{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-stabilityai--sdxl-turbo","slug":"stabilityai--sdxl-turbo","name":"sdxl-turbo","type":"model","url":"https://huggingface.co/stabilityai/sdxl-turbo","page_url":"https://unfragile.ai/stabilityai--sdxl-turbo","categories":["image-generation"],"tags":["diffusers","onnx","safetensors","text-to-image","license:other","diffusers:StableDiffusionXLPipeline","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-stabilityai--sdxl-turbo__cap_0","uri":"capability://image.visual.single.step.text.to.image.generation.with.adversarial.diffusion.distillation","name":"single-step text-to-image generation with adversarial diffusion distillation","description":"Generates photorealistic images from text prompts in a single diffusion step using adversarial diffusion distillation (ADD), a technique that trains a student model to match multi-step teacher model outputs. The architecture uses a UNet backbone with cross-attention layers for text conditioning, eliminating the iterative refinement loop of standard diffusion models. Inference runs on consumer GPUs (8GB VRAM) in ~0.5 seconds per image.","intents":["Generate high-quality images from text prompts in real-time for interactive applications","Deploy text-to-image generation on edge devices or serverless functions with strict latency budgets","Build batch image generation pipelines that prioritize throughput over iterative quality refinement","Prototype image-based UIs without waiting for multi-second diffusion iterations"],"best_for":["Real-time web applications requiring sub-second image generation","Mobile and edge deployment scenarios with limited compute","Developers building interactive creative tools with tight latency SLAs","Teams prototyping image-generation features before optimizing quality"],"limitations":["Single-step generation trades iterative refinement for speed — image quality plateaus earlier than multi-step models like SDXL 1.0","Prompt engineering sensitivity is higher; complex multi-object scenes may require more detailed prompts than standard SDXL","No built-in support for negative prompts or guidance scaling in the base model — requires custom pipeline modifications","Fixed 512×512 output resolution; upscaling requires separate super-resolution model","Adversarial training introduces potential mode collapse on underrepresented prompt categories"],"requires":["Python 3.8+","PyTorch 1.13+ with CUDA 11.6+ (or CPU, but inference ~10x slower)","8GB+ GPU VRAM (RTX 3060 or equivalent) for optimal performance","diffusers library 0.21.0+","transformers library 4.25.0+ for text encoding"],"input_types":["text (natural language prompts, 1-77 tokens after CLIP tokenization)","optional: seed (integer for reproducibility)","optional: guidance_scale (float, typically 0.0-7.5 for ADD models)"],"output_types":["PIL Image (512×512 RGB)","NumPy array (uint8, shape [512, 512, 3])","PyTorch tensor (float32, shape [1, 3, 512, 512])"],"categories":["image-visual","diffusion-models"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-stabilityai--sdxl-turbo__cap_1","uri":"capability://image.visual.clip.based.text.encoding.with.cross.attention.conditioning","name":"clip-based text encoding with cross-attention conditioning","description":"Encodes text prompts into 768-dimensional embeddings using OpenAI's CLIP text encoder, then conditions the diffusion UNet via cross-attention layers that align image generation with semantic text features. The architecture applies attention mechanisms across spatial feature maps, allowing fine-grained control over which image regions correspond to which prompt tokens. This enables both global scene composition and local attribute binding (e.g., 'red car' → red pixels localized to car regions).","intents":["Control image composition and object attributes through natural language descriptions","Bind specific visual attributes (colors, materials, styles) to objects mentioned in prompts","Generate variations of the same scene by modifying only certain prompt tokens","Debug generation failures by understanding which prompt tokens influence which image regions"],"best_for":["Developers building prompt-driven image generation interfaces","Researchers studying text-image alignment and semantic grounding","Teams building multi-modal applications requiring interpretable text-to-image mappings"],"limitations":["CLIP tokenizer has 77-token limit; longer prompts are truncated without warning","Cross-attention is computed at 64×64 spatial resolution (downsampled from 512×512), losing fine-grained spatial precision","Prompt ambiguity (e.g., 'bank' as financial institution vs riverbank) is resolved by CLIP's training data bias, not explicit disambiguation","No native support for weighted token importance or prompt weighting syntax (requires custom pipeline code)"],"requires":["transformers library 4.25.0+ with CLIP model weights (~1.4GB download)","PyTorch 1.13+","CUDA 11.6+ for GPU acceleration (CPU encoding adds ~2-3s latency)"],"input_types":["text (natural language, max 77 CLIP tokens)","optional: token_weights (list of floats for per-token importance, custom implementation)"],"output_types":["PyTorch tensor (float32, shape [1, 77, 768] — padded to max sequence length)","attention maps (float32, shape [num_layers, num_heads, 64, 64, 77] — optional, requires custom hook)"],"categories":["image-visual","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-stabilityai--sdxl-turbo__cap_2","uri":"capability://image.visual.latent.space.diffusion.with.unet.denoising.backbone","name":"latent-space diffusion with unet denoising backbone","description":"Performs iterative denoising in a compressed 64×64 latent space (4× downsampling from 512×512 pixel space) using a UNet architecture with residual blocks, attention layers, and time-step embeddings. The model learns to predict noise added to latents at each diffusion step, progressively refining the latent representation. In SDXL-Turbo, this is compressed to a single step via distillation, but the underlying UNet architecture remains unchanged from standard SDXL. Latent-space diffusion reduces memory overhead and computation vs pixel-space diffusion by ~16×.","intents":["Generate images efficiently on memory-constrained hardware by operating in compressed latent space","Achieve faster inference by reducing spatial dimensions from 512×512 to 64×64 for denoising","Enable fine-grained control over image generation through latent-space manipulation and interpolation","Support downstream tasks like image editing and inpainting by working with latent representations"],"best_for":["Developers deploying on GPUs with <8GB VRAM","Teams building batch image generation pipelines prioritizing throughput","Researchers exploring latent-space interpolation and image morphing"],"limitations":["Latent-space compression introduces quantization artifacts; fine details (e.g., text in images, intricate patterns) are often lost","UNet architecture has fixed receptive field; global coherence depends on attention mechanisms, which can fail on complex multi-object scenes","Single-step distillation removes iterative refinement, limiting the model's ability to correct early mistakes","Latent space is not human-interpretable; debugging generation failures requires decoding to pixel space"],"requires":["PyTorch 1.13+ with CUDA support","VAE decoder weights (~200MB) for converting latents back to pixel space","8GB+ GPU VRAM for batch inference; 4GB minimum for single-image generation"],"input_types":["noise tensor (float32, shape [batch_size, 4, 64, 64])","timestep embedding (integer, 0-999 for standard diffusion; 0 for single-step)","text conditioning (float32, shape [batch_size, 77, 768] from CLIP encoder)"],"output_types":["denoised latent tensor (float32, shape [batch_size, 4, 64, 64])","decoded image (PIL Image or NumPy array after VAE decoding)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-stabilityai--sdxl-turbo__cap_3","uri":"capability://image.visual.batch.image.generation.with.configurable.inference.parameters","name":"batch image generation with configurable inference parameters","description":"Generates multiple images in parallel by batching prompts and noise tensors through the UNet, leveraging GPU parallelism to amortize fixed overhead costs. The diffusers StableDiffusionXLPipeline orchestrates batching, handling variable prompt lengths via padding, synchronizing noise schedules, and managing memory allocation. Supports configurable parameters: guidance_scale (0.0-7.5), num_inference_steps (1 for turbo, 1-50 for standard), and seed for reproducibility. Batch size is limited by GPU VRAM; typical throughput is 10-20 images/second on RTX 3090.","intents":["Generate multiple image variations from a single prompt in parallel","Create image datasets for training downstream models (e.g., classifiers, super-resolution)","Implement A/B testing by generating multiple seeds and comparing outputs","Optimize cost-per-image by batching requests in serverless or cloud environments"],"best_for":["Teams building batch image generation pipelines for data augmentation","Developers optimizing inference cost in cloud deployments","Researchers generating synthetic datasets for model training"],"limitations":["Batch size is limited by GPU VRAM; RTX 3090 (24GB) supports ~4-6 images per batch at 512×512","Variable prompt lengths require padding to max sequence length (77 tokens), wasting computation on short prompts","No dynamic batching; batch size must be fixed at pipeline initialization","Guidance_scale > 0 requires computing both conditional and unconditional predictions, doubling memory usage"],"requires":["PyTorch 1.13+ with CUDA 11.6+","diffusers 0.21.0+","GPU with sufficient VRAM: 8GB minimum (batch_size=1), 16GB+ recommended (batch_size=4+)"],"input_types":["prompts (list of strings, variable length)","batch_size (integer, 1-8 typical)","num_inference_steps (integer, 1 for turbo)","guidance_scale (float, 0.0-7.5)","seed (integer or None for random)"],"output_types":["list of PIL Images (length = batch_size)","list of NumPy arrays (uint8, shape [512, 512, 3])"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-stabilityai--sdxl-turbo__cap_4","uri":"capability://image.visual.reproducible.image.generation.via.seed.control","name":"reproducible image generation via seed control","description":"Enables deterministic image generation by seeding PyTorch's random number generator and the noise initialization tensor. When the same seed, prompt, and hyperparameters are used, the model produces pixel-identical outputs. This is implemented via torch.manual_seed() and torch.cuda.manual_seed() calls before noise sampling. Seed control is essential for debugging, A/B testing, and ensuring consistency across deployments. Note: reproducibility is only guaranteed within the same PyTorch version and hardware; different GPUs or PyTorch versions may produce slightly different results due to floating-point non-determinism.","intents":["Debug generation failures by reproducing the exact same image","Implement A/B testing by generating multiple seeds and comparing outputs","Ensure consistency across deployments and environments","Create deterministic image datasets for model training and evaluation"],"best_for":["Developers debugging generation failures and prompt engineering","QA teams testing image generation pipelines","Researchers requiring reproducible synthetic data"],"limitations":["Reproducibility is only guaranteed within the same PyTorch version, CUDA version, and hardware (GPU model)","Different GPU architectures (e.g., RTX 3090 vs A100) may produce slightly different results due to floating-point rounding","Batch generation with multiple seeds requires separate forward passes; no way to generate multiple seeds in a single batch","Seed control does not guarantee reproducibility across different diffusers versions or model checkpoints"],"requires":["PyTorch 1.13+","CUDA 11.6+ (for GPU reproducibility)","diffusers 0.21.0+"],"input_types":["seed (integer, 0-2^32-1)","prompt (string)","other hyperparameters (guidance_scale, num_inference_steps, etc.)"],"output_types":["PIL Image (pixel-identical across runs with same seed and hardware)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-stabilityai--sdxl-turbo__cap_5","uri":"capability://image.visual.memory.efficient.inference.via.8.bit.quantization.and.attention.optimization","name":"memory-efficient inference via 8-bit quantization and attention optimization","description":"Reduces memory footprint and inference latency by applying 8-bit quantization to model weights and optimizing attention computation. The diffusers library supports loading SDXL-Turbo in 8-bit via bitsandbytes, reducing model size from 6.9GB (float32) to ~1.7GB (int8). Additionally, xFormers or Flash Attention implementations can be enabled to reduce attention memory from O(seq_len²) to O(seq_len) and speed up computation by 2-4×. These optimizations are transparent to the user and require only a single flag at pipeline initialization.","intents":["Deploy SDXL-Turbo on GPUs with <8GB VRAM (e.g., RTX 3060, RTX 4060)","Reduce inference latency by 20-30% via attention optimization","Minimize memory footprint for serverless or edge deployments","Enable larger batch sizes on memory-constrained hardware"],"best_for":["Developers deploying on consumer GPUs with 4-8GB VRAM","Teams optimizing inference cost in cloud environments","Edge deployment scenarios with strict memory budgets"],"limitations":["8-bit quantization introduces ~1-2% quality degradation (imperceptible to humans but measurable in metrics)","bitsandbytes requires CUDA 11.6+ and is not compatible with CPU inference","xFormers/Flash Attention are optional dependencies; if not installed, attention falls back to slower PyTorch implementation","Quantization adds ~1-2s overhead on first inference (model loading and quantization)","Not all attention implementations are compatible with all hardware; Flash Attention requires Ampere+ GPUs (RTX 30 series or newer)"],"requires":["PyTorch 1.13+ with CUDA 11.6+","bitsandbytes 0.39.0+ (for 8-bit quantization)","xFormers 0.0.16+ (optional, for attention optimization)","diffusers 0.21.0+"],"input_types":["load_in_8bit (boolean flag)","enable_attention_slicing (boolean flag)","enable_xformers_memory_efficient_attention (boolean flag)"],"output_types":["PIL Image (same as standard inference, no quality loss visible to humans)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-stabilityai--sdxl-turbo__cap_6","uri":"capability://image.visual.model.weight.loading.from.huggingface.hub.with.safetensors.format","name":"model weight loading from huggingface hub with safetensors format","description":"Loads pre-trained SDXL-Turbo weights from HuggingFace Hub using the safetensors format, a secure binary format that prevents arbitrary code execution during deserialization (unlike pickle). The diffusers library automatically downloads and caches weights (~6.9GB) on first use, storing them in ~/.cache/huggingface/hub/. Supports resumable downloads, local weight loading, and custom cache directories. Weights are organized as a diffusers pipeline (text_encoder, unet, vae, scheduler), enabling modular component replacement (e.g., swapping VAE or scheduler).","intents":["Load pre-trained SDXL-Turbo weights from HuggingFace Hub without manual downloading","Use safetensors format for secure weight loading without code execution risks","Cache weights locally to avoid repeated downloads","Replace individual pipeline components (VAE, scheduler) with custom implementations"],"best_for":["Developers integrating SDXL-Turbo into applications via HuggingFace Hub","Teams requiring secure model loading without pickle deserialization risks","Researchers experimenting with component swapping (e.g., different VAE or schedulers)"],"limitations":["Initial download is ~6.9GB; requires stable internet connection and ~15-30 minutes on typical broadband","Cache directory can grow large; no built-in cleanup mechanism (requires manual deletion of ~/.cache/huggingface/hub/)","Resumable downloads are not supported by all network conditions; interrupted downloads may require full restart","HuggingFace Hub API rate limits apply; excessive requests may trigger temporary blocks","No built-in version pinning; loading 'stabilityai/sdxl-turbo' always fetches the latest revision"],"requires":["Python 3.8+","huggingface_hub 0.16.0+","diffusers 0.21.0+","Internet connection for initial download","~7GB free disk space for weights cache"],"input_types":["model_id (string, 'stabilityai/sdxl-turbo')","revision (string, optional, default 'main')","cache_dir (string, optional, default ~/.cache/huggingface/hub/)","local_files_only (boolean, optional, for offline loading)"],"output_types":["StableDiffusionXLPipeline object with loaded weights"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-stabilityai--sdxl-turbo__cap_7","uri":"capability://image.visual.flexible.scheduler.configuration.for.noise.scheduling.and.timestep.sampling","name":"flexible scheduler configuration for noise scheduling and timestep sampling","description":"Supports multiple noise schedulers (DDPMScheduler, PNDMScheduler, EulerDiscreteScheduler, etc.) that define how noise is added during the forward diffusion process and how timesteps are sampled during inference. The scheduler controls the noise schedule (linear, cosine, or custom), timestep ordering (sequential, random, or custom), and step size. For SDXL-Turbo, the default is EulerDiscreteScheduler with a single step, but users can swap schedulers to experiment with different noise schedules or step counts. Scheduler configuration is decoupled from the model weights, enabling flexible experimentation without retraining.","intents":["Experiment with different noise schedules and timestep sampling strategies","Adjust inference speed vs quality by changing scheduler configuration","Implement custom timestep schedules for specialized applications (e.g., progressive refinement)","Debug generation quality by isolating scheduler effects from model effects"],"best_for":["Researchers experimenting with noise scheduling and diffusion theory","Developers fine-tuning inference quality vs latency tradeoffs","Teams implementing custom inference strategies (e.g., progressive refinement)"],"limitations":["Scheduler configuration is not well-documented; requires reading diffusers source code to understand all options","Changing scheduler may require retuning guidance_scale and other hyperparameters","Custom schedulers require implementing the Scheduler interface, which is not trivial","Scheduler effects are entangled with model training; a scheduler optimized for SDXL 1.0 may not work well with SDXL-Turbo","No built-in validation; invalid scheduler configurations may fail silently or produce poor results"],"requires":["diffusers 0.21.0+","PyTorch 1.13+"],"input_types":["scheduler class (e.g., EulerDiscreteScheduler, DDPMScheduler)","scheduler config (dict with num_train_timesteps, beta_schedule, etc.)"],"output_types":["configured scheduler object"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-stabilityai--sdxl-turbo__cap_8","uri":"capability://image.visual.inference.optimization.via.torch.compile.and.graph.capture","name":"inference optimization via torch.compile and graph capture","description":"Enables PyTorch 2.0+ graph compilation via torch.compile() to optimize the UNet forward pass by fusing operations, eliminating Python overhead, and generating optimized CUDA kernels. When enabled, the first inference call is slower (compilation overhead ~5-10s), but subsequent calls are 20-40% faster due to kernel fusion and reduced Python interpreter overhead. This is transparent to the user and requires only a single decorator or function call. Compatibility depends on PyTorch version and GPU architecture; not all operations are compilable.","intents":["Reduce inference latency by 20-40% via kernel fusion and Python overhead elimination","Optimize throughput for batch inference and serverless deployments","Improve energy efficiency by reducing GPU kernel launch overhead"],"best_for":["Developers optimizing inference latency for production deployments","Teams running high-throughput batch inference pipelines","Serverless deployments where compilation overhead is amortized across many requests"],"limitations":["torch.compile() requires PyTorch 2.0+, which is not yet widely adopted","Compilation overhead is 5-10s on first inference; not suitable for single-shot inference","Not all operations are compilable; some attention implementations or custom layers may fall back to eager execution","Compiled graphs are GPU-specific; recompilation is required when switching GPUs","Debugging compiled code is difficult; error messages are less informative than eager execution"],"requires":["PyTorch 2.0+","CUDA 11.8+ (for optimal performance)","GPU with compute capability 7.0+ (Volta or newer)"],"input_types":["unet (UNet2DConditionModel)","compile_mode (string, 'default', 'reduce-overhead', or 'max-autotune')"],"output_types":["compiled UNet2DConditionModel"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":49,"verified":false,"data_access_risk":"low","permissions":["Python 3.8+","PyTorch 1.13+ with CUDA 11.6+ (or CPU, but inference ~10x slower)","8GB+ GPU VRAM (RTX 3060 or equivalent) for optimal performance","diffusers library 0.21.0+","transformers library 4.25.0+ for text encoding","transformers library 4.25.0+ with CLIP model weights (~1.4GB download)","PyTorch 1.13+","CUDA 11.6+ for GPU acceleration (CPU encoding adds ~2-3s latency)","PyTorch 1.13+ with CUDA support","VAE decoder weights (~200MB) for converting latents back to pixel space"],"failure_modes":["Single-step generation trades iterative refinement for speed — image quality plateaus earlier than multi-step models like SDXL 1.0","Prompt engineering sensitivity is higher; complex multi-object scenes may require more detailed prompts than standard SDXL","No built-in support for negative prompts or guidance scaling in the base model — requires custom pipeline modifications","Fixed 512×512 output resolution; upscaling requires separate super-resolution model","Adversarial training introduces potential mode collapse on underrepresented prompt categories","CLIP tokenizer has 77-token limit; longer prompts are truncated without warning","Cross-attention is computed at 64×64 spatial resolution (downsampled from 512×512), losing fine-grained spatial precision","Prompt ambiguity (e.g., 'bank' as financial institution vs riverbank) is resolved by CLIP's training data bias, not explicit disambiguation","No native support for weighted token importance or prompt weighting syntax (requires custom pipeline code)","Latent-space compression introduces quantization artifacts; fine details (e.g., text in images, intricate patterns) are often lost","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7678596502178379,"quality":0.28,"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":"2026-05-24T12:16:22.766Z","last_scraped_at":"2026-05-03T14:22:49.651Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":895582,"model_likes":2567}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=stabilityai--sdxl-turbo","compare_url":"https://unfragile.ai/compare?artifact=stabilityai--sdxl-turbo"}},"signature":"8I7LQfkbfzaSPTi+IdoaNZwBhThO49arN457MbIROI8cyXGOm4ROTQ50Yt7uiJsq/nCTBupqREuFdAY6FjFUCQ==","signedAt":"2026-06-20T16:18:47.184Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/stabilityai--sdxl-turbo","artifact":"https://unfragile.ai/stabilityai--sdxl-turbo","verify":"https://unfragile.ai/api/v1/verify?slug=stabilityai--sdxl-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"}}