{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"stable-diffusion-3-5-large","slug":"stable-diffusion-3-5-large","name":"Stable Diffusion 3.5 Large","type":"model","url":"https://stability.ai/news/introducing-stable-diffusion-3-5","page_url":"https://unfragile.ai/stable-diffusion-3-5-large","categories":["image-generation","testing-quality"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"stable-diffusion-3-5-large__cap_0","uri":"capability://image.visual.text.to.image.generation.with.multimodal.diffusion.transformers","name":"text-to-image generation with multimodal diffusion transformers","description":"Generates images from natural language text prompts using a Multimodal Diffusion Transformer (MMDiT) architecture with 8.1 billion parameters. The model operates in latent space, progressively denoising from random noise conditioned on text embeddings across transformer blocks with integrated Query-Key Normalization. Supports output resolutions from 512×512 to 1 megapixel, with claimed superior text rendering and prompt adherence compared to Stable Diffusion 3.0.","intents":["Generate high-quality images from detailed text descriptions without manual design work","Create variations of visual concepts by adjusting prompt specificity and seed values","Produce images at specific resolutions up to 1 megapixel for print or web use","Iterate on image generation with different prompts to explore creative directions"],"best_for":["developers building image generation applications with open-source model control","teams requiring commercial image generation without API rate limits or usage fees","researchers fine-tuning diffusion models for domain-specific image synthesis"],"limitations":["Output quality and prompt adherence vary with seed values; same prompt with different seeds produces intentionally diverse results to preserve knowledge base diversity","Prompts lacking specificity may produce uncertain or inconsistent outputs","Maximum resolution capped at 1 megapixel; higher-resolution outputs require external upscaling","Text rendering quality depends on prompt clarity; complex multi-line text may render with errors","No built-in content filtering or safety mechanisms documented; relies on user responsibility"],"requires":["GPU with sufficient VRAM (exact requirements not documented; Medium variant targets consumer hardware)","Python 3.8+ with PyTorch or compatible inference framework","Model weights downloaded from Hugging Face (8.1GB for Large variant, 2.5GB for Medium)","Inference code from Stability AI GitHub repository"],"input_types":["text (natural language prompts, unstructured)","integer (seed for deterministic output variation)"],"output_types":["image (format unspecified; likely PNG or JPEG)"],"categories":["image-visual","generative-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"stable-diffusion-3-5-large__cap_1","uri":"capability://image.visual.fast.image.generation.with.distilled.diffusion.steps","name":"fast image generation with distilled diffusion steps","description":"Stable Diffusion 3.5 Large Turbo variant generates images in 4 diffusion steps instead of the standard multi-step process, achieving 'considerably faster' inference while maintaining the 8.1B parameter architecture. Uses knowledge distillation techniques to compress the denoising schedule without retraining from scratch, trading marginal quality for speed. Designed for real-time or interactive applications where latency is critical.","intents":["Generate preview images in interactive UI workflows with sub-second latency","Build real-time image generation features for web applications with consumer-grade hardware","Reduce inference costs in production by minimizing compute per image","Enable rapid iteration on prompts with immediate visual feedback"],"best_for":["web application developers building interactive image generation interfaces","teams deploying image generation on edge devices or resource-constrained servers","product teams prioritizing user experience latency over maximum quality"],"limitations":["Absolute inference latency not documented; '4 steps' is relative to unspecified baseline","Quality trade-offs vs. Large variant not quantified; aesthetic level may vary more","Distillation approach may reduce diversity in outputs for identical prompts","No benchmarks provided comparing speed/quality trade-off to other fast variants (e.g., LCM, consistency models)"],"requires":["GPU with sufficient VRAM (exact requirements not documented)","Python 3.8+ with PyTorch or compatible inference framework","Model weights for Large Turbo variant from Hugging Face (8.1GB)","Inference code from Stability AI GitHub repository"],"input_types":["text (natural language prompts)","integer (seed for output variation)"],"output_types":["image (format unspecified)"],"categories":["image-visual","optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"stable-diffusion-3-5-large__cap_10","uri":"capability://image.visual.inference.code.and.deployment.flexibility","name":"inference code and deployment flexibility","description":"Stability AI provides inference code on GitHub (repository URL not specified in documentation) enabling self-hosted deployment on various hardware configurations and frameworks. Code supports PyTorch and likely other inference engines (e.g., ONNX, TensorRT). No proprietary inference runtime required; standard Python/PyTorch stack enables deployment on cloud VMs, on-premises servers, or edge devices. Inference code is open-source, enabling community optimization and integration.","intents":["Deploy image generation on custom infrastructure without vendor lock-in","Integrate image generation into existing Python/PyTorch applications","Optimize inference for specific hardware (e.g., quantization, pruning, batching)","Contribute improvements or optimizations to the inference codebase"],"best_for":["developers building custom image generation applications","teams deploying image generation on specific hardware or cloud platforms","organizations requiring inference optimization for cost or latency","researchers extending or modifying inference behavior"],"limitations":["Inference code repository URL not documented; requires searching Stability AI GitHub","No official optimization guide; community resources vary in quality","Inference latency not benchmarked; optimization requires profiling and experimentation","No official support for inference engines beyond PyTorch (ONNX, TensorRT, etc.); community-driven","Inference code may require updates with model releases; maintenance burden on users"],"requires":["Python 3.8+ with PyTorch 2.0+ (or compatible version)","GPU with sufficient VRAM for inference (exact requirements not documented)","Inference code from Stability AI GitHub repository","Model weights from Hugging Face (8.1GB for Large, 2.5GB for Medium)","Familiarity with Python and PyTorch for integration and optimization"],"input_types":["text (natural language prompts)","integer (seed, resolution, other parameters)"],"output_types":["image (format unspecified; typically PNG or JPEG)"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"stable-diffusion-3-5-large__cap_11","uri":"capability://image.visual.superior.text.rendering.in.generated.images","name":"superior text rendering in generated images","description":"Achieves improved text rendering quality compared to predecessor models (SD 3 Medium) through the MMDiT architecture's joint text-image processing and enhanced text embedding integration. The model can generate readable, correctly-spelled text within images at various sizes and styles, addressing a major limitation of prior diffusion models that struggled with text generation.","intents":["Generate images with embedded text (signs, labels, book covers, posters) without manual text overlay","Create product mockups with accurate branding and text placement","Generate social media graphics with readable headlines and captions","Produce marketing materials with integrated typography"],"best_for":["Marketing and design teams creating graphics with text integration","E-commerce platforms generating product images with labels and descriptions","Content creators producing social media assets with captions","Graphic designers augmenting manual design with text-enabled generation"],"limitations":["Text rendering quality benchmarks unknown; no quantitative comparison vs. SDXL or competitors","Complex typography limitations unknown; unclear whether model handles overlapping text, rotated text, or non-Latin scripts","Text length constraints unknown; unclear whether model can render multi-line paragraphs or only short labels","Spelling accuracy unknown; potential for misspellings or garbled text in complex prompts","Font style control unknown; unclear whether model respects font style specifications in prompts"],"requires":["Text specification in prompt (format and length constraints unknown)","Sufficient resolution for text legibility (estimated 512×512 minimum)"],"input_types":["text prompt including desired text content"],"output_types":["image with rendered text"],"categories":["image-visual","text-rendering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"stable-diffusion-3-5-large__cap_12","uri":"capability://image.visual.improved.prompt.adherence.and.compositional.understanding","name":"improved prompt adherence and compositional understanding","description":"Demonstrates enhanced ability to follow detailed prompts and understand complex compositional requirements through the MMDiT architecture's improved text-image alignment and larger effective context window. The model better interprets spatial relationships, object interactions, and nuanced prompt specifications compared to prior diffusion models, reducing need for prompt engineering and negative prompts.","intents":["Generate complex scenes with multiple objects and specific spatial relationships from single prompt","Reduce prompt engineering effort by improving first-pass adherence to specifications","Create images matching detailed creative briefs without iterative refinement","Minimize use of negative prompts by improving positive prompt understanding"],"best_for":["Professional designers and creative directors with detailed specifications","Automated content generation pipelines where prompt engineering is bottleneck","Applications requiring consistent adherence to brand guidelines and specifications","Researchers studying prompt-to-image alignment and semantic understanding"],"limitations":["Prompt adherence quality benchmarks unknown; no quantitative comparison vs. SDXL or competitors","Compositional understanding limits unknown; unclear whether model handles complex multi-object scenes or only simple compositions","Prompt length and complexity constraints unknown; unclear whether model degrades with very long or ambiguous prompts","Negative prompt effectiveness unknown; unclear whether negative prompts are necessary or optional","Edge case handling unknown; unclear how model handles contradictory or ambiguous specifications"],"requires":["Detailed text prompt with specific compositional requirements","Understanding of effective prompt structure (not documented)"],"input_types":["text prompt with compositional specifications"],"output_types":["image adhering to prompt specifications"],"categories":["image-visual","semantic-understanding"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"stable-diffusion-3-5-large__cap_2","uri":"capability://image.visual.lightweight.image.generation.for.consumer.hardware","name":"lightweight image generation for consumer hardware","description":"Stable Diffusion 3.5 Medium variant reduces model size to 2.5 billion parameters while maintaining MMDiT architecture, enabling inference 'out of the box' on consumer hardware without GPU optimization. Uses improved MMDiT-X architecture design to maximize parameter efficiency. Supports output resolutions from 0.25 to 2 megapixels, doubling the maximum resolution of the Large variant while reducing memory footprint.","intents":["Run image generation locally on laptops or consumer GPUs without cloud dependencies","Deploy image generation on edge devices with limited VRAM (e.g., 4-8GB)","Reduce infrastructure costs for self-hosted image generation services","Enable offline image generation without internet connectivity"],"best_for":["individual developers and hobbyists without access to high-end GPUs","teams building privacy-sensitive applications requiring on-device processing","organizations deploying image generation in resource-constrained environments"],"limitations":["Aesthetic quality and prompt adherence lower than Large variant; documentation notes 'aesthetic level may vary'","Inference speed slower than Large Turbo variant due to smaller model capacity","Maximum resolution 2 megapixels vs. 1 megapixel for Large (absolute pixel count comparable but aspect ratio flexibility differs)","Reduced compositional understanding for complex multi-object scenes compared to larger variants","Exact VRAM requirements not documented; 'consumer hardware' is vague (likely 4-8GB minimum)"],"requires":["GPU with 4-8GB VRAM (estimated; exact requirements not documented)","Python 3.8+ with PyTorch or compatible inference framework","Model weights for Medium variant from Hugging Face (2.5GB)","Inference code from Stability AI GitHub repository"],"input_types":["text (natural language prompts)","integer (seed for output variation)"],"output_types":["image (format unspecified)"],"categories":["image-visual","optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"stable-diffusion-3-5-large__cap_3","uri":"capability://image.visual.lora.fine.tuning.for.custom.style.and.domain.adaptation","name":"lora fine-tuning for custom style and domain adaptation","description":"Supports Low-Rank Adaptation (LoRA) fine-tuning on all model variants (Large, Large Turbo, Medium) with stabilized training process via Query-Key Normalization in transformer blocks. LoRA adds learnable low-rank matrices to attention weights without modifying base model weights, enabling efficient adaptation to custom styles, objects, or domains. Designed as primary customization mechanism with documented support for community-contributed LoRA modules.","intents":["Fine-tune the model on custom image datasets to generate brand-specific or domain-specific visuals","Create reusable LoRA modules for specific artistic styles, character designs, or object categories","Adapt the model to niche domains (e.g., medical imaging, architectural visualization) with limited training data","Enable users to share and distribute fine-tuned models without redistributing full 8.1GB weights"],"best_for":["teams building custom image generation for specific brands or use cases","researchers exploring style transfer and domain adaptation in diffusion models","community contributors creating reusable LoRA modules for public distribution"],"limitations":["LoRA training process details not documented; no guidance on dataset size, learning rates, or convergence criteria","Exact memory overhead of LoRA training not specified; likely requires 16-24GB VRAM for Large variant","No built-in evaluation metrics or validation framework documented","Query-Key Normalization stabilizes training but no quantitative improvement metrics provided","Community LoRA modules may have varying quality and compatibility; no curation or testing framework mentioned"],"requires":["Base model weights (8.1GB for Large, 2.5GB for Medium)","Training dataset with 100+ images (estimated; exact minimum not documented)","GPU with sufficient VRAM for gradient computation (16-24GB estimated for Large)","LoRA training code and framework (likely diffusers library or similar)","Python 3.8+ with PyTorch"],"input_types":["image dataset (PNG, JPEG, or similar format)","text captions or labels for images (optional; format unspecified)"],"output_types":["LoRA weights file (format unspecified; likely safetensors or PyTorch .pt)"],"categories":["image-visual","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"stable-diffusion-3-5-large__cap_4","uri":"capability://image.visual.open.weight.model.distribution.with.permissive.licensing","name":"open-weight model distribution with permissive licensing","description":"Model weights released under Stability AI Community License as open-source artifacts, available for download from Hugging Face in standard formats (likely safetensors or PyTorch). License explicitly permits commercial and non-commercial use, fine-tuning, redistribution, and monetization of derived works across the entire pipeline (fine-tuned models, LoRA modules, applications, artwork). No API key or proprietary access required; full model control and deployment flexibility.","intents":["Deploy image generation without vendor lock-in or dependency on Stability AI infrastructure","Build commercial products using image generation without licensing fees or usage restrictions","Contribute improvements, optimizations, or domain-specific variants to the community","Maintain full control over model behavior, safety policies, and output filtering"],"best_for":["developers and teams building commercial products with image generation","organizations with data privacy requirements preventing cloud API usage","researchers and open-source contributors extending model capabilities","enterprises requiring long-term model stability without vendor dependency"],"limitations":["No official support or SLA; community-driven troubleshooting and documentation","Responsibility for implementing content filtering and safety mechanisms falls on user","No official fine-tuning guidance or best practices; community resources vary in quality","Model weights are large (8.1GB for Large, 2.5GB for Medium); significant storage and bandwidth requirements","Inference optimization (quantization, pruning, distillation) requires external tools; not provided by Stability AI"],"requires":["Hugging Face account (free) to download model weights","Sufficient storage space (8.1GB for Large, 2.5GB for Medium, plus inference code)","GPU with VRAM for inference (exact requirements not documented)","Python 3.8+ with PyTorch or compatible inference framework","Inference code from Stability AI GitHub repository"],"input_types":["model weights (safetensors or PyTorch format from Hugging Face)"],"output_types":["deployed image generation service (self-hosted or integrated into application)"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"stable-diffusion-3-5-large__cap_5","uri":"capability://image.visual.managed.image.generation.service.with.curated.model.routing","name":"managed image generation service with curated model routing","description":"Stability AI Brand Studio provides a SaaS platform offering web UI and workflow tools for image generation, inpainting, outpainting, and background removal. Implements 'Curated Model Routing' that selects from multiple providers (including Stable Diffusion variants) based on task requirements. Tiered pricing model: free trial (1000 credits), Core ($50/month, 5000 credits/month), and Enterprise (custom). Abstracts model selection and infrastructure management from users.","intents":["Generate images through a web UI without installing software or managing GPU infrastructure","Access multiple image generation models through a single interface with automatic model selection","Perform image editing tasks (inpainting, outpainting, background removal) without separate tools","Scale image generation workloads without managing compute resources or model deployment"],"best_for":["non-technical users and designers without machine learning expertise","teams prototyping image generation features before building custom infrastructure","organizations with variable workloads preferring pay-as-you-go pricing over fixed infrastructure costs","enterprises requiring managed service SLAs and support"],"limitations":["Curated Model Routing logic not documented; users cannot explicitly select model variant or control routing decisions","Credit system introduces per-image costs; exact credit consumption per task not specified","Free trial limited to 1000 credits; unclear how many images this generates","No API documented for programmatic access; appears to be web UI only","Vendor lock-in; no export of generated images with model metadata or reproducibility information","Pricing higher than self-hosted inference for high-volume use cases (Core tier: $50/month = ~$0.01 per credit, unclear credit-to-image ratio)"],"requires":["Stability AI account (free to create)","Web browser with modern JavaScript support","Internet connectivity","Payment method for Core or Enterprise tiers (credit card or similar)"],"input_types":["text (natural language prompts via web UI)","image (for inpainting, outpainting, background removal tasks)"],"output_types":["image (PNG or JPEG, downloadable from web UI)"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"stable-diffusion-3-5-large__cap_6","uri":"capability://image.visual.high.resolution.image.generation.up.to.1.megapixel","name":"high-resolution image generation up to 1 megapixel","description":"Stable Diffusion 3.5 Large supports output resolutions from 512×512 to 1 megapixel (1,000,000 pixels), enabling generation of images suitable for print, large displays, or detailed crops. Latent diffusion architecture operates in compressed latent space, enabling efficient generation of high-resolution outputs without proportional VRAM increase. Supports arbitrary aspect ratios within resolution constraints (e.g., 1024×1024, 768×1280, 512×1920).","intents":["Generate images for print materials (posters, banners, magazine covers) at publication-ready resolution","Create detailed images for large displays or immersive experiences without visible pixelation","Produce high-resolution crops or details from generated images for further editing","Support diverse aspect ratios for different use cases (portrait, landscape, square, ultra-wide)"],"best_for":["designers and creative professionals requiring print-quality outputs","teams building image generation for large-format displays or installations","applications requiring detailed image analysis or cropping after generation"],"limitations":["1 megapixel maximum; ultra-high-resolution outputs (4K, 8K) require external upscaling","VRAM requirements for 1MP generation not documented; likely 16-24GB for Large variant","Inference latency increases with resolution; exact timing not provided","Aspect ratio flexibility may reduce quality for extreme ratios (e.g., 512×1920) due to training data distribution","Medium variant supports up to 2 megapixels but with lower quality than Large variant"],"requires":["GPU with sufficient VRAM for high-resolution generation (16-24GB estimated)","Python 3.8+ with PyTorch or compatible inference framework","Model weights from Hugging Face (8.1GB for Large)","Inference code from Stability AI GitHub repository"],"input_types":["text (natural language prompts)","integer (output resolution in pixels, e.g., 1024×1024)","integer (seed for reproducibility)"],"output_types":["image (format unspecified; likely PNG or JPEG at specified resolution)"],"categories":["image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"stable-diffusion-3-5-large__cap_7","uri":"capability://image.visual.superior.text.rendering.in.generated.images","name":"superior text rendering in generated images","description":"Stable Diffusion 3.5 Large claims 'superior text rendering' compared to predecessors through improved MMDiT architecture and training. Text-to-image conditioning operates across all transformer blocks with Query-Key Normalization, enabling tighter coupling between text tokens and image generation. Supports rendering of multi-word phrases, proper spelling, and text layout within images, addressing a known weakness of earlier diffusion models.","intents":["Generate images containing readable text for posters, infographics, or branded content","Create images with specific text overlays without post-processing in design tools","Render multi-language text within generated images","Produce images with text-heavy compositions (e.g., book covers, product packaging mockups)"],"best_for":["designers creating text-heavy visual content without manual text overlay","teams generating branded content with specific messaging","applications requiring text-in-image generation as core feature"],"limitations":["Text rendering quality not quantified; no benchmarks comparing to DALL-E 3 or other models","Complex multi-line text may still render with errors; exact failure modes not documented","Text rendering quality depends on prompt clarity and specificity; vague prompts produce uncertain outputs","Non-Latin scripts (CJK, Arabic, etc.) support not documented; likely limited to Latin-based languages","Text positioning and layout control limited to natural language description; no explicit coordinate-based text placement"],"requires":["Clear, specific text prompts describing desired text content and placement","GPU with sufficient VRAM for inference (exact requirements not documented)","Python 3.8+ with PyTorch or compatible inference framework","Model weights from Hugging Face (8.1GB for Large, 2.5GB for Medium)"],"input_types":["text (natural language prompts including text content and placement description)"],"output_types":["image (format unspecified; contains rendered text)"],"categories":["image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"stable-diffusion-3-5-large__cap_8","uri":"capability://image.visual.improved.compositional.understanding.for.multi.object.scenes","name":"improved compositional understanding for multi-object scenes","description":"Stable Diffusion 3.5 Large claims 'exceptional prompt adherence' and 'improved compositional understanding' through MMDiT architecture that jointly processes text and image tokens. Transformer blocks with Query-Key Normalization enable better spatial reasoning about object relationships, counts, and layout. Supports complex prompts describing multiple objects, their spatial relationships, and attributes without degradation in quality.","intents":["Generate complex scenes with multiple objects and specific spatial relationships","Create images where object counts, sizes, and positions match prompt descriptions","Render scenes with accurate object interactions and relative positioning","Produce images with improved adherence to detailed, multi-clause prompts"],"best_for":["designers creating complex visual compositions without manual editing","applications requiring accurate scene generation from detailed descriptions","teams building image generation for narrative or storyboarding use cases"],"limitations":["Compositional understanding quality not quantified; no benchmarks or evaluation metrics provided","Complex prompts with many objects may still produce errors; exact failure modes not documented","Spatial reasoning limited to natural language description; no explicit coordinate or bounding box control","Prompt adherence varies with seed values; same prompt produces different compositions with different seeds","No evaluation framework for measuring compositional accuracy (e.g., object detection, spatial relationship verification)"],"requires":["Detailed, specific text prompts describing object composition and spatial relationships","GPU with sufficient VRAM for inference (exact requirements not documented)","Python 3.8+ with PyTorch or compatible inference framework","Model weights from Hugging Face (8.1GB for Large, 2.5GB for Medium)"],"input_types":["text (natural language prompts with detailed compositional descriptions)"],"output_types":["image (format unspecified; contains multiple objects with described relationships)"],"categories":["image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"stable-diffusion-3-5-large__cap_9","uri":"capability://image.visual.seed.based.deterministic.output.variation","name":"seed-based deterministic output variation","description":"Supports integer seed parameter to control randomness in image generation, enabling reproducible outputs and intentional variation. Same prompt with same seed produces identical image; different seeds produce diverse outputs from the same prompt. Model intentionally preserves variation across seeds to maintain knowledge base diversity and prevent mode collapse, documented as design trade-off.","intents":["Generate reproducible images for testing, documentation, or version control","Explore multiple variations of a prompt by iterating seed values","Implement deterministic image generation in applications requiring consistency","Debug prompt effectiveness by controlling randomness while varying text input"],"best_for":["developers building reproducible image generation pipelines","teams iterating on prompts and comparing outputs systematically","applications requiring deterministic behavior for testing or documentation"],"limitations":["Intentional design decision to preserve variation across seeds may reduce consistency for identical prompts","Seed values are 32-bit integers; no documentation on seed space distribution or collision properties","Variation magnitude across seeds not quantified; some seeds may produce similar outputs","No control over variation type (e.g., style variation vs. composition variation); all variation treated equally"],"requires":["Integer seed value (0 to 2^32-1, typical range)","GPU with sufficient VRAM for inference","Python 3.8+ with PyTorch or compatible inference framework","Model weights from Hugging Face"],"input_types":["text (natural language prompt)","integer (seed value for reproducibility)"],"output_types":["image (format unspecified; deterministic given prompt and seed)"],"categories":["image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"stable-diffusion-3-5-large__headline","uri":"capability://image.visual.high.quality.image.generation.model","name":"high-quality image generation model","description":"Stable Diffusion 3.5 Large is a cutting-edge image generation model that excels in creating high-resolution images from text prompts, offering superior text rendering and compositional understanding compared to earlier versions.","intents":["best image generation model","image generation for creative projects","high-quality image generation tool","AI model for generating images from text","top-rated image synthesis model"],"best_for":["artists","designers","content creators"],"limitations":["may produce varying outputs for vague prompts"],"requires":["text prompts"],"input_types":["text"],"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 sufficient VRAM (exact requirements not documented; Medium variant targets consumer hardware)","Python 3.8+ with PyTorch or compatible inference framework","Model weights downloaded from Hugging Face (8.1GB for Large variant, 2.5GB for Medium)","Inference code from Stability AI GitHub repository","GPU with sufficient VRAM (exact requirements not documented)","Model weights for Large Turbo variant from Hugging Face (8.1GB)","Python 3.8+ with PyTorch 2.0+ (or compatible version)","GPU with sufficient VRAM for inference (exact requirements not documented)","Model weights from Hugging Face (8.1GB for Large, 2.5GB for Medium)","Familiarity with Python and PyTorch for integration and optimization"],"failure_modes":["Output quality and prompt adherence vary with seed values; same prompt with different seeds produces intentionally diverse results to preserve knowledge base diversity","Prompts lacking specificity may produce uncertain or inconsistent outputs","Maximum resolution capped at 1 megapixel; higher-resolution outputs require external upscaling","Text rendering quality depends on prompt clarity; complex multi-line text may render with errors","No built-in content filtering or safety mechanisms documented; relies on user responsibility","Absolute inference latency not documented; '4 steps' is relative to unspecified baseline","Quality trade-offs vs. Large variant not quantified; aesthetic level may vary more","Distillation approach may reduce diversity in outputs for identical prompts","No benchmarks provided comparing speed/quality trade-off to other fast variants (e.g., LCM, consistency models)","Inference code repository URL not documented; requires searching Stability AI GitHub","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-3-5-large","compare_url":"https://unfragile.ai/compare?artifact=stable-diffusion-3-5-large"}},"signature":"YjUJZNYQmqBpAp753oEdB4dHhMufhnW6cCGXsdpJ7eobbXn80SWxHmVztoS0GmGlbPBZ0/fVWpV2BfFGz5WjCw==","signedAt":"2026-06-23T16:27:50.524Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/stable-diffusion-3-5-large","artifact":"https://unfragile.ai/stable-diffusion-3-5-large","verify":"https://unfragile.ai/api/v1/verify?slug=stable-diffusion-3-5-large","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"}}