{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-compvis--stable-diffusion-v1-4","slug":"compvis--stable-diffusion-v1-4","name":"stable-diffusion-v1-4","type":"model","url":"https://huggingface.co/CompVis/stable-diffusion-v1-4","page_url":"https://unfragile.ai/compvis--stable-diffusion-v1-4","categories":["image-generation"],"tags":["diffusers","safetensors","stable-diffusion","stable-diffusion-diffusers","text-to-image","arxiv:2207.12598","arxiv:2112.10752","arxiv:2103.00020","arxiv:2205.11487","arxiv:1910.09700","license:creativeml-openrail-m","endpoints_compatible","diffusers:StableDiffusionPipeline","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-compvis--stable-diffusion-v1-4__cap_0","uri":"capability://image.visual.latent.space.text.to.image.generation.with.diffusion.denoising","name":"latent-space text-to-image generation with diffusion denoising","description":"Generates images from text prompts by encoding text into a CLIP embedding space, then iteratively denoising a random latent vector through 50 diffusion steps in a compressed 4x-downsampled latent space rather than pixel space. Uses a UNet architecture conditioned on text embeddings to predict and subtract noise at each step, reconstructing coherent images through the reverse diffusion process. The latent-space approach reduces computational cost by ~4x compared to pixel-space diffusion while maintaining visual quality through a learned VAE decoder.","intents":["Generate photorealistic or artistic images from natural language descriptions","Create variations of images by adjusting prompt text and random seeds","Build image generation features into applications without training custom models","Prototype visual content at scale for design, marketing, or creative workflows"],"best_for":["ML engineers and researchers prototyping text-to-image pipelines","Application developers integrating open-source image generation into products","Teams requiring on-premises or self-hosted image generation without API dependencies","Researchers studying diffusion models and latent-space representations"],"limitations":["Inference requires 4-8GB VRAM for single image generation; batch processing scales linearly with batch size","Quality degrades with prompts longer than ~77 tokens due to CLIP tokenizer limits","Deterministic output only when seed is fixed; stochastic sampling introduces variance across runs","No native inpainting or outpainting; requires separate model variants or post-processing","Training data biases reflected in outputs; may struggle with non-English prompts or underrepresented concepts","Inference latency ~5-30 seconds per image on consumer GPUs depending on hardware and step count"],"requires":["Python 3.8+","PyTorch 1.13+ with CUDA 11.6+ or CPU fallback (significantly slower)","4GB+ VRAM for inference (8GB+ recommended for batch processing)","HuggingFace transformers library 4.21+","Diffusers library 0.10.0+ for StableDiffusionPipeline","Internet connection for initial model download (~4GB total weights)"],"input_types":["text (natural language prompt, 1-77 tokens after CLIP encoding)","integer (random seed for reproducibility)","float (guidance_scale parameter, typically 7.5-15.0 for prompt adherence)","integer (num_inference_steps, typically 20-50 for quality-speed tradeoff)"],"output_types":["PIL Image (512x512 RGB by default)","numpy array (float32, shape [1, 3, 512, 512] for batch processing)","torch tensor (optional, for downstream processing)"],"categories":["image-visual","generative-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-compvis--stable-diffusion-v1-4__cap_1","uri":"capability://text.generation.language.clip.based.semantic.text.embedding.and.prompt.encoding","name":"clip-based semantic text embedding and prompt encoding","description":"Encodes text prompts into 768-dimensional CLIP embeddings using a transformer-based text encoder trained on 400M image-text pairs. Tokenizes input text to max 77 tokens, pads or truncates longer prompts, and produces embeddings that align with image features in a shared semantic space. These embeddings are then broadcast and injected into the UNet denoising network via cross-attention mechanisms at multiple resolution scales, enabling the diffusion process to condition image generation on semantic meaning rather than raw text.","intents":["Convert natural language descriptions into fixed-size semantic vectors for conditioning image generation","Ensure prompt semantics are preserved across the full 50-step diffusion process","Enable fine-grained control over image content through prompt engineering and weighting","Support multi-lingual prompts (with degraded quality for non-English text)"],"best_for":["Developers building prompt-based image generation interfaces","Researchers studying text-image alignment and semantic embeddings","Teams implementing prompt optimization or A/B testing workflows"],"limitations":["CLIP tokenizer truncates prompts at 77 tokens; longer descriptions are silently dropped","Embedding space trained primarily on English; non-English prompts produce lower-quality results","No native support for negative prompts or prompt weighting; requires external libraries (e.g., compel)","Semantic ambiguity in prompts (e.g., 'bank') cannot be disambiguated without additional context","Embedding space is frozen; cannot be fine-tuned without retraining the entire pipeline"],"requires":["HuggingFace transformers library 4.21+","CLIP model weights (openai/clip-vit-large-patch14, ~600MB)","PyTorch 1.13+","~2GB VRAM for text encoder alone"],"input_types":["text (raw string, any length; truncated to 77 tokens)","string (optional negative prompt for classifier-free guidance)"],"output_types":["torch tensor (shape [1, 77, 768] for single prompt)","torch tensor (shape [batch_size, 77, 768] for batched prompts)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-compvis--stable-diffusion-v1-4__cap_10","uri":"capability://image.visual.variable.output.resolution.via.latent.interpolation","name":"variable output resolution via latent interpolation","description":"Supports non-standard output resolutions (e.g., 768x768, 384x384) by interpolating the latent representation before decoding. The VAE decoder expects 64x64 latents; for other resolutions, latents are resized using bilinear interpolation. For example, 768x768 output requires 96x96 latents (768/8), which are interpolated from the standard 64x64. This approach enables flexible output sizes without retraining, though quality degrades for resolutions far from 512x512.","intents":["Generate images at custom resolutions without retraining or fine-tuning","Support variable aspect ratios (e.g., 512x768, 384x512) for different use cases","Enable flexible output sizing for different applications and devices","Adapt to user-specified resolution requirements"],"best_for":["Developers building flexible image generation APIs","Teams supporting variable output resolutions for different use cases","Researchers studying the impact of resolution on generation quality"],"limitations":["Quality degrades significantly for resolutions far from 512x512 (e.g., 1024x1024)","Latent interpolation introduces artifacts and blurriness at non-standard resolutions","Memory usage scales quadratically with resolution; 768x768 requires ~2.25x more VRAM than 512x512","Inference latency increases with resolution; 768x768 is ~2.25x slower than 512x512"],"requires":["Diffusers library 0.10.0+","PyTorch 1.13+","Sufficient VRAM for target resolution (8GB+ for 768x768)"],"input_types":["tuple of integers (height, width, e.g., (768, 768))","integer (height)","integer (width)"],"output_types":["PIL Image (custom resolution, e.g., 768x768 RGB)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-compvis--stable-diffusion-v1-4__cap_11","uri":"capability://image.visual.negative.prompt.guidance.for.artifact.reduction","name":"negative prompt guidance for artifact reduction","description":"Supports negative prompts (e.g., 'blurry, low quality') by computing separate noise predictions for both positive and negative prompts, then combining them: noise_pred = noise_neg + guidance_scale * (noise_pos - noise_neg). This enables users to specify what they don't want in the image, reducing common artifacts (e.g., distorted text, anatomical errors) without modifying model weights. Negative prompts are encoded using the same CLIP text encoder as positive prompts.","intents":["Reduce common artifacts (blurry, low quality, distorted) via negative prompts","Improve image quality without retraining or fine-tuning","Enable users to specify what they don't want in the image","Combine positive and negative guidance for fine-grained control"],"best_for":["Application developers improving image quality for end-users","Teams building interactive image generation tools with quality controls","Researchers studying the impact of negative prompts on generation quality"],"limitations":["Negative prompts require 2x additional forward passes (one for negative, one for positive)","Effectiveness varies widely depending on the specific negative prompt","No principled way to select optimal negative prompts; requires empirical tuning","Negative prompts may conflict with positive prompts, reducing overall quality","Requires external libraries (e.g., compel) for advanced negative prompt syntax"],"requires":["Diffusers library 0.10.0+","PyTorch 1.13+","2x VRAM overhead compared to positive-only guidance"],"input_types":["string (negative prompt, e.g., 'blurry, low quality, distorted')","string (positive prompt)"],"output_types":["PIL Image (improved quality via negative guidance)"],"categories":["image-visual","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-compvis--stable-diffusion-v1-4__cap_2","uri":"capability://planning.reasoning.classifier.free.guidance.for.prompt.adherence.control","name":"classifier-free guidance for prompt adherence control","description":"Implements conditional guidance by computing two separate noise predictions: one conditioned on the text embedding and one unconditional (null embedding). The final noise prediction is computed as: noise_pred = noise_uncond + guidance_scale * (noise_cond - noise_uncond), where guidance_scale typically ranges 7.5-15.0. Higher guidance scales increase adherence to the prompt at the cost of reduced diversity and potential artifacts. This technique requires 2x forward passes per denoising step but provides intuitive control over prompt-image alignment without modifying model weights.","intents":["Control the strength of prompt adherence vs. image diversity via a single hyperparameter","Improve image quality and prompt fidelity without retraining or fine-tuning","Enable users to trade off between creative variation and semantic accuracy","Reduce common artifacts (e.g., distorted text, anatomical errors) through stronger guidance"],"best_for":["Application developers tuning image generation quality for end-users","Researchers studying the guidance-diversity tradeoff in diffusion models","Teams building interactive image generation tools with user-controlled quality sliders"],"limitations":["Guidance_scale > 15 often produces oversaturated colors, repetitive patterns, and visual artifacts","Requires 2x compute per denoising step compared to unconditional generation","No principled way to select optimal guidance_scale; requires empirical tuning per use case","Guidance cannot disambiguate conflicting prompt elements (e.g., 'cat and dog fighting peacefully')","Extreme guidance values (>20) may cause training instability or NaN outputs"],"requires":["Diffusers library 0.10.0+","PyTorch 1.13+","2x VRAM overhead compared to unconditional generation"],"input_types":["float (guidance_scale, typically 7.5-15.0; default 7.5)","text (prompt for conditional prediction)","null (implicit unconditional embedding)"],"output_types":["PIL Image (512x512 RGB, conditioned on guidance scale)"],"categories":["planning-reasoning","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-compvis--stable-diffusion-v1-4__cap_3","uri":"capability://data.processing.analysis.variational.autoencoder.vae.latent.encoding.and.decoding","name":"variational autoencoder (vae) latent encoding and decoding","description":"Compresses 512x512 RGB images into a 64x64 latent representation using a learned VAE encoder, reducing spatial dimensions by 8x and enabling diffusion to operate in a compact latent space. The VAE encoder maps images to a mean and log-variance, sampling latents via the reparameterization trick. After diffusion denoising in latent space, a VAE decoder reconstructs the 512x512 image from the denoised latent. This two-stage approach (encode → diffuse → decode) reduces memory and compute by ~4x compared to pixel-space diffusion while maintaining perceptual quality through the learned decoder.","intents":["Reduce memory footprint and inference latency by operating on compressed representations","Maintain image quality despite 8x spatial compression through learned reconstruction","Enable batch processing of multiple images within fixed VRAM budgets","Support variable output resolutions (e.g., 768x768) by adjusting latent dimensions"],"best_for":["Developers deploying image generation on resource-constrained hardware (mobile, edge devices)","Teams optimizing inference cost and latency for production pipelines","Researchers studying information bottlenecks in generative models"],"limitations":["VAE decoder introduces ~2-3% perceptual quality loss compared to pixel-space diffusion","Latent space is fixed at 64x64; non-standard output resolutions require interpolation or tiling","VAE weights are frozen; cannot be fine-tuned to improve reconstruction quality","Latent space is not interpretable; direct manipulation of latents produces artifacts","Batch processing requires latents to be stacked; cannot mix different resolutions in a single batch"],"requires":["Diffusers library 0.10.0+","VAE model weights (~167MB, included in stable-diffusion-v1-4 checkpoint)","PyTorch 1.13+","~1GB VRAM for VAE encoder/decoder"],"input_types":["PIL Image (512x512 RGB, or any resolution; auto-resized)","torch tensor (shape [batch_size, 3, 512, 512], float32 in range [-1, 1])"],"output_types":["torch tensor (latent, shape [batch_size, 4, 64, 64], float32)","PIL Image (512x512 RGB after decoding)"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-compvis--stable-diffusion-v1-4__cap_4","uri":"capability://image.visual.unet.based.iterative.noise.prediction.and.denoising","name":"unet-based iterative noise prediction and denoising","description":"Implements a 27-layer UNet architecture with skip connections, attention blocks, and time embeddings to predict noise at each diffusion step. The UNet takes as input: (1) the noisy latent at timestep t, (2) the timestep embedding (sinusoidal positional encoding), and (3) the CLIP text embedding via cross-attention. Over 50 denoising steps, the model progressively reduces noise, guided by the predicted noise direction. Each step computes: latent_t-1 = (latent_t - sqrt(1 - alpha_bar_t) * noise_pred) / sqrt(alpha_bar_t), where alpha_bar_t is a pre-computed noise schedule. This iterative refinement transforms random noise into coherent images aligned with the text prompt.","intents":["Iteratively refine noisy latents into clean, prompt-aligned images through learned noise prediction","Control generation quality and diversity via the number of denoising steps (20-50 typical)","Enable multi-step image refinement without retraining or fine-tuning","Support both deterministic (fixed seed) and stochastic (random seed) generation"],"best_for":["ML engineers implementing custom diffusion pipelines or fine-tuning strategies","Researchers studying noise prediction and denoising dynamics","Developers optimizing inference speed vs. quality tradeoffs"],"limitations":["Inference latency scales linearly with num_inference_steps; 50 steps ~20-30s on consumer GPU","Quality improvement plateaus after ~30 steps; diminishing returns for >50 steps","UNet weights are frozen; cannot be adapted to new domains without full retraining","Noise schedule is fixed; cannot be dynamically adjusted per-image","Attention mechanisms scale quadratically with latent resolution; memory usage grows with batch size"],"requires":["Diffusers library 0.10.0+","UNet model weights (~3.4GB, included in stable-diffusion-v1-4 checkpoint)","PyTorch 1.13+","4-8GB VRAM for single-image inference"],"input_types":["torch tensor (noisy latent, shape [batch_size, 4, 64, 64])","integer (timestep, 0-999)","torch tensor (CLIP text embedding, shape [batch_size, 77, 768])"],"output_types":["torch tensor (predicted noise, shape [batch_size, 4, 64, 64])"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-compvis--stable-diffusion-v1-4__cap_5","uri":"capability://planning.reasoning.fixed.noise.schedule.and.timestep.sampling","name":"fixed noise schedule and timestep sampling","description":"Implements a linear noise schedule with 1000 timesteps, where noise variance increases monotonically from beta_start=0.0001 to beta_end=0.02. Pre-computes cumulative products (alpha_bar_t) for efficient noise injection: noisy_latent = sqrt(alpha_bar_t) * clean_latent + sqrt(1 - alpha_bar_t) * noise. During inference, timesteps are sampled uniformly (or reversed for deterministic generation) and used to index into the pre-computed schedule. This fixed schedule ensures stable training dynamics and reproducible generation when seeds are fixed.","intents":["Ensure reproducible image generation by fixing the noise schedule and random seed","Control generation diversity via timestep sampling strategy (uniform vs. custom)","Enable deterministic pipelines for testing and validation","Support both stochastic and deterministic inference modes"],"best_for":["Developers building reproducible image generation pipelines","Teams implementing A/B testing or quality assurance workflows","Researchers studying the impact of noise schedules on generation quality"],"limitations":["Noise schedule is fixed; cannot be adapted per-image or per-prompt","Linear schedule may not be optimal for all domains; cosine or other schedules require retraining","Timestep sampling is uniform; no adaptive sampling based on image content","1000 timesteps is fixed; cannot be dynamically adjusted without retraining"],"requires":["Diffusers library 0.10.0+","PyTorch 1.13+","Pre-computed alpha_bar values (included in diffusers)"],"input_types":["integer (random seed for reproducibility)","integer (num_inference_steps, typically 20-50)"],"output_types":["list of integers (timesteps for denoising, length = num_inference_steps)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-compvis--stable-diffusion-v1-4__cap_6","uri":"capability://automation.workflow.batch.processing.and.memory.efficient.inference","name":"batch processing and memory-efficient inference","description":"Supports batched inference by stacking multiple prompts and latents, processing them through the UNet and VAE in parallel. Memory usage scales linearly with batch size; typical batch sizes are 1-4 on consumer GPUs (8GB VRAM) and 8-16 on enterprise GPUs (40GB+ VRAM). Implements gradient checkpointing and attention slicing to reduce peak memory usage, enabling larger batches or longer prompts. Supports mixed-precision inference (float16) to halve memory footprint with minimal quality loss.","intents":["Generate multiple images in parallel to amortize fixed overhead costs","Maximize GPU utilization and throughput for production pipelines","Enable larger batch sizes on memory-constrained hardware via mixed precision","Support variable batch sizes without code changes"],"best_for":["Teams building high-throughput image generation services","Developers optimizing inference cost and latency for production","Researchers benchmarking diffusion models at scale"],"limitations":["Memory usage scales linearly with batch size; no sublinear batching strategies","Batch processing requires all prompts to have the same length (after padding/truncation)","Mixed-precision inference (float16) may introduce subtle quality degradation on some hardware","Attention slicing reduces memory but increases latency by ~10-20%","Gradient checkpointing is not applicable during inference (only training)"],"requires":["Diffusers library 0.10.0+","PyTorch 1.13+","4GB+ VRAM for batch_size=1; 8GB+ for batch_size=4"],"input_types":["list of strings (prompts, length = batch_size)","integer (batch_size, typically 1-4)"],"output_types":["list of PIL Images (length = batch_size)","torch tensor (shape [batch_size, 3, 512, 512])"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-compvis--stable-diffusion-v1-4__cap_7","uri":"capability://automation.workflow.seed.based.reproducible.generation","name":"seed-based reproducible generation","description":"Enables deterministic image generation by seeding PyTorch's random number generator before inference. When a seed is fixed, the same prompt produces identical images across runs, enabling reproducible testing and validation. Seed is passed to the generator object, which controls randomness in latent initialization and denoising step sampling. Without a fixed seed, generation is stochastic and produces different images for the same prompt.","intents":["Reproduce specific images for testing, debugging, or quality assurance","Enable A/B testing by comparing images generated with different seeds","Create deterministic pipelines for validation and regression testing","Share reproducible generation parameters with collaborators"],"best_for":["QA teams validating image generation quality","Researchers comparing generation strategies","Developers building reproducible ML pipelines"],"limitations":["Reproducibility is hardware-specific; same seed may produce slightly different images on different GPUs due to floating-point precision","Seed must be explicitly set; default behavior is stochastic","Reproducibility is not guaranteed across different PyTorch versions or CUDA versions","Seed controls only randomness in the generation process, not in data loading or preprocessing"],"requires":["PyTorch 1.13+","Diffusers library 0.10.0+"],"input_types":["integer (seed, typically 0-2^32-1)"],"output_types":["PIL Image (deterministic, identical across runs with same seed)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-compvis--stable-diffusion-v1-4__cap_8","uri":"capability://tool.use.integration.safetensors.format.model.loading.and.weight.management","name":"safetensors format model loading and weight management","description":"Loads model weights from safetensors format (a safer, faster alternative to pickle-based PyTorch checkpoints) using the safetensors library. Safetensors format includes metadata, type information, and checksums, enabling faster loading (~2-3x speedup vs. pickle) and protection against arbitrary code execution. Model weights are loaded into GPU memory on-demand, with optional CPU offloading for memory-constrained devices. Supports loading from HuggingFace Hub directly via model IDs (e.g., 'CompVis/stable-diffusion-v1-4').","intents":["Load model weights quickly and safely without executing arbitrary code","Manage model weights efficiently on memory-constrained devices via CPU offloading","Download and cache models from HuggingFace Hub automatically","Verify model integrity via checksums and metadata"],"best_for":["Developers deploying models in production with security constraints","Teams managing large model collections with efficient caching","Researchers studying model loading performance and memory management"],"limitations":["Safetensors format is newer; some legacy models may only be available in pickle format","CPU offloading reduces memory footprint but increases latency by ~10-20%","Model caching is local; no built-in distributed caching or CDN support","Metadata in safetensors is optional; not all models include complete type information"],"requires":["safetensors library 0.3.0+","Diffusers library 0.10.0+","PyTorch 1.13+","Internet connection for initial model download (~4GB)"],"input_types":["string (model ID, e.g., 'CompVis/stable-diffusion-v1-4')","string (local path to safetensors file)"],"output_types":["dict (model weights, loaded into GPU or CPU memory)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-compvis--stable-diffusion-v1-4__cap_9","uri":"capability://image.visual.cross.attention.mechanism.for.semantic.conditioning","name":"cross-attention mechanism for semantic conditioning","description":"Injects CLIP text embeddings into the UNet via cross-attention at 4 resolution scales (8x, 16x, 32x, 64x downsampling). At each scale, the attention mechanism computes: Attention(Q, K, V) = softmax(Q * K^T / sqrt(d)) * V, where Q is derived from the latent features, K and V are derived from the CLIP embedding. This enables the model to attend to different parts of the prompt at different spatial scales, allowing fine-grained semantic control. Cross-attention is applied at every residual block, enabling hierarchical conditioning.","intents":["Enable fine-grained semantic control over image generation via text prompts","Condition image generation at multiple spatial scales for hierarchical semantic alignment","Support complex prompts with multiple concepts (e.g., 'a red car and a blue house')","Enable prompt-based image editing and manipulation"],"best_for":["Developers building interactive image generation interfaces with semantic control","Researchers studying attention mechanisms in conditional generation","Teams implementing prompt-based image editing or manipulation"],"limitations":["Cross-attention is computationally expensive; adds ~20-30% latency compared to unconditional generation","Attention weights are not easily interpretable; difficult to debug prompt-image misalignment","Multi-scale attention may conflict for complex prompts; no principled way to resolve conflicts","Attention mechanism is frozen; cannot be fine-tuned without retraining the entire model"],"requires":["Diffusers library 0.10.0+","PyTorch 1.13+","CLIP text embeddings (shape [batch_size, 77, 768])"],"input_types":["torch tensor (latent features at each resolution scale)","torch tensor (CLIP text embedding, shape [batch_size, 77, 768])"],"output_types":["torch tensor (attended features, same shape as input latent)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":50,"verified":false,"data_access_risk":"low","permissions":["Python 3.8+","PyTorch 1.13+ with CUDA 11.6+ or CPU fallback (significantly slower)","4GB+ VRAM for inference (8GB+ recommended for batch processing)","HuggingFace transformers library 4.21+","Diffusers library 0.10.0+ for StableDiffusionPipeline","Internet connection for initial model download (~4GB total weights)","CLIP model weights (openai/clip-vit-large-patch14, ~600MB)","PyTorch 1.13+","~2GB VRAM for text encoder alone","Diffusers library 0.10.0+"],"failure_modes":["Inference requires 4-8GB VRAM for single image generation; batch processing scales linearly with batch size","Quality degrades with prompts longer than ~77 tokens due to CLIP tokenizer limits","Deterministic output only when seed is fixed; stochastic sampling introduces variance across runs","No native inpainting or outpainting; requires separate model variants or post-processing","Training data biases reflected in outputs; may struggle with non-English prompts or underrepresented concepts","Inference latency ~5-30 seconds per image on consumer GPUs depending on hardware and step count","CLIP tokenizer truncates prompts at 77 tokens; longer descriptions are silently dropped","Embedding space trained primarily on English; non-English prompts produce lower-quality results","No native support for negative prompts or prompt weighting; requires external libraries (e.g., compel)","Semantic ambiguity in prompts (e.g., 'bank') cannot be disambiguated without additional context","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7642680462512651,"quality":0.34,"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":621488,"model_likes":7004}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=compvis--stable-diffusion-v1-4","compare_url":"https://unfragile.ai/compare?artifact=compvis--stable-diffusion-v1-4"}},"signature":"/l9lSflRYwwKhiX3qoIaQRbXEmGMCArtU0QmApPlLXYzmwKBBM+ca990WOFJYT5rEr+ov5RhycB2GRu9qUcODw==","signedAt":"2026-06-21T04:53:02.355Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/compvis--stable-diffusion-v1-4","artifact":"https://unfragile.ai/compvis--stable-diffusion-v1-4","verify":"https://unfragile.ai/api/v1/verify?slug=compvis--stable-diffusion-v1-4","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"}}