],"limitations":["Local server setup requires manual installation and configuration; not all users comfortable with command-line tools","Cloud backend requires API keys and network connectivity; latency higher than local (100-500ms network overhead)","Fallback logic adds complexity; switching backends mid-generation may produce inconsistent results","Cost tracking for cloud backends requires manual integration with billing APIs"],"requires":["Krita 5.0+","Python 3.9+","For local backend: ComfyUI, Invoke, or compatible diffusion server","For cloud backend: API key (Replicate, RunwayML, or similar)","Network connectivity (for cloud backends)"],"input_types":["backend selection (local, cloud, or auto)","server configuration (URL, API key, model path)","fallback strategy (prefer local, prefer cloud, or hybrid)"],"output_types":["generation result (from selected backend)","backend status (online, offline, latency metrics)","cost estimate (for cloud backends)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-acly--krita-ai-diffusion__cap_11","uri":"capability://tool.use.integration.model.discovery.download.and.verification.with.automatic.caching","name":"model discovery, download, and verification with automatic caching","description":"Discovers available diffusion models from registries (Hugging Face, CivitAI, etc.), downloads model weights with progress tracking and resume capability, verifies integrity using checksums, and caches models locally for reuse. The plugin maintains a model registry with metadata (architecture, size, download URL, checksum), handles partial downloads and network interruptions, and provides UI for browsing and installing models without command-line tools.","intents":["I want to browse and install diffusion models without using command-line tools","I need to verify that downloaded models are authentic and uncorrupted","I want to manage multiple model versions and switch between them easily"],"best_for":["non-technical users unfamiliar with model management","teams managing shared model repositories","users with limited storage needing selective model caching"],"limitations":["Model downloads are large (2-7GB per model); slow on limited bandwidth connections","Checksum verification adds ~5-10% overhead to download time","No built-in deduplication; storing multiple model versions consumes full disk space per version","Model registry depends on external sources (Hugging Face, CivitAI); availability and metadata quality vary"],"requires":["Krita 5.0+","Python 3.9+","Network connectivity for model discovery and download","Sufficient disk space (50GB+ for multiple models)"],"input_types":["model registry source (Hugging Face, CivitAI, custom URL)","model selection (name, version, architecture)","download parameters (parallel connections, resume on interrupt)"],"output_types":["model metadata (architecture, size, download URL, checksum)","download progress (bytes downloaded, ETA, speed)","model verification result (checksum match, integrity status)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-acly--krita-ai-diffusion__cap_12","uri":"capability://automation.workflow.style.and.sampler.preset.management.with.parameter.persistence","name":"style and sampler preset management with parameter persistence","description":"Enables users to save and load generation parameter presets (prompt, model, sampler, guidance scale, steps, seed, ControlNet settings, etc.) as named styles or configurations. The plugin stores presets in a local registry with metadata, provides UI for browsing and applying presets, and supports preset sharing via export/import. Presets can be organized into categories and tagged for easy discovery.","intents":["I want to save my favorite generation settings and reuse them without re-entering parameters","I need to share generation presets with team members","I want to organize presets by style or use case for quick access"],"best_for":["artists with consistent style preferences","teams standardizing on specific generation parameters","users building reusable generation workflows"],"limitations":["Presets are static snapshots; if underlying models change, presets may become incompatible","No automatic preset versioning; updating a preset overwrites previous versions","Preset sharing requires manual export/import; no built-in collaboration or version control"],"requires":["Krita 5.0+","Python 3.9+","Local preset storage (JSON or similar)"],"input_types":["generation parameters (prompt, model, sampler, guidance, steps, seed, ControlNet settings)","preset name and category","preset metadata (description, tags, author)"],"output_types":["preset list (browsable, filterable by category/tag)","preset export (JSON or other portable format)","applied parameters (when preset is loaded)"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-acly--krita-ai-diffusion__cap_13","uri":"capability://planning.reasoning.custom.workflow.system.with.node.graph.ui.and.parameter.binding","name":"custom workflow system with node-graph ui and parameter binding","description":"Enables advanced users to define custom generation workflows using a node-graph interface, where nodes represent diffusion operations (sampling, conditioning, upscaling, etc.) and edges represent data flow. The plugin provides a visual workflow editor with parameter binding, enabling users to create complex multi-step pipelines (e.g., generate → upscale → inpaint) without code. Workflows are stored as JSON and can be shared or version-controlled.","intents":["I want to create a custom pipeline that generates, upscales, and inpaints in sequence","I need to define complex conditioning logic that isn't available in the standard UI","I want to share my generation workflow with other users"],"best_for":["advanced users comfortable with node-graph interfaces (Blender, Nuke, etc.)","developers building custom generation pipelines","teams standardizing on complex multi-step workflows"],"limitations":["Node-graph UI has steep learning curve for non-technical users","Custom workflows require understanding of diffusion internals (sampling, conditioning, etc.)","Workflow debugging is difficult; errors in node connections may produce cryptic failures","Performance optimization requires manual tuning of node parameters (batch size, tile size, etc.)"],"requires":["Krita 5.0+","Python 3.9+","Understanding of diffusion model architecture and node-graph concepts"],"input_types":["node definitions (operation type, input/output types, parameters)","node connections (edges defining data flow)","parameter bindings (linking node parameters to UI controls)"],"output_types":["workflow JSON (portable, version-controllable)","workflow execution result (image or intermediate outputs)","workflow visualization (node graph with data flow)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-acly--krita-ai-diffusion__cap_14","uri":"capability://text.generation.language.text.prompt.autocomplete.and.semantic.search.with.embedding.based.suggestions","name":"text prompt autocomplete and semantic search with embedding-based suggestions","description":"Provides intelligent autocomplete for generation prompts using embedding-based semantic search over a prompt database. As users type, the plugin suggests relevant prompt completions based on semantic similarity to the input, enabling faster prompt writing and discovery of effective prompt patterns. Suggestions are ranked by relevance and frequency, and users can customize the suggestion database.","intents":["I want autocomplete suggestions while writing prompts to speed up prompt entry","I need to discover effective prompt patterns for specific styles or subjects","I want to learn from successful prompts used by other users"],"best_for":["users new to prompt engineering seeking guidance","rapid ideation workflows where prompt speed matters","teams building shared prompt knowledge bases"],"limitations":["Autocomplete quality depends on prompt database quality; garbage in, garbage out","Embedding-based search adds ~50-100ms latency per keystroke, potentially causing UI lag","Suggestions may reinforce common patterns, limiting exploration of novel prompts","No built-in mechanism to filter inappropriate or low-quality suggestions"],"requires":["Krita 5.0+","Python 3.9+","Embedding model (CLIP or similar) for semantic search","Prompt database (local or cloud-based)"],"input_types":["partial prompt text (user input)","prompt database (corpus of known effective prompts)","embedding model (for semantic similarity)"],"output_types":["autocomplete suggestions (ranked by relevance)","suggestion metadata (frequency, success rate, source)"],"categories":["text-generation-language","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-acly--krita-ai-diffusion__cap_15","uri":"capability://text.generation.language.localization.and.multi.language.ui.support.with.community.translations","name":"localization and multi-language ui support with community translations","description":"Provides multi-language UI support with community-contributed translations, enabling users to use the plugin in their native language. The plugin uses a translation framework (e.g., gettext) with string extraction and community translation workflows, and supports dynamic language switching without restart. Includes fallback to English for untranslated strings.","intents":["I want to use the plugin in my native language, not English","I want to contribute translations for my language","I need the plugin to support multiple languages for international teams"],"best_for":["non-English-speaking users","international teams with multilingual members","open-source communities with translation volunteers"],"limitations":["Translation quality depends on volunteer contributors; some languages may have incomplete or inaccurate translations","UI layout may break with longer translations (e.g., German compound words); requires careful UI design","Dynamic language switching requires reloading UI components; may cause brief visual glitches","Maintaining translations requires ongoing effort as UI changes"],"requires":["Krita 5.0+","Python 3.9+","Translation framework (gettext or similar)"],"input_types":["language selection (from available translations)","translation contributions (community-provided translations)"],"output_types":["localized UI (in selected language)","translation status (coverage percentage per language)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-acly--krita-ai-diffusion__cap_2","uri":"capability://image.visual.region.based.prompting.with.layer.linked.conditioning","name":"region-based prompting with layer-linked conditioning","description":"Enables spatial control over generation by linking Krita layers to distinct text prompts, which are then converted to region-specific conditioning signals during diffusion. The plugin maintains a region registry that maps layer geometry to prompt embeddings, allowing users to define what should be generated in different areas of the canvas without manual mask creation. Regions can overlap, and the diffusion backend composites their conditioning signals.","intents":["I want to generate different content in different parts of my image using separate prompts","I need to ensure a character stays in the left half while the background generates on the right","I want to control which areas of my painting are affected by specific style or content prompts"],"best_for":["concept artists creating complex scenes with multiple distinct elements","illustrators needing fine-grained control over character vs background generation","game developers generating level layouts with region-specific constraints"],"limitations":["Region conditioning adds computational overhead (~15-20% per additional region) due to multi-prompt embedding and compositing","Overlapping regions with conflicting prompts may produce artifacts at boundaries; requires manual blending or soft region edges","Layer-to-region linking is manual; no automatic semantic segmentation to infer regions from content"],"requires":["Krita 5.0+","Python 3.9+","Krita layer structure (one layer per region or manual region definition)","Text prompts for each region"],"input_types":["Krita layers (geometry defines region boundaries)","text prompts (one per region)","region blending mode (hard boundary, soft feather, or overlap strategy)","region priority/weight (for conflict resolution)"],"output_types":["image (generation respecting region-prompt associations)","region metadata (stored in Krita document for persistence)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-acly--krita-ai-diffusion__cap_3","uri":"capability://image.visual.controlnet.based.structural.conditioning.scribble.line.art.canny.pose.depth.normals.segmentation","name":"controlnet-based structural conditioning (scribble, line art, canny, pose, depth, normals, segmentation)","description":"Applies ControlNet models to guide diffusion generation using structural cues extracted from the canvas or user input. The plugin supports multiple ControlNet modes: scribble (user-drawn lines), line art (edge detection), canny (edge detection), pose (skeleton extraction), depth (depth map), normals (surface normals), and segmentation (semantic masks). Each mode extracts or generates the appropriate conditioning signal and passes it to the diffusion backend with configurable control strength.","intents":["I want to sketch rough lines and have the model generate detailed content following my sketch","I need to generate a character in a specific pose without manually drawing the full figure","I want to maintain depth consistency in my generation by providing a depth map","I need to ensure generated content respects semantic regions (e.g., sky stays in sky area)"],"best_for":["concept artists using ControlNet for rapid ideation with structural guidance","character animators generating poses and expressions","environment artists maintaining spatial coherence across large scenes","developers building AI-assisted 3D asset pipelines"],"limitations":["ControlNet models are architecture-specific; SDXL ControlNets incompatible with SD1.5, requiring model-specific conditioning pipelines","Pose detection requires clear figure silhouettes; fails on occluded or overlapping characters","Depth and normals conditioning requires pre-computed maps or real-time estimation, adding 200-500ms latency per generation","Segmentation conditioning requires manual mask creation or external segmentation model; no built-in semantic understanding"],"requires":["Krita 5.0+","Python 3.9+","ControlNet model weights matching the base diffusion model (SD1.5 or SDXL ControlNets)","For pose mode: OpenPose or similar pose detection library","For depth mode: MiDaS or similar depth estimation model"],"input_types":["image (canvas or reference for conditioning extraction)","user input (scribbles, sketches, or manual masks)","control strength (0.0-1.0 weighting for conditioning influence)","ControlNet mode selection (scribble, line art, canny, pose, depth, normals, segmentation)"],"output_types":["image (generation guided by ControlNet conditioning)","conditioning visualization (preview of extracted control signal for debugging)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-acly--krita-ai-diffusion__cap_4","uri":"capability://image.visual.ip.adapter.reference.image.and.style.transfer.conditioning","name":"ip-adapter reference image and style transfer conditioning","description":"Enables style transfer and reference-based generation using IP-Adapter, which encodes reference images into a style/aesthetic embedding that guides diffusion without replacing the base prompt. The plugin extracts image features using a CLIP-based encoder, generates IP-Adapter conditioning tokens, and blends them with text prompt embeddings at configurable weights. Supports multiple reference images with independent weight control.","intents":["I want to generate images in the style of a reference image without explicitly describing the style","I need to maintain character consistency across multiple generations using a reference portrait","I want to apply a specific aesthetic (e.g., oil painting, watercolor) to generated content"],"best_for":["character artists maintaining visual consistency across concept iterations","illustrators applying consistent style across multiple scenes","game developers generating assets matching a specific art direction"],"limitations":["IP-Adapter quality depends on reference image relevance; dissimilar references produce weak or contradictory conditioning","Multiple reference images with conflicting styles may produce averaged/blended results rather than distinct style application","IP-Adapter adds ~100-150ms latency per generation due to CLIP encoding and adapter inference","Requires IP-Adapter model weights; not all base models have compatible adapters (SDXL adapters more mature than SD1.5)"],"requires":["Krita 5.0+","Python 3.9+","IP-Adapter model weights compatible with base diffusion model","CLIP image encoder (usually bundled with IP-Adapter)","Reference image(s) in Krita or external file"],"input_types":["reference image (style/aesthetic source)","text prompt (primary generation direction)","IP-Adapter weight (0.0-1.0, controls style influence)","multiple reference images (optional, each with independent weight)"],"output_types":["image (generation guided by reference style)","style embedding visualization (for debugging style transfer effectiveness)"],"categories":["image-visual","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-acly--krita-ai-diffusion__cap_5","uri":"capability://tool.use.integration.multi.model.support.with.automatic.architecture.detection.and.adapter.selection","name":"multi-model support with automatic architecture detection and adapter selection","description":"Abstracts diffusion model architecture differences (SD1.5, SDXL, Illustrious, Flux) behind a unified interface, automatically detecting model type and selecting appropriate conditioning adapters, tokenizers, and inference pipelines. The plugin maintains a model registry with metadata (architecture, supported ControlNets, IP-Adapter availability, optimal resolution), and routes generation requests to the correct backend implementation without user intervention.","intents":["I want to switch between different diffusion models without reconfiguring conditioning pipelines","I need to use SDXL for high-quality generations but fall back to SD1.5 for faster iteration","I want the plugin to automatically select the best model for my generation parameters"],"best_for":["users experimenting with multiple model architectures","teams with heterogeneous hardware (some machines with 8GB VRAM, others with 24GB+)","developers building model-agnostic AI pipelines"],"limitations":["Model switching requires reloading weights into VRAM (~5-30s depending on model size), blocking generation queue","Not all ControlNet modes available for all models; plugin must gracefully degrade unsupported modes","Prompt tokenization differs between models (SD1.5 uses 77 tokens, SDXL uses 77+77); plugin must handle token limit differences","Model-specific optimizations (e.g., SDXL's refiner) require separate inference passes, adding latency"],"requires":["Krita 5.0+","Python 3.9+","Model weights for at least one supported architecture (SD1.5, SDXL, Illustrious, or Flux)","Sufficient VRAM for largest model in use (8GB minimum for SD1.5, 16GB+ for SDXL/Flux)"],"input_types":["model selection (manual or automatic based on generation parameters)","model metadata (architecture, capabilities, resolution constraints)"],"output_types":["image (generation using selected model)","model compatibility report (which ControlNets/adapters available for selected model)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-acly--krita-ai-diffusion__cap_6","uri":"capability://image.visual.live.painting.with.real.time.canvas.interpretation.and.incremental.generation","name":"live painting with real-time canvas interpretation and incremental generation","description":"Interprets user brush strokes in real-time as generation guidance, updating the canvas incrementally as the user paints. The plugin monitors brush input, extracts stroke geometry and color information, encodes strokes as conditioning signals (similar to scribble ControlNet), and triggers generation updates at configurable intervals (e.g., every 500ms or every 10 strokes). Generated content is composited onto the canvas as a preview layer, allowing users to see results while painting.","intents":["I want to paint rough strokes and see AI-generated details appear in real-time","I need to iteratively refine generation by adding more strokes without waiting for full generation cycles","I want to use painting as a direct interface to generation, without separate prompt/parameter dialogs"],"best_for":["digital artists preferring tactile, real-time feedback over parameter tuning","rapid ideation workflows where speed matters more than precision","users with high-end hardware (GPU with 16GB+ VRAM) enabling real-time inference"],"limitations":["Real-time generation requires low-latency inference; typical generation takes 5-30s, limiting update frequency to every 5-10 seconds rather than true real-time","Incremental updates can produce flickering or inconsistent results if generation parameters change mid-stroke","Preview layer compositing adds ~50-100ms per frame to Krita's render loop, potentially causing UI lag on lower-end hardware","Stroke interpretation is lossy; complex multi-color strokes may not encode cleanly into conditioning signals"],"requires":["Krita 5.0+","Python 3.9+","GPU with 16GB+ VRAM for sub-10s generation latency","Brush input monitoring integration with Krita's event loop"],"input_types":["brush strokes (position, pressure, color, timing)","generation parameters (model, prompt, ControlNet mode)","update frequency (interval or stroke count threshold)"],"output_types":["preview layer (real-time generation composited on canvas)","stroke history (for undo/redo and stroke-to-generation mapping)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-acly--krita-ai-diffusion__cap_7","uri":"capability://image.visual.image.upscaling.to.4k.8k.resolutions.with.tile.based.processing","name":"image upscaling to 4k/8k+ resolutions with tile-based processing","description":"Scales generated or existing images to 4K (2160p), 8K (4320p), or higher resolutions using diffusion-based upscaling with tile-based processing to manage memory constraints. The plugin divides large images into overlapping tiles, upscales each tile independently using a diffusion upscaler model, and blends tiles at boundaries to eliminate seams. Supports both 2x and 4x upscaling factors with configurable tile overlap and blending strategies.","intents":["I want to upscale my 1024x1024 generation to 4K for print or high-resolution display","I need to increase resolution without losing detail or introducing artifacts","I want to upscale large images that exceed my GPU memory capacity"],"best_for":["artists preparing work for print or high-resolution displays","game developers generating high-resolution textures from lower-res sources","users with limited VRAM who need to upscale beyond single-pass capability"],"limitations":["Tile-based upscaling introduces visible seams at tile boundaries if overlap is insufficient; requires 64-128px overlap and blending, reducing effective upscaling efficiency","Upscaling adds 2-5x latency compared to generation at target resolution; 4K upscaling typically takes 30-60s","Diffusion-based upscaling may hallucinate details not present in original; results unpredictable for low-detail source images","Memory usage scales with tile size; 4K upscaling with 512px tiles requires ~8GB VRAM even with tiling"],"requires":["Krita 5.0+","Python 3.9+","Upscaler model weights (separate from base diffusion model)","8GB+ VRAM for 4K upscaling, 16GB+ for 8K"],"input_types":["image (source for upscaling)","target resolution (2x, 4x, or custom scale factor)","upscaling model (architecture-specific upscaler)","tile size and overlap parameters"],"output_types":["image (upscaled to target resolution as new Krita layer)","upscaling metadata (tile layout, blend regions, processing time)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-acly--krita-ai-diffusion__cap_8","uri":"capability://automation.workflow.job.queue.with.history.preview.and.batch.generation","name":"job queue with history, preview, and batch generation","description":"Maintains an asynchronous job queue for generation requests, enabling batch processing, job history tracking, and preview management without blocking the Krita UI. The plugin queues generation jobs with parameters, executes them sequentially or in parallel (if hardware supports), stores results with metadata (parameters, generation time, seed), and provides a history interface for reviewing and re-running past generations. Supports batch generation of multiple variations with parameter sweeps.","intents":["I want to queue multiple generation requests and let them process in the background","I need to review past generations and their parameters to understand what worked","I want to generate multiple variations of the same prompt with different seeds or parameters"],"best_for":["artists iterating on multiple concepts simultaneously","teams running parameter sweeps to find optimal settings","users with high-end hardware enabling parallel job execution"],"limitations":["Sequential job execution blocks UI between jobs; parallel execution requires multiple GPUs or careful VRAM management","Job history stored in memory; large histories (>1000 jobs) may consume significant RAM","No built-in job persistence; history lost on plugin restart unless explicitly saved","Batch generation with parameter sweeps can generate hundreds of images; requires manual curation or external filtering"],"requires":["Krita 5.0+","Python 3.9+","Sufficient VRAM for at least one concurrent generation (8GB minimum)"],"input_types":["generation request (image, prompt, parameters)","batch parameters (seed range, parameter sweep definitions)","queue priority (normal, high, low)"],"output_types":["job status (queued, processing, completed, failed)","generation result (image + metadata)","history export (JSON or CSV with generation parameters and results)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-acly--krita-ai-diffusion__cap_9","uri":"capability://automation.workflow.automatic.resolution.scaling.and.tile.layout.for.large.images","name":"automatic resolution scaling and tile layout for large images","description":"Automatically scales generation resolution based on available VRAM and target image size, using tile-based layout to process images larger than the model's native resolution. The plugin estimates VRAM requirements, selects optimal tile size and overlap, and orchestrates multi-tile generation with boundary blending. Supports both upscaling (generating at lower resolution then upscaling) and native tiling (generating tiles at full resolution).","intents":["I want to generate a 2048x2048 image but my GPU only supports 1024x1024 generations","I need the plugin to automatically choose the best resolution strategy for my hardware","I want to generate panoramic or very large images without manual tiling"],"best_for":["users with limited VRAM (8GB) needing to generate large images","artists creating panoramic compositions or large environment concepts","developers building resolution-agnostic generation pipelines"],"limitations":["Tile-based generation introduces seam artifacts at boundaries; requires careful overlap and blending to minimize visible discontinuities","Automatic resolution selection may choose suboptimal strategies; users with specific quality/speed tradeoffs may need manual override","Very large images (>4096px) with small tile sizes (<512px) require many tiles, multiplying generation time by tile count","Boundary blending adds ~10-20% latency overhead per generation"],"requires":["Krita 5.0+","Python 3.9+","VRAM detection and estimation logic (GPU memory query API)"],"input_types":["target image size (width, height)","available VRAM (auto-detected or manual override)","quality preference (prioritize quality vs speed)","tile overlap percentage (default 10-20%)"],"output_types":["resolution strategy (native, upscaled, or tiled)","tile layout (tile positions, overlap regions, blend masks)","image (generated at target resolution)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":43,"verified":false,"data_access_risk":"high","permissions":["Krita 5.0+","Python 3.9+","Stable Diffusion 1.5, SDXL, Illustrious, or Flux model weights (local or cloud)","Active selection in Krita document","Diffusion model with inpainting capability (SDXL or Flux recommended for coherence)","Sufficient VRAM (8GB+ for 1024px outpaints, 16GB+ for 2048px)","For local backend: ComfyUI, Invoke, or compatible diffusion server","For cloud backend: API key (Replicate, RunwayML, or similar)","Network connectivity (for cloud backends)","Network connectivity for model discovery and download"],"failure_modes":["Inpainting quality degrades at very small selection sizes (<64px) due to diffusion model receptive field constraints","Selection-based conditioning requires active Krita selection layer; no fallback to free-form painting without selection","Prompt-free mode relies on model's ability to infer context; results unpredictable for ambiguous or complex scenes","Edge coherence depends on model training data; models trained on centered subjects may struggle with natural edge transitions","Outpainting beyond 512px per direction requires tiling/stitching logic which can introduce visible seams at tile boundaries","Memory usage scales with canvas size; very large outpaints (>2048px total) may require resolution downsampling","Local server setup requires manual installation and configuration; not all users comfortable with command-line tools","Cloud backend requires API keys and network connectivity; latency higher than local (100-500ms network overhead)","Fallback logic adds complexity; switching backends mid-generation may produce inconsistent results","Cost tracking for cloud backends requires manual integration with billing APIs","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.3489501883570055,"quality":0.5,"ecosystem":0.48999999999999994,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.549Z","last_scraped_at":"2026-05-03T13:58:42.318Z","last_commit":"2026-05-02T19:39:09Z"},"community":{"stars":10039,"forks":577,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=acly--krita-ai-diffusion","compare_url":"https://unfragile.ai/compare?artifact=acly--krita-ai-diffusion"}},"signature":"CjMHXlKkDdzbhQNEDV1thS6e70+HVsUiRKwvFE1PlVa7hO90S0rxzrOv+eE5hDLpNObol9xlfq9dZy+KOI+LBg==","signedAt":"2026-06-22T23:11:29.375Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/acly--krita-ai-diffusion","artifact":"https://unfragile.ai/acly--krita-ai-diffusion","verify":"https://unfragile.ai/api/v1/verify?slug=acly--krita-ai-diffusion","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"}}