{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-stabilityai--sd-turbo","slug":"stabilityai--sd-turbo","name":"sd-turbo","type":"model","url":"https://huggingface.co/stabilityai/sd-turbo","page_url":"https://unfragile.ai/stabilityai--sd-turbo","categories":["image-generation"],"tags":["diffusers","safetensors","text-to-image","diffusers:StableDiffusionPipeline","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-stabilityai--sd-turbo__cap_0","uri":"capability://image.visual.single.step.text.to.image.generation.with.latency.optimization","name":"single-step text-to-image generation with latency optimization","description":"Generates photorealistic images from text prompts in a single diffusion step using a distilled UNet architecture, eliminating the iterative denoising loop required by standard Stable Diffusion models. The model employs knowledge distillation from a multi-step teacher model to compress inference into one forward pass, trading some quality for sub-second generation latency. Implemented via the diffusers StableDiffusionPipeline with custom scheduler configuration that skips intermediate denoising steps.","intents":["Generate images in real-time interactive applications where sub-second latency is critical","Build low-latency image generation APIs that can serve high-throughput requests without GPU scaling","Create responsive UI components that generate images on-demand without noticeable delay","Deploy image generation on edge devices or consumer hardware with limited compute budgets"],"best_for":["developers building real-time creative tools or interactive demos","teams deploying image generation at scale with latency constraints","edge ML engineers targeting consumer GPUs or mobile inference","startups prototyping image-based products with cost-sensitive infrastructure"],"limitations":["Single-step generation produces lower visual quality and fine detail compared to 20-50 step Stable Diffusion v1.5 or SDXL","Reduced semantic understanding of complex multi-object prompts due to compressed inference capacity","Limited control over generation process — no intermediate step manipulation or progressive refinement possible","Output resolution capped at 512x512 pixels; no native support for higher resolutions without tiling or upsampling","Deterministic single-step output means less diversity in generations from identical prompts compared to multi-step models"],"requires":["Python 3.8+","PyTorch 1.13+ with CUDA 11.6+ for GPU acceleration (CPU inference possible but ~30-60 seconds per image)","diffusers library 0.21.0+","4GB+ VRAM for GPU inference, or 8GB+ system RAM for CPU-only","HuggingFace transformers library 4.25.0+ for tokenizer and text encoder"],"input_types":["text (natural language prompts, 1-77 tokens after tokenization)","optional: negative prompts (text describing unwanted content)","optional: seed (integer for reproducible generation)","optional: guidance_scale (float 1.0-20.0 for prompt adherence strength)"],"output_types":["PIL Image (512x512 RGB)","torch.Tensor (1, 3, 512, 512 float32)","numpy array (512, 512, 3 uint8)"],"categories":["image-visual","real-time-inference"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-stabilityai--sd-turbo__cap_1","uri":"capability://text.generation.language.prompt.to.latent.encoding.with.clip.text.embeddings","name":"prompt-to-latent encoding with clip text embeddings","description":"Encodes natural language prompts into a 768-dimensional CLIP text embedding space using OpenAI's CLIP ViT-L/14 tokenizer and text encoder, which conditions the diffusion process. The text encoder processes up to 77 tokens, padding or truncating longer prompts, and outputs embeddings that guide the UNet denoiser toward semantically relevant image generation. This embedding-based conditioning replaces pixel-space guidance, enabling efficient cross-modal alignment without explicit image-text pairs during inference.","intents":["Convert arbitrary natural language descriptions into machine-readable image generation instructions","Implement semantic search over generated images by comparing CLIP embeddings","Build prompt engineering interfaces that show embedding similarity scores","Enable multi-modal applications that combine text and image understanding"],"best_for":["developers building user-facing image generation interfaces","researchers studying text-image alignment and semantic understanding","teams implementing prompt optimization or A/B testing workflows","applications requiring semantic similarity matching between prompts"],"limitations":["CLIP tokenizer limited to 77 tokens; longer prompts are truncated without warning, losing semantic information","CLIP embeddings trained on internet-scale data may have biases or misalignments for domain-specific terminology","No explicit support for weighted prompts or token-level importance — all tokens treated equally in conditioning","Embedding space is fixed at 768 dimensions; cannot be fine-tuned or adapted for custom domains without retraining"],"requires":["transformers library 4.25.0+","CLIP model weights (automatically downloaded from HuggingFace on first use, ~340MB)","PyTorch 1.13+","2GB+ VRAM for text encoder inference"],"input_types":["text (natural language prompts, ASCII or Unicode)","optional: negative prompts (text describing unwanted attributes)"],"output_types":["torch.Tensor (1, 77, 768 float32) — token embeddings","torch.Tensor (1, 768 float32) — pooled prompt embedding"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-stabilityai--sd-turbo__cap_2","uri":"capability://image.visual.distilled.unet.denoising.with.single.step.inference","name":"distilled unet denoising with single-step inference","description":"A compressed UNet architecture that performs image denoising in a single forward pass, trained via knowledge distillation from a multi-step teacher model. The UNet processes latent-space representations (4x compressed via VAE) and progressively refines them conditioned on CLIP embeddings and timestep information. Unlike standard diffusion which iterates 20-50 times, this model skips directly from pure noise to final image, using learned shortcuts to approximate the full denoising trajectory in one step.","intents":["Generate images with minimal computational overhead for deployment on resource-constrained hardware","Build batch image generation pipelines that process multiple prompts in parallel without sequential iteration","Implement real-time image generation in web browsers or mobile apps via ONNX or TensorFlow.js conversion","Create interactive tools where users see results instantly without waiting for iterative refinement"],"best_for":["edge ML engineers optimizing for inference latency and memory usage","web developers deploying image generation in browsers via WebGL or WebGPU","mobile app developers targeting iOS/Android with on-device inference","infrastructure teams minimizing GPU utilization and cost per image"],"limitations":["Single-step inference cannot be interrupted or guided mid-generation; no progressive refinement or user control over denoising trajectory","Knowledge distillation introduces a quality ceiling — cannot exceed teacher model quality even with longer inference","Latent-space artifacts or compression noise more visible than in multi-step models due to lack of iterative refinement","No built-in support for inpainting, outpainting, or image-to-image tasks — designed for unconditional text-to-image only","Reduced diversity in outputs for identical prompts compared to stochastic multi-step sampling"],"requires":["PyTorch 1.13+ or ONNX Runtime 1.14+","Model weights (2.0GB safetensors file)","CUDA 11.6+ for GPU acceleration (optional but recommended)","4GB+ VRAM for batch size 1 on consumer GPUs"],"input_types":["latent tensors (1, 4, 64, 64 float32) from VAE encoder","CLIP text embeddings (1, 77, 768 float32)","timestep tensor (integer, typically 0 for single-step)","optional: guidance_scale (float for classifier-free guidance strength)"],"output_types":["latent tensors (1, 4, 64, 64 float32) — denoised latents","PIL Image (512x512 RGB) after VAE decoding"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-stabilityai--sd-turbo__cap_3","uri":"capability://image.visual.vae.latent.encoding.and.decoding.for.image.compression","name":"vae latent encoding and decoding for image compression","description":"Encodes 512x512 RGB images into a 4x-compressed latent space (64x64x4 tensors) using a pre-trained Variational Autoencoder, and decodes denoised latents back to pixel space. The VAE operates in the diffusion pipeline as a bottleneck: prompts and noise are processed in latent space (4x faster than pixel space), then decoded to final images. This compression reduces memory usage and computation by 16x compared to pixel-space diffusion, enabling faster inference on consumer hardware.","intents":["Reduce memory footprint and computation time by working in compressed latent space instead of pixel space","Enable batch processing of multiple images simultaneously within GPU memory constraints","Implement image-to-image or inpainting workflows by encoding reference images into latent space","Build multi-modal applications that combine image generation with image understanding tasks"],"best_for":["developers optimizing inference latency and GPU memory usage","teams building batch image generation pipelines","researchers studying latent-space representations and generative models","applications requiring image compression or feature extraction"],"limitations":["VAE compression introduces quantization artifacts and loss of fine details; decoded images are slightly blurrier than originals","Latent space is not interpretable — cannot directly manipulate latents for semantic edits without additional models","VAE decoder quality varies by checkpoint; some checkpoints produce more visible compression artifacts than others","Fixed 4x compression ratio; cannot adjust compression level without retraining or using alternative VAE architectures","Decoding latents back to pixels adds ~100-200ms latency per image on consumer GPUs"],"requires":["PyTorch 1.13+","VAE model weights (167MB safetensors file, automatically downloaded)","2GB+ VRAM for VAE encoding/decoding","diffusers library 0.21.0+"],"input_types":["PIL Image (512x512 RGB) for encoding","torch.Tensor (1, 4, 64, 64 float32) for decoding"],"output_types":["torch.Tensor (1, 4, 64, 64 float32) — latent representation","PIL Image (512x512 RGB) — decoded image"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-stabilityai--sd-turbo__cap_4","uri":"capability://image.visual.classifier.free.guidance.for.prompt.adherence.control","name":"classifier-free guidance for prompt adherence control","description":"Implements classifier-free guidance (CFG) by running the UNet twice per generation step — once conditioned on the text embedding and once unconditionally — then interpolating between outputs using a guidance_scale parameter. Higher guidance_scale values (7-15) increase adherence to the prompt at the cost of reduced diversity and potential artifacts; lower values (1-3) produce more diverse but less prompt-aligned images. This technique requires no additional classifier network, instead using the model's own unconditional predictions as a baseline.","intents":["Control how strictly the model adheres to input prompts vs generating diverse variations","Fine-tune output quality by adjusting guidance strength without retraining or changing prompts","Implement interactive sliders or parameters that let users control prompt influence in real-time","Balance between prompt fidelity and image diversity for different use cases"],"best_for":["developers building interactive image generation UIs with user-facing controls","teams tuning model behavior for specific domains or quality requirements","researchers studying the trade-off between prompt adherence and diversity","applications requiring flexible control over generation characteristics"],"limitations":["Guidance requires 2x forward passes per generation step, doubling inference latency (0.5s → 1s for sd-turbo)","High guidance_scale values (>15) can produce artifacts, oversaturation, or unrealistic features","Guidance_scale is a global parameter — cannot apply different guidance to different parts of the prompt","Unconditional generation quality depends on training data; models trained on curated data may produce poor unconditional outputs","No built-in mechanism to weight different parts of the prompt differently — all tokens receive equal guidance"],"requires":["Model trained with classifier-free guidance (sd-turbo supports this)","guidance_scale parameter (float, typically 1.0-20.0)","2x computational budget compared to unconditional generation"],"input_types":["CLIP text embeddings (1, 77, 768 float32)","guidance_scale (float, default 7.5)","optional: negative_prompt_embeds for explicit negative conditioning"],"output_types":["PIL Image (512x512 RGB) — guided generation output"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-stabilityai--sd-turbo__cap_5","uri":"capability://tool.use.integration.diffusers.pipeline.integration.with.scheduler.abstraction","name":"diffusers pipeline integration with scheduler abstraction","description":"Wraps the UNet, VAE, and text encoder into a unified StableDiffusionPipeline object that abstracts away the complexity of noise scheduling, timestep management, and multi-component orchestration. The pipeline uses a scheduler (e.g., DDIMScheduler, PNDMScheduler) to determine noise levels and denoising steps, enabling swappable inference strategies without changing the core model. For sd-turbo, the pipeline is configured with a single-step scheduler that skips intermediate steps, but the same pipeline can be used with multi-step schedulers for other checkpoints.","intents":["Simplify image generation by providing a high-level API that handles component coordination","Swap inference strategies (schedulers) without rewriting generation code","Integrate image generation into larger applications without managing low-level diffusion mechanics","Enable reproducible generation by controlling random seeds and scheduler parameters"],"best_for":["developers building applications that need simple, high-level image generation APIs","teams experimenting with different schedulers or inference strategies","researchers prototyping diffusion-based applications without deep ML expertise","applications requiring reproducible generation with fixed seeds"],"limitations":["Pipeline abstraction adds ~50-100ms overhead per generation due to component orchestration and tensor transfers","Limited visibility into intermediate steps — difficult to inspect or modify latents mid-generation without subclassing","Scheduler selection is global — cannot use different schedulers for different parts of the generation","Pipeline does not support dynamic batch sizes; batch size must be set at initialization","No built-in support for advanced features like LoRA, textual inversion, or custom attention mechanisms without custom code"],"requires":["diffusers library 0.21.0+","PyTorch 1.13+","transformers library 4.25.0+","safetensors library for loading model weights"],"input_types":["prompt (string)","negative_prompt (string, optional)","height, width (integers, default 512)","num_inference_steps (integer, default 1 for sd-turbo)","guidance_scale (float, default 7.5)","seed (integer, optional)"],"output_types":["PIL Image (512x512 RGB)","numpy array (512, 512, 3 uint8)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-stabilityai--sd-turbo__cap_6","uri":"capability://tool.use.integration.safetensors.model.weight.loading.with.format.compatibility","name":"safetensors model weight loading with format compatibility","description":"Loads model weights from safetensors format (a safer, faster alternative to pickle-based PyTorch .pt files) directly into the UNet, VAE, and text encoder components. Safetensors provides memory-mapped loading, enabling efficient weight initialization without loading the entire file into RAM first. The pipeline automatically detects and loads safetensors files from HuggingFace Hub, with fallback to .pt format if safetensors is unavailable, ensuring compatibility across different model sources.","intents":["Load model weights safely without executing arbitrary Python code (pickle vulnerability)","Reduce memory overhead during model loading via memory-mapped file access","Enable faster model initialization by avoiding pickle deserialization overhead","Ensure reproducible model loading across different systems and Python versions"],"best_for":["developers prioritizing security and reproducibility in model loading","teams deploying models in restricted environments where pickle is disabled","applications with strict memory constraints that benefit from memory-mapped loading","researchers sharing models via HuggingFace Hub with standardized formats"],"limitations":["Safetensors format is newer and less widely supported than .pt format in some legacy tools","Memory-mapped loading requires the file to remain accessible on disk; cannot be used with in-memory model caches","Safetensors files are slightly larger than .pt files due to metadata overhead (~5-10% larger)","No built-in support for partial weight loading or selective layer initialization"],"requires":["safetensors library 0.3.0+","PyTorch 1.13+","2GB+ disk space for model weights"],"input_types":["safetensors file path (local or HuggingFace Hub identifier)","optional: device (CPU or CUDA)"],"output_types":["loaded model state dict in PyTorch"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-stabilityai--sd-turbo__cap_7","uri":"capability://automation.workflow.seed.based.reproducible.generation.for.deterministic.outputs","name":"seed-based reproducible generation for deterministic outputs","description":"Enables reproducible image generation by seeding the random number generator with a fixed integer value, ensuring identical outputs for identical prompts and parameters across different runs and hardware. The seed controls noise initialization and any stochastic operations in the scheduler, making generation fully deterministic when seed is specified. This is critical for testing, debugging, and creating consistent outputs in production systems.","intents":["Generate identical images for the same prompt across different runs for testing and validation","Create reproducible demos or examples that always produce the same output","Debug generation issues by isolating randomness from other variables","Enable version control and reproducibility in ML pipelines"],"best_for":["developers building testing and validation frameworks","teams creating reproducible demos or documentation","researchers conducting controlled experiments with image generation","applications requiring deterministic behavior for compliance or auditing"],"limitations":["Reproducibility is only guaranteed within the same PyTorch version and hardware; different versions or GPUs may produce slightly different results due to floating-point precision","Seed-based reproducibility does not apply to external randomness (e.g., prompt variations, guidance_scale changes)","Very large seed values (>2^31) may cause unexpected behavior in some random number generators","Reproducibility requires fixing all other parameters (prompt, guidance_scale, scheduler, etc.); any change breaks determinism"],"requires":["seed parameter (integer, 0-2^32-1)","PyTorch 1.13+ with deterministic mode enabled (optional but recommended)"],"input_types":["seed (integer, optional, default None for random generation)"],"output_types":["PIL Image (512x512 RGB) — deterministic output for fixed seed"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":46,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","PyTorch 1.13+ with CUDA 11.6+ for GPU acceleration (CPU inference possible but ~30-60 seconds per image)","diffusers library 0.21.0+","4GB+ VRAM for GPU inference, or 8GB+ system RAM for CPU-only","HuggingFace transformers library 4.25.0+ for tokenizer and text encoder","transformers library 4.25.0+","CLIP model weights (automatically downloaded from HuggingFace on first use, ~340MB)","PyTorch 1.13+","2GB+ VRAM for text encoder inference","PyTorch 1.13+ or ONNX Runtime 1.14+"],"failure_modes":["Single-step generation produces lower visual quality and fine detail compared to 20-50 step Stable Diffusion v1.5 or SDXL","Reduced semantic understanding of complex multi-object prompts due to compressed inference capacity","Limited control over generation process — no intermediate step manipulation or progressive refinement possible","Output resolution capped at 512x512 pixels; no native support for higher resolutions without tiling or upsampling","Deterministic single-step output means less diversity in generations from identical prompts compared to multi-step models","CLIP tokenizer limited to 77 tokens; longer prompts are truncated without warning, losing semantic information","CLIP embeddings trained on internet-scale data may have biases or misalignments for domain-specific terminology","No explicit support for weighted prompts or token-level importance — all tokens treated equally in conditioning","Embedding space is fixed at 768 dimensions; cannot be fine-tuned or adapted for custom domains without retraining","Single-step inference cannot be interrupted or guided mid-generation; no progressive refinement or user control over denoising trajectory","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.703049368504555,"quality":0.26,"ecosystem":0.45,"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":"2026-05-24T12:16:22.766Z","last_scraped_at":"2026-05-03T14:22:49.651Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":608507,"model_likes":447}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=stabilityai--sd-turbo","compare_url":"https://unfragile.ai/compare?artifact=stabilityai--sd-turbo"}},"signature":"IBK2GnrFgC0QbV5k9Yf9e4hGnwnc76d/szS7zUoukfcEfzVVFeLaUA32GHWIhwa8cbH+uKD+YOC5bZ7OEnrYAQ==","signedAt":"2026-06-20T03:03:57.474Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/stabilityai--sd-turbo","artifact":"https://unfragile.ai/stabilityai--sd-turbo","verify":"https://unfragile.ai/api/v1/verify?slug=stabilityai--sd-turbo","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"}}