{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-martineux--dvine82-xl","slug":"martineux--dvine82-xl","name":"dvine82-xl","type":"model","url":"https://huggingface.co/martineux/dvine82-xl","page_url":"https://unfragile.ai/martineux--dvine82-xl","categories":["image-generation"],"tags":["diffusers","safetensors","endpoints_compatible","diffusers:StableDiffusionXLPipeline","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-martineux--dvine82-xl__cap_0","uri":"capability://image.visual.text.to.image.generation.via.diffusion.based.synthesis","name":"text-to-image generation via diffusion-based synthesis","description":"Generates photorealistic images from natural language text prompts using a latent diffusion architecture built on the Stable Diffusion XL foundation. The model operates by iteratively denoising a random latent vector conditioned on CLIP text embeddings, progressively refining image details across 20-50 sampling steps. Uses a pre-trained text encoder to convert prompts into high-dimensional semantic embeddings that guide the diffusion process toward user-specified visual concepts.","intents":["Generate high-quality product mockups and marketing imagery from text descriptions without hiring photographers","Create concept art and visual prototypes rapidly during design ideation phases","Produce diverse image variations from a single prompt for A/B testing creative directions","Batch-generate training datasets for computer vision models with programmatic prompt variation"],"best_for":["indie game developers and digital artists prototyping visual assets","marketing teams generating on-demand product photography and promotional content","ML engineers building synthetic training datasets with controlled diversity","design studios exploring multiple creative directions at scale"],"limitations":["Inference latency of 15-45 seconds per image on consumer GPUs (RTX 3080), longer on CPU-only systems","Memory footprint of ~7-9GB VRAM required for full model; quantization reduces to ~4GB but increases latency by 20-30%","Text prompt understanding limited to ~77 tokens; longer descriptions are truncated, losing semantic nuance","Struggles with precise text rendering, complex spatial relationships, and anatomically correct hands/fingers in generated images","No built-in image editing or inpainting — generates full images only, requires separate tools for selective modifications","Deterministic output requires fixed random seed; stochastic sampling produces different results each run without seed control"],"requires":["Python 3.8+","PyTorch 1.13+ with CUDA 11.8+ (for GPU acceleration) or CPU fallback (significantly slower)","Diffusers library 0.21.0+","Minimum 6GB VRAM for inference, 16GB+ recommended for batch processing","HuggingFace Hub API token for model weight download (free tier sufficient)","~13GB disk space for full model weights in safetensors format"],"input_types":["text (natural language prompts, 1-77 tokens)","optional: negative prompts (text describing unwanted visual elements)","optional: guidance scale parameter (float 7.0-15.0, controls prompt adherence)","optional: random seed (integer, for reproducible outputs)"],"output_types":["PIL Image objects (in-memory)","PNG/JPEG files (disk-persisted)","NumPy arrays (for downstream processing)","Batch outputs as image grids or individual files"],"categories":["image-visual","generative-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-martineux--dvine82-xl__cap_1","uri":"capability://image.visual.prompt.conditioned.image.generation.with.negative.prompt.guidance","name":"prompt-conditioned image generation with negative prompt guidance","description":"Extends core text-to-image by accepting both positive prompts (desired visual elements) and negative prompts (elements to exclude) simultaneously, using classifier-free guidance to weight the model's attention toward positive conditioning while away from negative conditioning. Implements dual-path denoising where the model predicts noise reduction for three conditions: unconditional, positive-conditioned, and negative-conditioned, then interpolates predictions using guidance scale weights to produce final denoising direction.","intents":["Exclude unwanted visual artifacts (e.g., 'no blurry faces, no watermarks') to improve output quality without trial-and-error","Enforce style constraints (e.g., 'no photorealism, only oil painting') by combining positive and negative prompts","Reduce hallucinations of common failure modes (e.g., 'no extra limbs, no distorted text') in generated images"],"best_for":["content creators iterating on visual concepts with specific exclusion criteria","teams generating branded content where certain visual elements must be avoided"],"limitations":["Negative prompts add 33-50% latency overhead due to additional forward passes through the diffusion model","Guidance scale tuning is empirical; values >15 often produce oversaturated, unrealistic images; <7 ignores prompts entirely","Negative prompts less effective than positive ones; model prioritizes positive conditioning, making negative guidance a weak signal"],"requires":["Python 3.8+","Diffusers library 0.21.0+ with classifier-free guidance support","Same GPU/memory requirements as base text-to-image capability"],"input_types":["text (positive prompt, 1-77 tokens)","text (negative prompt, 1-77 tokens)","float (guidance_scale, typical range 7.0-15.0)"],"output_types":["PIL Image objects","PNG/JPEG files"],"categories":["image-visual","generative-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-martineux--dvine82-xl__cap_2","uri":"capability://image.visual.batch.image.generation.with.prompt.variation","name":"batch image generation with prompt variation","description":"Generates multiple images in sequence from a single prompt or a list of prompts, leveraging the Diffusers pipeline's batching infrastructure to amortize model loading overhead and enable efficient GPU utilization across multiple generations. Supports programmatic prompt templating (e.g., 'a {color} {object} in {style}') to generate diverse variations by substituting template variables, useful for synthetic dataset creation and A/B testing.","intents":["Generate 10-100 image variations from a single base prompt for dataset augmentation or creative exploration","Create product mockups in multiple colors/styles programmatically without manual prompt editing","Batch-process a CSV of prompts into corresponding images for large-scale content generation"],"best_for":["ML engineers building synthetic training datasets with controlled prompt variation","e-commerce platforms generating product images in multiple variants","design studios exploring creative directions at scale"],"limitations":["Batch size limited by available VRAM; typical max 4-8 images per batch on 8GB GPUs before OOM errors","No built-in progress tracking or error recovery; failed generations in a batch require manual retry logic","Prompt templating is manual; no automatic prompt optimization or diversity sampling"],"requires":["Python 3.8+","Diffusers library 0.21.0+","8GB+ VRAM for batch size >2; 16GB+ recommended for batch size 4-8","Optional: pandas for CSV-based prompt loading"],"input_types":["list of text prompts (each 1-77 tokens)","optional: template string with {variable} placeholders","optional: list of variable substitutions (e.g., colors, styles)"],"output_types":["list of PIL Image objects","directory of PNG/JPEG files (one per prompt)","optional: CSV mapping prompts to output filenames"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-martineux--dvine82-xl__cap_3","uri":"capability://safety.moderation.safetensors.based.model.weight.loading.with.security.validation","name":"safetensors-based model weight loading with security validation","description":"Loads model weights from safetensors format (a secure, human-readable serialization standard) instead of pickle, preventing arbitrary code execution vulnerabilities during deserialization. The Diffusers library automatically detects safetensors files and uses a memory-safe deserializer that validates tensor shapes and dtypes before loading, ensuring weights match expected model architecture. Supports streaming weight loading from HuggingFace Hub, downloading only required tensors for inference without materializing the full 13GB model in memory.","intents":["Load model weights safely without risk of pickle-based code injection attacks","Reduce model download time by streaming only inference-required tensors from HuggingFace Hub","Verify model integrity via safetensors' built-in checksum validation"],"best_for":["security-conscious teams deploying models in production environments","developers with limited bandwidth or storage, needing efficient weight loading","organizations with strict supply-chain security requirements"],"limitations":["Safetensors format is read-only; fine-tuning or weight modification requires conversion back to PyTorch format","Streaming loading adds 5-10% latency overhead vs pre-downloaded weights due to network I/O","No built-in compression; safetensors files are same size as original PyTorch checkpoints (~13GB)"],"requires":["Python 3.8+","Diffusers library 0.21.0+","safetensors library 0.3.0+","HuggingFace Hub API token (free tier sufficient)","Network connectivity for streaming from HuggingFace Hub"],"input_types":["model identifier string (e.g., 'martineux/dvine82-xl')","optional: local path to safetensors file"],"output_types":["loaded PyTorch model state dict in GPU/CPU memory"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-martineux--dvine82-xl__cap_4","uri":"capability://automation.workflow.inference.optimization.via.mixed.precision.computation","name":"inference optimization via mixed-precision computation","description":"Automatically executes diffusion denoising steps using mixed-precision arithmetic (float16 for most operations, float32 for numerically sensitive steps) to reduce memory footprint by ~50% and increase throughput by 20-40% vs full float32 inference. The Diffusers pipeline detects GPU capabilities and automatically selects optimal precision; developers can explicitly enable via `pipe.enable_attention_slicing()` or `pipe.to('cuda:0', dtype=torch.float16)` for fine-grained control.","intents":["Generate images on consumer GPUs (RTX 3060, 4GB VRAM) that would otherwise require RTX 3080+ with float32","Reduce inference latency from 30s to 18-22s per image on high-end GPUs for faster iteration","Lower power consumption and cooling requirements for large-scale batch generation"],"best_for":["indie developers and researchers with limited GPU budgets","production services requiring sub-30s latency for user-facing image generation","edge deployments on mobile or embedded GPUs with <8GB VRAM"],"limitations":["Mixed precision introduces ~1-2% quality degradation in fine details (barely perceptible to human eye)","Requires GPU with native float16 support (NVIDIA Ampere/Ada, AMD RDNA2+); older GPUs fall back to slower emulation","Attention slicing (alternative optimization) reduces memory but adds 10-15% latency overhead vs mixed precision","Some edge cases (e.g., very long prompts) may produce slightly different outputs due to numerical precision differences"],"requires":["Python 3.8+","PyTorch 1.13+ with CUDA 11.8+","NVIDIA GPU with compute capability 7.0+ (Volta) for native float16; RTX 2060+ recommended","Diffusers library 0.21.0+"],"input_types":["dtype parameter (torch.float16 or torch.float32)","optional: enable_attention_slicing boolean flag"],"output_types":["PIL Image objects (identical visual output regardless of precision)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-martineux--dvine82-xl__cap_5","uri":"capability://image.visual.lora.based.model.fine.tuning.and.style.transfer","name":"lora-based model fine-tuning and style transfer","description":"Supports loading Low-Rank Adaptation (LoRA) weights that modify the base SDXL model's behavior without replacing full weights, enabling style transfer, subject-specific generation, or domain adaptation with minimal computational overhead. LoRA weights are typically 10-100MB (vs 13GB for full model), loaded via `load_lora_weights()` in Diffusers, and merged into the base model's attention layers to steer generation toward learned styles or subjects. Multiple LoRAs can be composed sequentially, allowing fine-grained control over output aesthetics.","intents":["Generate images in a specific artistic style (e.g., 'oil painting', 'anime', 'cyberpunk') by loading a pre-trained LoRA","Fine-tune the model on custom datasets (e.g., product photos, character designs) with <1 hour training on consumer GPUs","Combine multiple LoRAs to blend styles (e.g., 'anime + oil painting + cyberpunk') for novel aesthetic combinations"],"best_for":["artists and designers wanting consistent style across generated images","e-commerce platforms fine-tuning models on product catalogs for brand-consistent imagery","indie game developers creating game-specific visual assets with custom LoRAs"],"limitations":["LoRA composition is sequential; loading 3+ LoRAs adds 5-10% latency per additional LoRA","Fine-tuning requires 500-1000 high-quality training images for good results; smaller datasets overfit","LoRA weights are model-specific; a LoRA trained for SDXL v1.0 may not work with dvine82-xl without retraining","No built-in LoRA discovery or marketplace integration; requires manual sourcing from HuggingFace Hub or community sites"],"requires":["Python 3.8+","Diffusers library 0.21.0+ with LoRA support","PyTorch 1.13+","For fine-tuning: 8GB+ VRAM, 500-1000 training images, 1-4 hours training time","For inference: LoRA weights file (10-100MB) from HuggingFace Hub or local disk"],"input_types":["LoRA model identifier or local path (e.g., 'civitai/anime-style-lora')","optional: LoRA weight scale (float 0.0-1.0, controls LoRA influence)","optional: list of LoRA identifiers for composition"],"output_types":["PIL Image objects (styled according to loaded LoRA)","fine-tuning outputs: trained LoRA weights file (safetensors format)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-martineux--dvine82-xl__cap_6","uri":"capability://image.visual.image.to.image.generation.with.structural.guidance","name":"image-to-image generation with structural guidance","description":"Extends text-to-image by accepting an input image and generating variations that preserve the input's composition, structure, or style while respecting text prompts. Implements this via latent space injection: the input image is encoded into latent space, then diffusion begins from a noisy version of that latent (controlled by `strength` parameter, 0.0-1.0) rather than pure noise, biasing generation toward the input's structure. Enables use cases like style transfer, composition-preserving editing, and image-to-image translation.","intents":["Apply a new style to an existing image (e.g., 'convert this photo to oil painting') while preserving composition","Generate variations of a design (e.g., 'same layout, different color scheme') without manual editing","Translate images across domains (e.g., 'convert sketch to photorealistic rendering') with structural guidance"],"best_for":["designers iterating on compositions without starting from scratch","e-commerce platforms generating product image variations from a single photo","artists exploring style variations on existing artwork"],"limitations":["Requires input image in specific format (PIL Image, 512x512 or 768x768 pixels); resizing may distort composition","Strength parameter tuning is empirical; 0.3-0.5 preserves structure but ignores prompts; 0.7-0.9 heavily modifies structure","Latent space injection adds ~10% latency vs pure text-to-image due to image encoding step","Cannot add new objects to images; primarily modifies existing elements' appearance and style"],"requires":["Python 3.8+","Diffusers library 0.21.0+ with StableDiffusionImg2ImgPipeline","PIL Image library for image loading/resizing","Input image (PNG/JPEG, 512x512 or 768x768 pixels recommended)"],"input_types":["PIL Image object (input image to modify)","text prompt (1-77 tokens, describing desired modifications)","float (strength, 0.0-1.0, controls structural preservation)"],"output_types":["PIL Image object (modified image, same dimensions as input)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-martineux--dvine82-xl__cap_7","uri":"capability://image.visual.inpainting.with.mask.guided.selective.editing","name":"inpainting with mask-guided selective editing","description":"Generates content within a masked region of an image while preserving unmasked areas, enabling selective editing without affecting the entire image. Implements this by encoding the input image and mask into latent space, then running diffusion only on masked regions while keeping unmasked latents fixed. Requires a binary mask (white = edit region, black = preserve region) and a text prompt describing desired content for the masked area.","intents":["Remove unwanted objects from images (e.g., 'remove the person, fill with background') by masking and inpainting","Add new objects to specific regions (e.g., 'add a vase on the table') without modifying surroundings","Fix image defects (e.g., 'fix the blurry face') by masking and regenerating with a corrective prompt"],"best_for":["photo editors and designers doing selective image modifications","e-commerce platforms removing backgrounds or adding product variations","content creators fixing image defects without full re-generation"],"limitations":["Requires manual mask creation (binary image); no automatic object detection or segmentation","Mask boundaries often show visible seams or artifacts; requires feathering or post-processing for seamless blending","Inpainting quality degrades with large masked regions (>50% of image); small targeted edits work best","Prompt specificity critical; vague prompts produce inconsistent inpainting results","Adds ~15-20% latency vs text-to-image due to mask encoding and selective latent diffusion"],"requires":["Python 3.8+","Diffusers library 0.21.0+ with StableDiffusionInpaintPipeline","PIL Image library for image/mask loading","Input image (PNG/JPEG, 512x512 or 768x768 pixels)","Binary mask image (same dimensions as input, white = edit region)"],"input_types":["PIL Image object (input image)","PIL Image object (binary mask, white = edit region, black = preserve)","text prompt (1-77 tokens, describing content for masked region)"],"output_types":["PIL Image object (edited image, same dimensions as input)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-martineux--dvine82-xl__cap_8","uri":"capability://tool.use.integration.api.compatible.inference.endpoints.for.cloud.deployment","name":"api-compatible inference endpoints for cloud deployment","description":"Model is compatible with HuggingFace Inference Endpoints, enabling serverless deployment without managing infrastructure. Developers can deploy dvine82-xl as a managed endpoint that scales automatically based on traffic, with built-in authentication, rate limiting, and monitoring. Endpoints expose a REST API matching the Diffusers pipeline interface, allowing client code to call image generation via HTTP POST requests without local GPU requirements.","intents":["Deploy image generation as a scalable web service without managing Kubernetes or GPU infrastructure","Expose image generation API to web/mobile clients without exposing model weights or local GPU","Monitor inference metrics (latency, throughput, errors) via HuggingFace dashboard"],"best_for":["startups and small teams building image generation features without DevOps expertise","web applications requiring on-demand image generation without local GPU","teams needing automatic scaling based on traffic without manual capacity planning"],"limitations":["Inference latency includes network round-trip time (~50-200ms) plus server-side generation (15-45s), totaling 15-50s per image","Pricing is per-inference-hour (not per-image), making large batch jobs expensive vs local inference","Cold start latency (first request after idle period) can be 30-60s due to model loading","API rate limits and quotas may restrict throughput for high-volume applications","Data sent to HuggingFace servers; not suitable for privacy-sensitive applications"],"requires":["HuggingFace account with Inference Endpoints enabled","API token for authentication","HTTP client library (requests, curl, etc.)","Paid HuggingFace Inference Endpoints subscription (free tier has limited quotas)"],"input_types":["JSON payload with 'prompt' (text), 'negative_prompt' (text), 'guidance_scale' (float), 'num_inference_steps' (int)"],"output_types":["JSON response with base64-encoded image or image URL"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-martineux--dvine82-xl__cap_9","uri":"capability://automation.workflow.deterministic.image.generation.with.seed.control","name":"deterministic image generation with seed control","description":"Enables reproducible image generation by accepting a random seed parameter that controls the initial noise vector and all stochastic operations during diffusion. Setting the same seed produces identical images across runs, enabling version control of generated content and debugging of generation issues. Implemented via PyTorch's random number generator seeding at the start of the diffusion process.","intents":["Generate reproducible images for testing and debugging generation quality","Version-control generated images by storing seed values instead of image files","Enable A/B testing by generating two images with identical prompts but different seeds"],"best_for":["ML engineers debugging generation issues and validating model changes","teams needing reproducible outputs for quality assurance and regression testing","researchers comparing generation quality across model variants"],"limitations":["Seed reproducibility is GPU-specific; same seed on different GPU models (RTX 3080 vs A100) may produce slightly different images due to floating-point precision differences","Seed reproducibility breaks across PyTorch versions due to RNG implementation changes","No seed discovery or optimization; finding a 'good' seed requires trial-and-error or brute-force search"],"requires":["Python 3.8+","PyTorch 1.13+","Diffusers library 0.21.0+","Consistent GPU model and PyTorch version across runs"],"input_types":["integer (seed value, 0-2^32-1)"],"output_types":["PIL Image object (deterministic output for given seed)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","PyTorch 1.13+ with CUDA 11.8+ (for GPU acceleration) or CPU fallback (significantly slower)","Diffusers library 0.21.0+","Minimum 6GB VRAM for inference, 16GB+ recommended for batch processing","HuggingFace Hub API token for model weight download (free tier sufficient)","~13GB disk space for full model weights in safetensors format","Diffusers library 0.21.0+ with classifier-free guidance support","Same GPU/memory requirements as base text-to-image capability","8GB+ VRAM for batch size >2; 16GB+ recommended for batch size 4-8","Optional: pandas for CSV-based prompt loading"],"failure_modes":["Inference latency of 15-45 seconds per image on consumer GPUs (RTX 3080), longer on CPU-only systems","Memory footprint of ~7-9GB VRAM required for full model; quantization reduces to ~4GB but increases latency by 20-30%","Text prompt understanding limited to ~77 tokens; longer descriptions are truncated, losing semantic nuance","Struggles with precise text rendering, complex spatial relationships, and anatomically correct hands/fingers in generated images","No built-in image editing or inpainting — generates full images only, requires separate tools for selective modifications","Deterministic output requires fixed random seed; stochastic sampling produces different results each run without seed control","Negative prompts add 33-50% latency overhead due to additional forward passes through the diffusion model","Guidance scale tuning is empirical; values >15 often produce oversaturated, unrealistic images; <7 ignores prompts entirely","Negative prompts less effective than positive ones; model prioritizes positive conditioning, making negative guidance a weak signal","Batch size limited by available VRAM; typical max 4-8 images per batch on 8GB GPUs before OOM errors","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.5520716367229133,"quality":0.3,"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":"2026-05-24T12:16:22.765Z","last_scraped_at":"2026-05-03T14:22:49.651Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":282129,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=martineux--dvine82-xl","compare_url":"https://unfragile.ai/compare?artifact=martineux--dvine82-xl"}},"signature":"wqWiVZRQ6Ejo61PTh7+dVoE0hzfauT0qiSE2eKls+7RgzAup309cwtO8kJU/avqzpaBM4I4V3QEzDOWpL/sYAg==","signedAt":"2026-06-20T02:11:37.245Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/martineux--dvine82-xl","artifact":"https://unfragile.ai/martineux--dvine82-xl","verify":"https://unfragile.ai/api/v1/verify?slug=martineux--dvine82-xl","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"}}