{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-crynux-network--stable-diffusion-v1-5","slug":"crynux-network--stable-diffusion-v1-5","name":"stable-diffusion-v1-5","type":"model","url":"https://huggingface.co/crynux-network/stable-diffusion-v1-5","page_url":"https://unfragile.ai/crynux-network--stable-diffusion-v1-5","categories":["image-generation"],"tags":["diffusers","safetensors","arxiv:1910.09700","endpoints_compatible","diffusers:StableDiffusionPipeline","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-crynux-network--stable-diffusion-v1-5__cap_0","uri":"capability://image.visual.text.to.image.generation.via.latent.diffusion","name":"text-to-image generation via latent diffusion","description":"Generates photorealistic and artistic images from natural language text prompts using a latent diffusion model architecture. The pipeline encodes text prompts into CLIP embeddings, then iteratively denoises a random latent vector through 50+ diffusion steps guided by the text embedding, finally decoding the latent representation back to pixel space via a VAE decoder. This approach reduces computational cost compared to pixel-space diffusion by operating in a compressed 4x-4x-8x latent space.","intents":["Generate high-quality images from text descriptions for creative projects","Create variations of visual concepts without manual design work","Prototype visual assets for games, marketing, or product design","Batch-generate training data or synthetic imagery for ML pipelines"],"best_for":["Independent artists and designers prototyping visual concepts","ML engineers building image generation pipelines or fine-tuning workflows","Teams deploying open-source image generation without cloud dependencies","Researchers studying diffusion models and generative AI architectures"],"limitations":["Inference latency is 5-30 seconds per image on consumer GPUs (RTX 3080) due to iterative denoising steps","Memory footprint ~4-6GB VRAM required for full model in fp32; requires quantization or smaller batch sizes for <8GB devices","Generated images are 512x512 pixels by default; higher resolutions require upsampling or fine-tuning","Text understanding limited to CLIP's training data; struggles with complex spatial relationships, exact counts, or rare concepts","No built-in safety filtering; requires external content moderation for production use","Deterministic seeding required for reproducibility; floating-point precision variations across hardware can produce different outputs"],"requires":["Python 3.8+","PyTorch 1.9+ with CUDA 11.0+ or CPU (significantly slower)","4GB+ VRAM for inference (8GB+ recommended for batch processing)","HuggingFace transformers library 4.25+","diffusers library 0.10.0+","safetensors library for model loading"],"input_types":["text (natural language prompt, 1-77 tokens after CLIP tokenization)","optional: negative prompt (text to suppress in generation)","optional: seed (integer for reproducibility)","optional: guidance_scale (float 7.5-15.0 for prompt adherence strength)"],"output_types":["PIL Image (512x512 RGB by default)","numpy array (uint8, shape [1, 512, 512, 3] for batch=1)","optional: latent tensor (for chaining with other diffusion operations)"],"categories":["image-visual","generative-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-crynux-network--stable-diffusion-v1-5__cap_1","uri":"capability://image.visual.prompt.guided.image.refinement.via.classifier.free.guidance","name":"prompt-guided image refinement via classifier-free guidance","description":"Implements classifier-free guidance (CFG) during the diffusion process by computing conditional and unconditional noise predictions, then blending them with a guidance_scale weight to steer generation toward the text prompt. At each denoising step, the model predicts noise for both the text-conditioned and unconditioned (empty prompt) latents, then interpolates: noise_final = noise_uncond + guidance_scale * (noise_cond - noise_uncond). Higher guidance_scale (7.5-15.0) increases prompt adherence at the cost of reduced diversity and potential artifacts.","intents":["Control how strictly the model follows the input prompt vs. generating diverse variations","Increase visual quality and prompt alignment for production-grade image generation","Trade off between prompt fidelity and creative variation based on use case","Debug prompt understanding by observing guidance_scale sensitivity"],"best_for":["Developers tuning image generation quality for specific domains (product photography, character design)","Researchers studying the effect of guidance strength on diffusion model behavior","Production systems requiring consistent, prompt-aligned outputs"],"limitations":["Guidance_scale > 15.0 often produces oversaturated colors, unrealistic textures, or 'fried' artifacts","Requires 2x forward passes per denoising step (conditional + unconditional), increasing inference time by ~50%","Guidance strength is global; cannot selectively guide different regions of the image differently","Optimal guidance_scale varies by prompt and model; no automatic tuning mechanism"],"requires":["diffusers library with CFG support (0.10.0+)","guidance_scale parameter exposed in pipeline (default 7.5)"],"input_types":["guidance_scale (float, typically 7.5-15.0; 1.0 = no guidance)","prompt (text)","negative_prompt (optional; text to suppress)"],"output_types":["PIL Image (512x512 RGB)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-crynux-network--stable-diffusion-v1-5__cap_10","uri":"capability://image.visual.lora.based.fine.tuning.and.model.adaptation","name":"lora-based fine-tuning and model adaptation","description":"Enables parameter-efficient fine-tuning via Low-Rank Adaptation (LoRA), where only small rank-decomposed matrices are trained instead of full model weights. LoRA adds trainable weight matrices (A and B) to selected layers, with rank typically 4-64. During inference, LoRA weights are merged into the base model or applied as a separate forward pass. This approach reduces fine-tuning memory from ~24GB (full model) to ~2-4GB (LoRA only) and enables fast adaptation to new styles, objects, or concepts.","intents":["Fine-tune Stable Diffusion on custom datasets (e.g., personal photos, brand styles) with limited compute","Create style-specific or concept-specific models without full retraining","Adapt pre-trained models to new domains with 10-100x less data than full training","Enable multi-LoRA composition for combining multiple adaptations"],"best_for":["Individual artists and creators personalizing image generation","Small teams fine-tuning for specific use cases without large compute budgets","Researchers studying parameter-efficient fine-tuning and model adaptation"],"limitations":["LoRA rank is a hyperparameter; higher rank (64) approaches full fine-tuning quality but increases memory","LoRA fine-tuning requires curated training data; poor data quality limits adaptation effectiveness","LoRA weights are model-specific; cannot transfer between different base models","Inference with LoRA adds ~5-10% latency due to additional matrix multiplications","No built-in tools for LoRA composition or conflict resolution when combining multiple LoRAs"],"requires":["diffusers library with LoRA support (0.18.0+)","peft library for LoRA implementation","training dataset (100-1000 images typical)","2-4GB VRAM for LoRA fine-tuning"],"input_types":["training dataset (images + text captions)","LoRA rank (integer, 4-64)","learning rate (float, typically 1e-4 to 1e-5)"],"output_types":["LoRA weights (small .safetensors file, 10-100MB)","fine-tuned model (base model + LoRA weights)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-crynux-network--stable-diffusion-v1-5__cap_11","uri":"capability://image.visual.image.to.image.generation.with.strength.control","name":"image-to-image generation with strength control","description":"Generates new images conditioned on an input image by encoding the image into latents, adding noise according to a strength parameter (0.0-1.0), and then denoising with text guidance. Strength controls how much the output deviates from the input: strength=0.0 returns the input image unchanged, strength=1.0 ignores the input and generates from scratch. Internally, the pipeline skips the first (1 - strength) * num_inference_steps denoising steps, preserving input image structure while allowing variation.","intents":["Generate variations of existing images with text-guided modifications","Perform style transfer by conditioning on an image and providing a style prompt","Iteratively refine images through multiple generation passes","Enable interactive image editing workflows"],"best_for":["Content creators iterating on visual designs","Style transfer and artistic image manipulation","Interactive image editing applications"],"limitations":["Strength parameter is global; cannot vary strength per region","High strength (> 0.8) may produce artifacts or lose input image structure","Low strength (< 0.2) may ignore the text prompt and preserve input too closely","Input image must be 512x512 or resized, losing original aspect ratio","No built-in inpainting support; requires separate inpainting pipeline for region-specific editing"],"requires":["diffusers library with StableDiffusionImg2ImgPipeline","input image (PIL Image, 512x512 RGB)"],"input_types":["image (PIL Image, 512x512 RGB)","prompt (text)","strength (float, 0.0-1.0)"],"output_types":["PIL Image (512x512 RGB)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-crynux-network--stable-diffusion-v1-5__cap_12","uri":"capability://image.visual.inpainting.with.mask.based.region.editing","name":"inpainting with mask-based region editing","description":"Generates images within masked regions while preserving unmasked areas, enabling targeted image editing. The inpainting pipeline accepts an image, mask (binary or soft), and text prompt. Masked regions are encoded into latents, noise is added, and the diffusion process generates new content in masked areas while keeping unmasked areas fixed. The mask is applied at each denoising step to blend generated and original content. This enables precise control over which image regions are modified.","intents":["Edit specific regions of images without affecting the rest","Remove or replace objects in images using text descriptions","Fill in missing or corrupted image regions","Enable interactive image editing with precise control"],"best_for":["Image editing applications requiring region-specific control","Object removal and replacement workflows","Content creators refining images with targeted edits"],"limitations":["Mask must be binary or soft (0.0-1.0); no support for soft transitions","Inpainting quality depends on mask quality; hard edges can produce artifacts","Inpainting may not perfectly blend generated content with surrounding areas","Requires separate inpainting pipeline (StableDiffusionInpaintPipeline); not compatible with text-to-image pipeline","Mask must be same resolution as image (512x512); no automatic resizing"],"requires":["diffusers library with StableDiffusionInpaintPipeline","input image (PIL Image, 512x512 RGB)","mask (PIL Image, 512x512 grayscale, 0-255 or 0.0-1.0)"],"input_types":["image (PIL Image, 512x512 RGB)","mask (PIL Image, 512x512 grayscale)","prompt (text)"],"output_types":["PIL Image (512x512 RGB)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-crynux-network--stable-diffusion-v1-5__cap_2","uri":"capability://image.visual.batch.image.generation.with.seed.control","name":"batch image generation with seed control","description":"Processes multiple text prompts in parallel by batching latent tensors and text embeddings through the diffusion loop, with per-sample seed control for reproducibility. The pipeline accepts batch_size > 1, generates unique random latents for each sample (or uses provided seeds), and returns a batch of images in a single forward pass. Seed management uses PyTorch's random number generator state to ensure deterministic output when the same seed is provided.","intents":["Generate multiple images from different prompts in a single GPU pass for efficiency","Reproduce exact images by saving and reusing seeds for A/B testing or debugging","Create image datasets with controlled variation (same prompt, different seeds)","Optimize throughput for production image generation services"],"best_for":["Batch processing pipelines generating 10-1000s of images","ML engineers building synthetic data generation workflows","Production services requiring reproducible, deterministic outputs"],"limitations":["Batch size limited by available VRAM; typical max 4-8 on 8GB GPUs, 16+ on 24GB+ GPUs","Seed reproducibility only guaranteed within same PyTorch version, CUDA version, and hardware; cross-platform reproducibility not guaranteed","No built-in progress tracking or cancellation for long batches","Memory usage scales linearly with batch_size; no dynamic batching or streaming"],"requires":["PyTorch 1.9+ with deterministic mode support","diffusers StableDiffusionPipeline with batch support","sufficient VRAM for batch_size * (latent_memory + text_embedding_memory)"],"input_types":["prompts (list of strings, length = batch_size)","seeds (optional list of integers, length = batch_size; if None, random seeds generated)","batch_size (integer, 1-16+ depending on VRAM)"],"output_types":["list of PIL Images (length = batch_size, each 512x512 RGB)","optional: list of seeds used (for reproducibility logging)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-crynux-network--stable-diffusion-v1-5__cap_3","uri":"capability://image.visual.negative.prompt.suppression","name":"negative prompt suppression","description":"Accepts a negative_prompt parameter that is encoded into embeddings and used during classifier-free guidance to suppress unwanted visual concepts. The pipeline computes noise predictions conditioned on both the positive prompt and negative prompt, then uses guidance to push the generation away from the negative prompt direction. Internally, negative prompts are concatenated with positive prompts in the batch dimension, requiring 2x text encoding passes (or 1 pass with concatenation) to generate both embeddings.","intents":["Exclude unwanted visual styles, objects, or attributes from generated images","Improve image quality by suppressing common artifacts (blurry, low-res, deformed)","Fine-tune generation without retraining or prompt engineering alone","Enforce content policies by suppressing specific concepts at generation time"],"best_for":["Content creators refining image aesthetics without manual post-processing","Production systems enforcing content policies or brand guidelines","Researchers studying concept suppression in diffusion models"],"limitations":["Negative prompts are less effective than positive prompts; suppression is 'soft' and not guaranteed","Requires additional text encoding pass, adding ~100-200ms latency","No fine-grained spatial control; suppression applies globally to the entire image","Negative prompt effectiveness varies by concept; some concepts are harder to suppress than others","Conflicting positive and negative prompts can produce degraded outputs"],"requires":["diffusers library 0.10.0+","negative_prompt parameter in pipeline"],"input_types":["negative_prompt (string, 1-77 tokens after CLIP tokenization)","prompt (string, positive prompt)"],"output_types":["PIL Image (512x512 RGB)"],"categories":["image-visual","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-crynux-network--stable-diffusion-v1-5__cap_4","uri":"capability://text.generation.language.clip.based.text.embedding.and.semantic.understanding","name":"clip-based text embedding and semantic understanding","description":"Encodes text prompts into 768-dimensional CLIP embeddings using a pre-trained CLIP text encoder (trained on 400M image-text pairs). The encoder tokenizes input text (max 77 tokens), passes tokens through a transformer, and extracts the final hidden state as the embedding. These embeddings are then used to condition the diffusion process via cross-attention layers in the UNet. CLIP embeddings capture semantic meaning of text in a space aligned with image features, enabling the diffusion model to generate images matching the text description.","intents":["Convert natural language prompts into semantic embeddings for image generation","Understand and generate images for complex, multi-concept text descriptions","Enable semantic search or similarity matching between prompts and images","Debug prompt understanding by inspecting embedding space"],"best_for":["Developers building text-to-image systems with semantic understanding","Researchers studying CLIP embeddings and vision-language alignment","Systems requiring prompt-image similarity matching or retrieval"],"limitations":["CLIP tokenizer has 77-token limit; longer prompts are truncated","CLIP embeddings are 768-dimensional; not human-interpretable","CLIP training data has biases and gaps; some concepts are poorly represented","Text understanding limited to CLIP's training distribution; out-of-distribution prompts may produce unexpected results","No built-in support for structured prompts or semantic constraints; only free-form text"],"requires":["transformers library 4.25+","CLIP model weights (automatically downloaded from HuggingFace)","tokenizers library for CLIP tokenization"],"input_types":["text (natural language prompt, up to 77 tokens)"],"output_types":["torch.Tensor (shape [1, 77, 768] for batch=1; 768-dimensional CLIP embeddings)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-crynux-network--stable-diffusion-v1-5__cap_5","uri":"capability://image.visual.vae.based.latent.encoding.and.decoding","name":"vae-based latent encoding and decoding","description":"Compresses 512x512 RGB images into 64x64x4 latent tensors using a pre-trained Variational Autoencoder (VAE) encoder, enabling diffusion to operate in a compressed space. The VAE encoder downsamples the image through convolutional blocks with residual connections, producing a latent distribution (mean and log-variance). During generation, the VAE decoder upsamples the denoised latent back to 512x512 RGB pixel space. This compression reduces memory and computation by ~64x compared to pixel-space diffusion.","intents":["Reduce memory footprint and inference latency by operating in compressed latent space","Enable high-resolution image generation (512x512) on consumer GPUs","Encode existing images into latents for inpainting or image-to-image tasks","Understand the latent space structure for fine-tuning or manipulation"],"best_for":["Developers deploying image generation on resource-constrained hardware","Researchers studying VAE-based compression and latent space properties","Systems requiring both image generation and encoding (inpainting, editing)"],"limitations":["VAE introduces compression artifacts; some fine details are lost in the latent bottleneck","VAE decoder can produce slight color shifts or blurriness compared to original images","Latent space is not directly interpretable; manipulation requires understanding VAE structure","VAE scaling factor (0.18215) is a fixed hyperparameter; no adaptive scaling","VAE is frozen (not fine-tuned); cannot improve reconstruction quality without retraining"],"requires":["diffusers library with VAE support","pre-trained VAE model weights (automatically downloaded)"],"input_types":["PIL Image (512x512 RGB) for encoding","latent tensor (64x64x4) for decoding"],"output_types":["latent tensor (64x64x4) from encoder","PIL Image (512x512 RGB) from decoder"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-crynux-network--stable-diffusion-v1-5__cap_6","uri":"capability://image.visual.cross.attention.based.prompt.conditioning","name":"cross-attention-based prompt conditioning","description":"Conditions the diffusion process on text embeddings via cross-attention layers in the UNet. At each denoising step, the UNet computes self-attention over spatial features and cross-attention between spatial features and text embeddings. The cross-attention mechanism (Q from spatial features, K and V from text embeddings) enables the model to selectively attend to relevant parts of the prompt at each spatial location. This architecture allows fine-grained control over which prompt concepts influence which image regions.","intents":["Enable spatial control over prompt concepts by attending to different prompt tokens at different image regions","Improve semantic alignment between text and generated images through attention mechanisms","Debug prompt understanding by inspecting attention maps","Enable advanced techniques like prompt weighting or spatial conditioning"],"best_for":["Researchers studying attention mechanisms in diffusion models","Developers building advanced image generation techniques (prompt weighting, spatial control)","Systems requiring interpretability of prompt-image alignment"],"limitations":["Cross-attention maps are not directly exposed in the standard pipeline; requires custom code to extract","Attention visualization is post-hoc; cannot directly manipulate attention during generation","Cross-attention is computed at multiple scales (64x64, 32x32, 16x16); aggregation is non-trivial","No built-in support for spatial prompt weighting or region-specific guidance"],"requires":["diffusers library with UNet cross-attention support","custom hooks or modifications to extract attention maps"],"input_types":["text embeddings (768-dimensional CLIP embeddings)","spatial features (from UNet layers)"],"output_types":["attention maps (optional; shape [batch, num_heads, spatial_h, spatial_w, text_len])","denoised latents (conditioned on text)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-crynux-network--stable-diffusion-v1-5__cap_7","uri":"capability://image.visual.diffusion.based.iterative.denoising.with.timestep.scheduling","name":"diffusion-based iterative denoising with timestep scheduling","description":"Generates images through 50+ iterative denoising steps, where at each step the model predicts noise added to the latent and subtracts it. The process uses a timestep scheduler (e.g., DDPM, PNDM, Euler) that defines the noise schedule (how much noise to add/remove at each step) and the order of steps. The scheduler controls the trade-off between inference speed (fewer steps, faster but lower quality) and quality (more steps, slower but higher quality). Common schedulers include DDPM (50 steps), PNDM (20 steps), and Euler (20-50 steps).","intents":["Generate high-quality images through iterative refinement","Trade off inference speed vs. image quality by adjusting number of steps","Experiment with different noise schedules to optimize quality-speed tradeoff","Understand diffusion process dynamics and convergence behavior"],"best_for":["Developers optimizing image generation latency for production systems","Researchers studying diffusion process dynamics and scheduler design","Systems requiring flexible quality-speed tradeoffs"],"limitations":["Fewer steps (< 20) produce lower quality, more artifacts, and less prompt adherence","More steps (> 50) provide diminishing returns on quality while increasing latency linearly","Scheduler choice affects quality and speed; no universal optimal scheduler","Timestep scheduling is a global parameter; cannot vary step count per region","Scheduler must be compatible with model training; using incompatible schedulers produces poor results"],"requires":["diffusers library with scheduler support (PNDMScheduler, DDPMScheduler, EulerDiscreteScheduler, etc.)","num_inference_steps parameter (typically 20-50)"],"input_types":["num_inference_steps (integer, 20-50 typical)","scheduler (string or scheduler object, e.g., 'pndm', 'ddpm', 'euler')"],"output_types":["PIL Image (512x512 RGB)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-crynux-network--stable-diffusion-v1-5__cap_8","uri":"capability://safety.moderation.safetensors.based.model.loading.with.memory.safety","name":"safetensors-based model loading with memory safety","description":"Loads model weights from safetensors format (a memory-safe serialization format) instead of pickle, preventing arbitrary code execution during model loading. Safetensors uses a simple binary format with explicit type information, enabling safe deserialization without executing Python code. The diffusers library automatically detects and loads safetensors files, falling back to pickle if safetensors is unavailable. This approach reduces security risk when loading untrusted model weights from HuggingFace or other sources.","intents":["Load model weights safely without risk of arbitrary code execution","Verify model integrity through explicit type information in safetensors format","Reduce security surface when using community-contributed models","Enable fast model loading with memory-mapped access (optional)"],"best_for":["Production systems loading models from untrusted sources","Security-conscious developers and organizations","Systems requiring model provenance and integrity verification"],"limitations":["Safetensors format is newer; older models may only be available in pickle format","Safetensors loading is slightly slower than pickle for small models (< 100MB) due to format overhead","No built-in signature verification; safetensors format itself doesn't prevent tampering, only code execution","Requires safetensors library to be installed; adds dependency"],"requires":["safetensors library 0.3.0+","diffusers library 0.10.0+ with safetensors support"],"input_types":["safetensors file path or HuggingFace model ID"],"output_types":["loaded model weights (torch.nn.Module)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-crynux-network--stable-diffusion-v1-5__cap_9","uri":"capability://image.visual.inference.optimization.via.mixed.precision.and.memory.efficient.attention","name":"inference optimization via mixed-precision and memory-efficient attention","description":"Supports mixed-precision inference (fp16 or int8) to reduce memory footprint and increase speed, and enables memory-efficient attention implementations (e.g., xFormers, Flash Attention) to reduce attention memory complexity from O(n²) to O(n). Users can enable mixed-precision via `pipe.to('cuda', dtype=torch.float16)` and memory-efficient attention via `enable_attention_slicing()` or `enable_xformers_memory_efficient_attention()`. These optimizations are composable and can be combined for maximum efficiency.","intents":["Reduce memory footprint to enable inference on smaller GPUs (< 4GB VRAM)","Increase inference speed by 2-3x through mixed-precision and efficient attention","Enable batch processing on resource-constrained hardware","Optimize cost and latency for production image generation services"],"best_for":["Developers deploying on edge devices or small GPUs","Production systems optimizing for latency and cost","Researchers studying inference optimization techniques"],"limitations":["Mixed-precision (fp16) can produce slight quality degradation or numerical instability in some cases","Memory-efficient attention (xFormers) requires additional dependencies and may not be available on all hardware","Attention slicing reduces memory but increases latency by ~20-30%","Optimization effectiveness varies by hardware; no automatic tuning","Some optimizations are incompatible with certain features (e.g., attention slicing + xFormers)"],"requires":["PyTorch with mixed-precision support","xFormers library (optional, for memory-efficient attention)","CUDA compute capability 7.0+ for fp16 (Volta or newer)"],"input_types":["dtype (torch.float32, torch.float16, or torch.int8)","enable_attention_slicing (boolean)","enable_xformers_memory_efficient_attention (boolean)"],"output_types":["PIL Image (512x512 RGB, same quality as fp32 with minimal degradation)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":45,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","PyTorch 1.9+ with CUDA 11.0+ or CPU (significantly slower)","4GB+ VRAM for inference (8GB+ recommended for batch processing)","HuggingFace transformers library 4.25+","diffusers library 0.10.0+","safetensors library for model loading","diffusers library with CFG support (0.10.0+)","guidance_scale parameter exposed in pipeline (default 7.5)","diffusers library with LoRA support (0.18.0+)","peft library for LoRA implementation"],"failure_modes":["Inference latency is 5-30 seconds per image on consumer GPUs (RTX 3080) due to iterative denoising steps","Memory footprint ~4-6GB VRAM required for full model in fp32; requires quantization or smaller batch sizes for <8GB devices","Generated images are 512x512 pixels by default; higher resolutions require upsampling or fine-tuning","Text understanding limited to CLIP's training data; struggles with complex spatial relationships, exact counts, or rare concepts","No built-in safety filtering; requires external content moderation for production use","Deterministic seeding required for reproducibility; floating-point precision variations across hardware can produce different outputs","Guidance_scale > 15.0 often produces oversaturated colors, unrealistic textures, or 'fried' artifacts","Requires 2x forward passes per denoising step (conditional + unconditional), increasing inference time by ~50%","Guidance strength is global; cannot selectively guide different regions of the image differently","Optimal guidance_scale varies by prompt and model; no automatic tuning mechanism","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.62319374911686,"quality":0.35,"ecosystem":0.48000000000000004,"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":785165,"model_likes":1}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=crynux-network--stable-diffusion-v1-5","compare_url":"https://unfragile.ai/compare?artifact=crynux-network--stable-diffusion-v1-5"}},"signature":"zvQQcVmXsB8mLkw2TTdopShY/4itQcBnYcWlqkzAau1xKdJY4y+KQoAdgBNlKs5MJ8Kka5V+p6ZBBPCBwqPTBA==","signedAt":"2026-06-20T23:43:35.201Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/crynux-network--stable-diffusion-v1-5","artifact":"https://unfragile.ai/crynux-network--stable-diffusion-v1-5","verify":"https://unfragile.ai/api/v1/verify?slug=crynux-network--stable-diffusion-v1-5","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"}}