{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"stable-diffusion-xl","slug":"stable-diffusion-xl","name":"Stable Diffusion XL","type":"model","url":"https://stability.ai/stable-diffusion","page_url":"https://unfragile.ai/stable-diffusion-xl","categories":["image-generation","model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"stable-diffusion-xl__cap_0","uri":"capability://image.visual.text.to.image.generation.with.dual.stage.refinement.pipeline","name":"text-to-image generation with dual-stage refinement pipeline","description":"Generates images from natural language prompts using a two-stage latent diffusion architecture: a 6.6B-parameter base model produces initial outputs at 1024x1024 resolution, then a specialized refiner model enhances fine details and texture quality in a second pass. The base model uses a dual-encoder UNet that jointly processes text embeddings and image latents, enabling tight prompt-to-image alignment without requiring massive model scaling.","intents":["Generate high-quality product mockups and marketing visuals from text descriptions","Create concept art and design variations for creative projects without manual iteration","Produce diverse visual outputs for content creation at scale with consistent style control","Prototype UI/UX designs and visual layouts from written specifications"],"best_for":["Content creators and designers needing fast iteration on visual concepts","Product teams prototyping visual designs before engineering investment","Solo developers building image-generation features into applications","Non-technical founders testing visual product ideas with minimal cost"],"limitations":["Native resolution capped at 1024x1024 for base SDXL; upscaling required for higher resolutions introduces quality degradation","Two-stage pipeline adds ~2-3 seconds latency vs single-pass models; Turbo variant reduces to ~4 diffusion steps but with quality trade-offs","Prompt length and complexity constraints unknown; overly detailed or contradictory prompts may degrade coherence","Struggles with precise text rendering, small object details, and anatomically complex poses due to latent space compression","No built-in semantic understanding of spatial relationships; complex scene composition requires careful prompt engineering"],"requires":["GPU with minimum 8GB VRAM for inference (consumer hardware for Medium variant)","Text prompt in English (other languages unsupported or untested)","API key for Stability AI API, or self-hosted deployment license for on-premise use","Diffusion sampling library (diffusers, ComfyUI, or equivalent) for local inference"],"input_types":["text (natural language prompt, 1-1000 characters typical)","optional: seed value for reproducibility","optional: guidance scale parameter (7.5-15 typical range)"],"output_types":["PNG/JPEG image at 1024x1024 pixels","optional: latent representation for downstream processing"],"categories":["image-visual","generative-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"stable-diffusion-xl__cap_1","uri":"capability://image.visual.image.to.image.transformation.with.style.and.content.control","name":"image-to-image transformation with style and content control","description":"Transforms existing images by encoding them into the latent space and applying diffusion conditioning with a text prompt, enabling style transfer, composition changes, and detail enhancement. The model preserves structural information from the input image while allowing the prompt to guide stylistic and semantic modifications through a configurable strength parameter that controls the balance between input fidelity and prompt influence.","intents":["Apply consistent artistic styles across multiple product photos for catalog uniformity","Recompose existing images with different backgrounds, lighting, or perspectives","Enhance low-quality or aged photographs with modern aesthetic improvements","Generate variations of existing designs with different color schemes or materials"],"best_for":["E-commerce teams needing rapid photo editing and style consistency","Creative agencies producing design variations at scale","Photographers and retouchers automating repetitive enhancement tasks","Game developers and 3D artists generating texture and concept variations"],"limitations":["Strength parameter (0-1) controls input preservation but lacks fine-grained spatial control; cannot selectively modify regions without inpainting","Structural changes are limited by the input image's composition; radical recomposition may fail or produce artifacts","Latency increases with image resolution; processing 2048x2048 images requires tiling or downsampling","Quality degrades when input image contains artifacts, compression, or unusual aspect ratios"],"requires":["Input image in PNG, JPEG, or WebP format","Image dimensions compatible with model (typically 512-1024px; larger requires tiling)","Text prompt describing desired output style or modifications","Strength parameter (0.0-1.0) to control preservation vs transformation"],"input_types":["image (PNG, JPEG, WebP)","text (style or modification prompt)","float (strength parameter, 0.0-1.0)"],"output_types":["PNG/JPEG image at same resolution as input","optional: latent representation for chaining operations"],"categories":["image-visual","generative-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"stable-diffusion-xl__cap_10","uri":"capability://image.visual.self.hosted.deployment.with.advanced.customization.and.fine.tuning","name":"self-hosted deployment with advanced customization and fine-tuning","description":"Enables on-premise deployment of SDXL with full control over model weights, inference parameters, and custom extensions. Supports local fine-tuning of LoRA adapters, ControlNets, and IP-Adapters on proprietary data; integrates with custom inference frameworks (ComfyUI, Automatic1111, diffusers) and orchestration platforms. Requires commercial license for production use.","intents":["Deploy image generation in air-gapped or restricted network environments","Fine-tune SDXL on proprietary datasets (product catalogs, character designs) without exposing data to cloud","Integrate image generation into existing ML pipelines and data processing workflows","Maintain full control over model versions, inference parameters, and custom extensions"],"best_for":["Enterprise teams with data privacy or compliance requirements","Organizations with existing GPU infrastructure and ML ops capabilities","Research teams experimenting with custom model architectures and training","Teams requiring deterministic inference or custom sampling algorithms"],"limitations":["Requires significant DevOps and ML infrastructure expertise; not suitable for teams without GPU management experience","Self-hosted deployment requires 50-100GB storage for model weights, datasets, and inference caches","Fine-tuning on proprietary data requires GPU resources (8-80GB VRAM depending on method); training time is weeks to months","Commercial license required for production use; licensing terms and pricing unknown from provided documentation","No automatic updates or security patches; requires manual model updates and dependency management"],"requires":["Commercial self-hosted license from Stability AI (terms and pricing unknown)","GPU with 8GB+ VRAM for inference, 24GB+ for fine-tuning","Python 3.9+, PyTorch, and CUDA toolkit","Inference framework (diffusers, ComfyUI, Automatic1111, or custom)","50-100GB storage for models and datasets","Network infrastructure for model serving (optional: Kubernetes, Docker)"],"input_types":["text (prompt)","optional: image (for image-to-image or inpainting)","optional: custom model weights or LoRA adapters","optional: fine-tuning dataset (images + captions)"],"output_types":["PNG/JPEG image at custom resolution","optional: fine-tuned model weights (.safetensors, .ckpt)","optional: inference metrics and performance logs"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"stable-diffusion-xl__cap_11","uri":"capability://image.visual.community.lora.and.adapter.ecosystem.with.thousands.of.pre.trained.modules","name":"community lora and adapter ecosystem with thousands of pre-trained modules","description":"Extensive ecosystem of community-trained LoRA adapters, ControlNets, and IP-Adapters available through platforms like Hugging Face, CivitAI, and GitHub. Enables rapid composition of pre-trained modules for specific styles, objects, and concepts without training. Quality and maintenance vary widely; no standardized evaluation or versioning system.","intents":["Discover and apply pre-trained styles and concepts without training custom adapters","Compose multiple community adapters to create novel style combinations","Leverage community-driven customization to extend SDXL capabilities","Reduce time-to-value by using existing adapters instead of training from scratch"],"best_for":["Developers and creators exploring diverse styles without training resources","Teams rapidly prototyping visual concepts using pre-trained modules","Community members sharing and discovering custom models","Hobbyists and enthusiasts experimenting with SDXL customization"],"limitations":["Community adapter quality is highly variable; no standardized evaluation or testing","Adapter maintenance is inconsistent; many abandoned or incompatible with newer SDXL versions","No versioning system; breaking changes in base model may render adapters unusable","Licensing and attribution requirements vary; unclear legal status of some adapters","Composition of multiple adapters is unreliable; weight conflicts and style interference common","No built-in discovery or recommendation system; finding relevant adapters requires manual search"],"requires":["Base SDXL model (6.6GB)","Adapter files (.safetensors or .ckpt format, 1-100MB each)","Inference library supporting adapter loading (diffusers, ComfyUI, Automatic1111)","Internet access to download adapters from community platforms (Hugging Face, CivitAI, etc.)"],"input_types":["adapter file paths and URLs","optional: adapter weight parameters (0.0-1.0 per adapter)"],"output_types":["loaded adapter modules ready for inference","optional: adapter metadata and compatibility information"],"categories":["image-visual","community-ecosystem"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"stable-diffusion-xl__cap_12","uri":"capability://image.visual.diverse.representation.and.global.imagery.synthesis","name":"diverse representation and global imagery synthesis","description":"Generates images representing diverse people, cultures, and scenes from around the world through training data curation and fine-tuning. The model is designed to produce images that reflect global diversity in demographics, environments, and cultural contexts without requiring explicit diversity prompts. This capability addresses historical biases in image generation models toward Western/English-speaking demographics.","intents":["Generate representative imagery for global audiences without bias toward specific demographics","Create inclusive marketing materials and content reflecting diverse populations","Produce training data for downstream models with improved demographic representation","Avoid reinforcing stereotypes or underrepresenting non-Western cultures in generated images"],"best_for":["Teams creating inclusive marketing or educational content","Organizations committed to diversity and representation in generated media","Researchers studying bias in generative models"],"limitations":["Diversity representation is statistical; individual generations may not reflect diversity","Bias mitigation is imperfect; stereotypes and underrepresentation may still occur","No explicit control over demographic representation in specific images","Diversity metrics and evaluation methodology undocumented","Training data composition and filtering criteria unknown"],"requires":["Text prompt (diversity emerges from training, not explicit prompting)","API key or web interface access"],"input_types":["text (prompt)"],"output_types":["image (with improved demographic diversity vs. earlier models)"],"categories":["image-visual","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"stable-diffusion-xl__cap_2","uri":"capability://image.visual.inpainting.and.outpainting.with.mask.guided.generation","name":"inpainting and outpainting with mask-guided generation","description":"Selectively regenerates masked regions of an image while preserving unmasked areas, enabling localized editing, object removal, and canvas expansion. The model encodes the input image and mask into the latent space, then applies diffusion only to masked regions while conditioning on both the text prompt and the preserved image context, maintaining seamless blending at mask boundaries through attention mechanisms.","intents":["Remove unwanted objects or people from photographs without manual cloning","Extend image composition by expanding canvas and generating new content in masked areas","Replace specific objects or regions with new content matching the surrounding context","Fill in missing or damaged areas of historical or degraded images"],"best_for":["Photo editors and retouchers automating object removal workflows","Content creators expanding images for different aspect ratios or layouts","E-commerce teams removing backgrounds or replacing product contexts","Archivists and historians restoring damaged historical photographs"],"limitations":["Mask quality directly impacts output; soft or poorly-defined masks produce visible seams or artifacts","Large masked regions (>50% of image) may fail to maintain coherence with surrounding context","Outpainting quality degrades at canvas edges; generated content may not align with perspective or lighting","Seamless blending requires careful prompt engineering; generic prompts produce obvious transitions","No spatial control over generated content placement within masked region"],"requires":["Input image in PNG, JPEG, or WebP format","Binary or soft mask (PNG with alpha channel or grayscale image) indicating regions to regenerate","Text prompt describing desired content for masked regions","Mask resolution matching input image dimensions"],"input_types":["image (PNG, JPEG, WebP)","mask (binary or soft mask as PNG/grayscale)","text (description of content to generate in masked regions)"],"output_types":["PNG/JPEG image with masked regions regenerated","optional: confidence map indicating blend quality"],"categories":["image-visual","generative-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"stable-diffusion-xl__cap_3","uri":"capability://image.visual.lora.adapter.composition.for.style.and.concept.customization","name":"lora adapter composition for style and concept customization","description":"Loads and composes Low-Rank Adaptation (LoRA) modules that modify the base model's weights to encode specific artistic styles, objects, or concepts without full model retraining. Multiple LoRAs can be stacked with individual weight parameters, enabling fine-grained control over style blending and concept intensity. The architecture injects learned low-rank matrices into the UNet and text encoder, requiring only 1-100MB per adapter vs 6.6GB for full model fine-tuning.","intents":["Apply consistent branded visual styles across generated images without training custom models","Blend multiple artistic styles (e.g., 'oil painting' + 'cyberpunk') with independent intensity control","Generate images featuring specific objects, characters, or concepts from community-trained adapters","Rapidly prototype custom visual styles by composing pre-trained LoRA modules"],"best_for":["Agencies and studios needing brand-consistent image generation without model training","Developers building customizable image generation features for end users","Creative professionals exploring style combinations without GPU-intensive fine-tuning","Teams managing multiple visual styles across product lines"],"limitations":["LoRA composition quality degrades with >3-4 simultaneous adapters; weight conflicts and style interference increase","Community LoRAs vary widely in quality and training methodology; no standardized evaluation or versioning","Adapter weights are not normalized; finding optimal composition weights requires manual experimentation","LoRAs trained on specific base model versions may not transfer to newer SDXL variants","No built-in mechanism for conflict detection or automatic weight balancing across adapters"],"requires":["Base SDXL model (6.6GB)","LoRA adapter files (.safetensors or .ckpt format, 1-100MB each)","Inference library supporting LoRA loading (diffusers, ComfyUI, Automatic1111)","Text prompt and optional LoRA weight parameters (typically 0.0-1.0 per adapter)"],"input_types":["text (prompt)","LoRA file paths and weight parameters","optional: seed for reproducibility"],"output_types":["PNG/JPEG image at 1024x1024 with LoRA-modified style","optional: metadata indicating loaded LoRAs and weights"],"categories":["image-visual","model-customization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"stable-diffusion-xl__cap_4","uri":"capability://image.visual.controlnet.spatial.conditioning.for.composition.and.structure.control","name":"controlnet spatial conditioning for composition and structure control","description":"Guides image generation using auxiliary conditioning inputs (edge maps, depth maps, pose skeletons, segmentation masks) that constrain the diffusion process to follow specified spatial structures. ControlNet modules inject conditioning information into the UNet at multiple scales, enabling precise control over composition, object placement, and structural layout without requiring prompt engineering for spatial relationships.","intents":["Generate images that match specific spatial layouts, poses, or compositions from reference images","Create consistent character poses and perspectives across multiple generated images","Enforce architectural or structural constraints in generated scenes (e.g., room layouts, building facades)","Combine multiple ControlNets (pose + depth + edge) for fine-grained structural control"],"best_for":["Game developers and 3D artists generating consistent character poses and environments","Architects and designers visualizing spaces with constrained layouts","Comic and storyboard artists maintaining consistent character positioning across panels","Product designers generating variations with fixed structural constraints"],"limitations":["ControlNet quality depends on input conditioning map quality; noisy or inaccurate maps produce artifacts","Conditioning strength parameter (0-1) controls adherence; high values may override prompt intent, low values reduce effectiveness","Different ControlNet types (pose, depth, edge, etc.) have varying robustness; pose ControlNets are more reliable than semantic segmentation","Combining multiple ControlNets increases failure risk and requires careful weight balancing","Inference latency increases ~30-50% per ControlNet due to additional conditioning branches"],"requires":["Base SDXL model (6.6GB)","ControlNet adapter file (.safetensors, 100-400MB depending on type)","Conditioning input (edge map, depth map, pose skeleton, or segmentation mask)","Text prompt describing desired content","Conditioning strength parameter (0.0-1.0)"],"input_types":["text (prompt)","image (conditioning map: edges, depth, pose, segmentation, etc.)","float (conditioning strength, 0.0-1.0)"],"output_types":["PNG/JPEG image at 1024x1024 following spatial constraints","optional: confidence map indicating conditioning adherence"],"categories":["image-visual","spatial-control"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"stable-diffusion-xl__cap_5","uri":"capability://image.visual.ip.adapter.identity.and.concept.preservation.across.generations","name":"ip-adapter identity and concept preservation across generations","description":"Encodes visual concepts or identities from reference images into a shared embedding space, then conditions generation on these embeddings to maintain consistent visual characteristics across multiple generated images. IP-Adapters work by projecting image embeddings (from CLIP or other vision encoders) into the text embedding space, allowing the diffusion model to preserve identity, style, or object appearance without fine-tuning.","intents":["Generate multiple variations of a character or object while maintaining consistent visual identity","Apply a specific person's likeness or style across different contexts and compositions","Maintain product appearance consistency across generated marketing materials","Create style-consistent image series from a single reference image"],"best_for":["Character designers and illustrators generating consistent character variations","Marketing teams maintaining brand visual identity across generated assets","Game developers creating consistent NPC appearances across scenes","Fashion and product designers visualizing items in different contexts"],"limitations":["Identity preservation quality depends on reference image quality and distinctiveness; generic or low-quality references produce weak conditioning","IP-Adapter strength parameter (0-1) controls identity adherence; high values may override prompt intent and reduce diversity","Combining IP-Adapter with LoRA or ControlNet requires careful weight balancing; conflicts can produce artifacts","Identity preservation degrades with extreme pose changes or occlusions not present in reference image","No mechanism to blend multiple identities or concepts; single reference image per generation"],"requires":["Base SDXL model (6.6GB)","IP-Adapter weights (.safetensors, 100-200MB)","Vision encoder (CLIP ViT-H or equivalent) for embedding reference images","Reference image containing identity or concept to preserve","Text prompt describing desired output context"],"input_types":["image (reference image for identity/concept extraction)","text (prompt describing desired output context)","float (IP-Adapter strength, 0.0-1.0)"],"output_types":["PNG/JPEG image at 1024x1024 with preserved identity/concept","optional: embedding vector for downstream analysis"],"categories":["image-visual","identity-preservation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"stable-diffusion-xl__cap_6","uri":"capability://image.visual.stable.diffusion.3.5.turbo.fast.inference.with.4.step.generation","name":"stable diffusion 3.5 turbo fast inference with 4-step generation","description":"Optimized variant of SDXL that generates high-quality images in just 4 diffusion steps instead of 20-50, achieving 5-10x speedup through architectural distillation and optimized sampling schedules. Trades marginal quality for dramatic latency reduction, enabling real-time or near-real-time image generation in interactive applications. Maintains prompt adherence comparable to full SDXL while running on consumer hardware.","intents":["Build interactive image generation features with <1 second latency for user-facing applications","Generate rapid design iterations during creative brainstorming sessions","Deploy image generation on edge devices or serverless functions with strict latency budgets","Create real-time visual feedback loops in design tools or games"],"best_for":["Web and mobile developers building interactive image generation features","Real-time creative tools requiring sub-second generation latency","Serverless/edge deployment scenarios with strict compute budgets","Teams prioritizing user experience latency over marginal quality gains"],"limitations":["4-step generation produces slightly lower detail and texture quality compared to 20-50 step full SDXL","Reduced sampling steps limit the model's ability to correct errors; prompt quality becomes more critical","Guidance scale effectiveness is reduced; typical range 7.5-15 may produce oversaturated or artifacts at extremes","LoRA and ControlNet composition may be less stable with reduced diffusion steps","Quality degradation becomes visible with complex prompts or unusual style combinations"],"requires":["Stable Diffusion 3.5 Turbo model weights (size unknown, likely 2-4GB)","GPU with minimum 6GB VRAM for inference","Inference library supporting optimized sampling schedules (diffusers, ComfyUI)","Text prompt in English"],"input_types":["text (prompt)","optional: seed for reproducibility","optional: guidance scale (7.5-15 typical)"],"output_types":["PNG/JPEG image at 1024x1024 resolution","optional: generation metadata (steps, guidance, seed)"],"categories":["image-visual","performance-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"stable-diffusion-xl__cap_7","uri":"capability://image.visual.stable.diffusion.3.5.medium.consumer.hardware.optimization","name":"stable diffusion 3.5 medium consumer hardware optimization","description":"Lightweight variant of SDXL optimized to run on consumer GPUs (6-8GB VRAM) and CPUs, enabling local deployment without cloud infrastructure. Maintains quality comparable to full SDXL through architectural efficiency and optimized quantization, while supporting full fine-tuning capabilities (LoRA, ControlNet, IP-Adapter) on consumer hardware.","intents":["Deploy image generation locally without cloud API costs or latency","Fine-tune custom models on consumer hardware for proprietary use cases","Build privacy-preserving image generation features that never send data to external servers","Enable offline image generation for applications in restricted network environments"],"best_for":["Individual developers and small teams with limited cloud budgets","Organizations with privacy or data residency requirements","Researchers and hobbyists experimenting with model customization","Offline-first applications and edge deployment scenarios"],"limitations":["Inference latency on consumer GPUs is 10-30 seconds per image; CPU inference is 2-5 minutes","Fine-tuning on consumer hardware requires careful memory management; batch sizes limited to 1-4","Model size and VRAM constraints limit simultaneous LoRA/ControlNet composition","Quality is slightly lower than full SDXL due to architectural optimizations and quantization","No built-in optimization for multi-GPU setups; single-GPU inference only"],"requires":["GPU with 6-8GB VRAM (RTX 3060, RTX 4060, or equivalent) OR CPU with 16GB+ RAM","Python 3.9+","PyTorch or TensorFlow with CUDA/CPU support","Diffusers library or equivalent inference framework","8-16GB system RAM for model loading and inference"],"input_types":["text (prompt)","optional: image (for image-to-image or inpainting)","optional: mask (for inpainting)","optional: LoRA/ControlNet parameters"],"output_types":["PNG/JPEG image at 1024x1024 resolution","optional: latent representation for chaining"],"categories":["image-visual","edge-deployment"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"stable-diffusion-xl__cap_8","uri":"capability://image.visual.stability.ai.rest.api.with.multi.model.routing.and.async.processing","name":"stability ai rest api with multi-model routing and async processing","description":"Cloud-hosted API providing access to Stable Diffusion variants (SDXL, 3.5 Large/Turbo/Medium) with automatic model selection, request queuing, and async job processing. Handles authentication via API keys, rate limiting, and usage tracking. Supports batch processing, webhook callbacks for long-running jobs, and integration with cloud storage for input/output management.","intents":["Integrate image generation into web/mobile applications without managing GPU infrastructure","Process large batches of image generation requests with automatic queuing and retry logic","Build production image generation services with SLA guarantees and monitoring","Scale image generation workloads elastically based on demand without capacity planning"],"best_for":["Startups and small teams building image generation features without DevOps resources","Web and mobile developers integrating image generation into user-facing applications","Enterprise teams requiring managed infrastructure and SLA guarantees","Batch processing workflows generating thousands of images on schedule"],"limitations":["API latency is 5-30 seconds per image depending on model and queue depth; not suitable for real-time interactive applications","Per-image pricing (typically $0.01-0.10 per image) accumulates quickly at scale; local deployment is more cost-effective for high-volume use","Rate limiting and quota enforcement may throttle burst requests; requires request queuing and retry logic","API responses include limited metadata; no access to intermediate latent representations for advanced workflows","Vendor lock-in; switching to alternative providers requires code changes and retraining of custom models"],"requires":["Stability AI API key (obtained from Stability AI dashboard)","HTTP client library (curl, requests, axios, etc.)","Authentication header with API key","Billing account with valid payment method","Network connectivity to Stability AI endpoints"],"input_types":["text (prompt, 1-1000 characters)","optional: image (for image-to-image, PNG/JPEG)","optional: mask (for inpainting, PNG with alpha)","optional: model selection parameter","optional: seed, guidance scale, steps"],"output_types":["PNG/JPEG image at requested resolution","JSON response with image URL, metadata, and usage statistics"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"stable-diffusion-xl__cap_9","uri":"capability://image.visual.brand.studio.commercial.platform.with.tiered.pricing.and.team.collaboration","name":"brand studio commercial platform with tiered pricing and team collaboration","description":"Web-based creative platform built on SDXL providing user-friendly image generation, editing, and management tools with team collaboration features, asset libraries, and brand consistency controls. Offers tiered pricing (Trial free, Core $50/month, Enterprise custom) with usage quotas, API access, and integration with design workflows. Abstracts technical complexity of prompt engineering and model configuration.","intents":["Non-technical marketers and designers generating branded marketing assets without prompt engineering","Marketing teams collaborating on asset creation with approval workflows and version control","Agencies managing multiple client brands with separate asset libraries and brand guidelines","E-commerce teams generating product photography and lifestyle images at scale"],"best_for":["Non-technical marketing and creative teams","Small to mid-size agencies managing multiple client projects","In-house marketing departments needing rapid asset generation","E-commerce and product teams with high-volume image needs"],"limitations":["Trial tier (free) has limited monthly credits; Core tier ($50/month) may be insufficient for high-volume use","Web UI abstracts technical control; advanced users cannot access low-level parameters like guidance scale or sampling schedules","No direct access to LoRA, ControlNet, or IP-Adapter composition; limited customization compared to API or local inference","Brand consistency controls are template-based; cannot enforce arbitrary style constraints","Team collaboration features may have latency or synchronization issues at scale"],"requires":["Stability AI account (free or paid tier)","Web browser with modern JavaScript support","Monthly subscription for Core tier ($50/month) or Enterprise tier (custom pricing)","Optional: API key for programmatic access to Brand Studio assets"],"input_types":["text (natural language description of desired image)","optional: reference image for style inspiration","optional: brand guidelines or style templates","optional: team collaboration metadata (approvers, tags)"],"output_types":["PNG/JPEG image at 1024x1024 resolution","optional: asset metadata (creation date, creator, approvals)","optional: API response for programmatic asset retrieval"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"stable-diffusion-xl__headline","uri":"capability://image.visual.open.source.image.generation.model","name":"open-source image generation model","description":"Stable Diffusion XL is a powerful open-source image generation model known for its excellent prompt adherence and ability to generate high-quality images at 1024x1024 resolution, making it a popular choice among developers and artists alike.","intents":["best open-source image generator","image generation model for creative projects","high-quality image generation tool","AI model for generating images from text prompts","top image generation models for developers"],"best_for":["artists","developers","content creators"],"limitations":[],"requires":[],"input_types":["text prompts"],"output_types":["images"],"categories":["image-visual"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["GPU with minimum 8GB VRAM for inference (consumer hardware for Medium variant)","Text prompt in English (other languages unsupported or untested)","API key for Stability AI API, or self-hosted deployment license for on-premise use","Diffusion sampling library (diffusers, ComfyUI, or equivalent) for local inference","Input image in PNG, JPEG, or WebP format","Image dimensions compatible with model (typically 512-1024px; larger requires tiling)","Text prompt describing desired output style or modifications","Strength parameter (0.0-1.0) to control preservation vs transformation","Commercial self-hosted license from Stability AI (terms and pricing unknown)","GPU with 8GB+ VRAM for inference, 24GB+ for fine-tuning"],"failure_modes":["Native resolution capped at 1024x1024 for base SDXL; upscaling required for higher resolutions introduces quality degradation","Two-stage pipeline adds ~2-3 seconds latency vs single-pass models; Turbo variant reduces to ~4 diffusion steps but with quality trade-offs","Prompt length and complexity constraints unknown; overly detailed or contradictory prompts may degrade coherence","Struggles with precise text rendering, small object details, and anatomically complex poses due to latent space compression","No built-in semantic understanding of spatial relationships; complex scene composition requires careful prompt engineering","Strength parameter (0-1) controls input preservation but lacks fine-grained spatial control; cannot selectively modify regions without inpainting","Structural changes are limited by the input image's composition; radical recomposition may fail or produce artifacts","Latency increases with image resolution; processing 2048x2048 images requires tiling or downsampling","Quality degrades when input image contains artifacts, compression, or unusual aspect ratios","Requires significant DevOps and ML infrastructure expertise; not suitable for teams without GPU management experience","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"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":"2026-05-24T12:16:28.695Z","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=stable-diffusion-xl","compare_url":"https://unfragile.ai/compare?artifact=stable-diffusion-xl"}},"signature":"uau1ZoyJPPJmV8tF/0+6sBVWw1N+ug4uSVknqHw9JNYqFDvYkk9NfnnxXvCSupD1wFyaq1mYNio/BJEmifLKDQ==","signedAt":"2026-06-23T10:27:33.824Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/stable-diffusion-xl","artifact":"https://unfragile.ai/stable-diffusion-xl","verify":"https://unfragile.ai/api/v1/verify?slug=stable-diffusion-xl","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"}}