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Guidance is applied during denoising by interpolating between conditional and unconditional score estimates.","intents":["balance between prompt fidelity and creative variation for specific use cases","reduce unwanted artifacts by increasing guidance when model generates off-prompt content","enable more diverse outputs for exploratory design by lowering guidance","fine-tune output quality for specific prompt styles (detailed descriptions vs vague concepts)"],"best_for":["designers needing fine-grained control over generation behavior","teams optimizing for specific prompt styles or domains","researchers studying prompt-image alignment tradeoffs","content creators balancing consistency with creative variation"],"limitations":["guidance_scale > 15 typically produces artifacts (oversaturation, distortion) rather than improved alignment","guidance_scale < 5 produces outputs with minimal prompt correlation","CFG adds ~30-40% computational overhead vs unconditional generation (requires two forward passes per step)","optimal guidance_scale varies by prompt complexity — no automatic tuning mechanism","high guidance can amplify model biases present in training data"],"requires":["diffusers>=0.21.0","understanding of CFG mechanics for effective tuning"],"input_types":["guidance_scale float (0.0-20.0 range, 7.0-15.0 recommended)","prompt text (str)","negative_prompt text (optional, amplifies guidance effect)"],"output_types":["PIL Image (with guidance applied during generation)","guidance_scale value used (for logging)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-john6666--diving-illustrious-real-asian-v50-sdxl__cap_4","uri":"capability://image.visual.negative.prompt.specification.for.unwanted.attribute.exclusion","name":"negative prompt specification for unwanted attribute exclusion","description":"Accepts optional negative_prompt parameter to explicitly exclude unwanted visual attributes from generation. 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SDXL architecture natively supports variable resolution through positional encoding flexibility, enabling aspect ratio control (portrait, landscape, square) without retraining. Memory usage scales with resolution — higher resolutions require proportionally more VRAM.","intents":["generate images at specific aspect ratios for different use cases (social media, print, web)","optimize quality-to-speed tradeoff by selecting appropriate resolution","create portrait vs landscape variations of same subject","match output dimensions to downstream application requirements"],"best_for":["content creators producing images for multiple platforms with different aspect ratios","teams optimizing inference speed vs quality for resource-constrained environments","developers building applications requiring specific output dimensions","designers prototyping layouts with generated imagery"],"limitations":["generation time scales quadratically with resolution (1024x1024 ~4x slower than 512x512)","VRAM usage scales quadratically — 1024x1024 requires ~2x VRAM of 768x768","extreme aspect ratios (e.g., 512x1024) may produce distorted or low-quality outputs","model trained primarily on square/near-square images — non-square ratios show reduced quality","no automatic aspect ratio optimization — user must manually select appropriate dimensions"],"requires":["height and width as multiples of 64","minimum 6GB VRAM for 1024x1024 on consumer GPUs","understanding of resolution-to-quality-to-speed tradeoffs"],"input_types":["height integer (multiple of 64, typically 512-1024)","width integer (multiple of 64, typically 512-1024)","prompt text (str)"],"output_types":["PIL Image (height x width dimensions)","resolution used (for logging)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-john6666--diving-illustrious-real-asian-v50-sdxl__cap_6","uri":"capability://image.visual.inference.step.count.tuning.for.quality.speed.tradeoff","name":"inference step count tuning for quality-speed tradeoff","description":"Exposes num_inference_steps parameter controlling denoising iterations (typically 20-50 steps). More steps produce higher quality but increase generation time linearly; fewer steps enable faster generation but risk quality degradation and prompt misalignment. Diffusers scheduler (DDIM, Euler, etc.) determines how noise is progressively removed across steps. Optimal step count varies by prompt complexity and desired quality level.","intents":["optimize generation speed for real-time or interactive applications by reducing steps","maximize output quality for high-stakes use cases by increasing steps","find optimal step count for specific prompt styles through experimentation","balance latency vs quality for resource-constrained deployments"],"best_for":["developers optimizing inference latency for user-facing applications","teams with variable compute budgets needing flexible quality settings","researchers studying convergence behavior of diffusion models","content creators balancing iteration speed with output quality"],"limitations":["generation time scales linearly with step count (50 steps ~2.5x slower than 20 steps)","diminishing returns above 40-50 steps — quality improvement plateaus","below 20 steps, quality degrades significantly and prompt alignment suffers","optimal step count varies by prompt — no automatic tuning mechanism","scheduler choice (DDIM vs Euler) affects optimal step count"],"requires":["num_inference_steps as integer (20-50 recommended range)","understanding of quality-latency tradeoffs for specific use case"],"input_types":["num_inference_steps integer (20-50 typical, 10-100 supported)","prompt text (str)","scheduler selection (DDIM, Euler, etc.)"],"output_types":["PIL Image (generated with specified step count)","step count used (for logging/optimization)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":43,"verified":false,"data_access_risk":"low","permissions":["Python 3.8+","PyTorch 1.13+ with CUDA 11.8+ (for GPU acceleration) or CPU fallback","Hugging Face Diffusers library 0.21.0+","safetensors library for model loading","minimum 6GB VRAM for GPU inference or 16GB RAM for CPU","internet connection for initial model download (7GB)","diffusers>=0.21.0","safetensors>=0.3.1","transformers>=4.30.0 (for CLIP text encoder)","torch>=1.13.0"],"failure_modes":["output quality degrades with complex multi-subject compositions or specific pose/action requirements","fine-tuning on Illustrious base model may introduce stylistic artifacts in edge cases (extreme angles, unusual lighting)","generation speed ~30-60 seconds per image on consumer GPUs (RTX 3080), longer on CPU inference","prompt engineering required for consistent results — vague prompts produce high variance outputs","no built-in face detection or quality filtering — requires post-processing to reject low-quality generations","model weights (~7GB) require significant storage and VRAM (minimum 6GB for inference)","safetensors format adds ~5-10% overhead on first load (one-time conversion from safetensors to runtime tensors)","Diffusers pipeline abstractions add ~50-100ms latency per generation step vs bare PyTorch","no native support for LoRA/adapter merging — requires manual weight manipulation","limited to Diffusers-supported schedulers (DDPM, DDIM, Euler, etc.) — no custom sampling algorithms","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.5552550923731263,"quality":0.39,"ecosystem":0.5000000000000001,"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":"2026-05-24T12:16:22.765Z","last_scraped_at":"2026-05-03T14:22:49.651Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":295355,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=john6666--diving-illustrious-real-asian-v50-sdxl","compare_url":"https://unfragile.ai/compare?artifact=john6666--diving-illustrious-real-asian-v50-sdxl"}},"signature":"GDKNz39rWZFQBO3pTsm04T/6vt5NtgKCjGDd1UM7Mt/PCXWL/KlKgyWUQ2/QV1ce0rNqpaxGl782WMbLycO5DA==","signedAt":"2026-06-21T03:22:29.457Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/john6666--diving-illustrious-real-asian-v50-sdxl","artifact":"https://unfragile.ai/john6666--diving-illustrious-real-asian-v50-sdxl","verify":"https://unfragile.ai/api/v1/verify?slug=john6666--diving-illustrious-real-asian-v50-sdxl","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"}}