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Uses diffusion-based image generation with spatial conditioning to maintain pose fidelity and prevent garment distortion artifacts.","intents":["I want to see how a specific piece of clothing looks on a particular person without physical fitting","I need to generate product mockups showing garments on diverse body types and poses","I want to create virtual try-on experiences for e-commerce without requiring 3D models"],"best_for":["e-commerce platforms building virtual try-on features","fashion retailers testing product photography at scale","clothing brands prototyping designs on diverse models"],"limitations":["Requires clear, well-lit images of both garment and person for optimal results","May struggle with complex garment details like intricate patterns or embellishments","Performance degrades with extreme poses or occlusions","Inference latency ~10-30 seconds per image on CPU, faster on GPU"],"requires":["Input image of garment (PNG/JPG, recommended 512x768px or higher)","Input image of person/model (PNG/JPG, full-body or torso visible)","GPU access recommended for sub-5-second inference (HuggingFace Spaces provides free T4)"],"input_types":["image (garment photo)","image (person/model photo)"],"output_types":["image (synthetic try-on result)"],"categories":["image-visual","e-commerce"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-kwai-kolors--kolors-virtual-try-on__cap_1","uri":"capability://image.visual.multi.garment.composition.and.layering","name":"multi-garment composition and layering","description":"Enables sequential or simultaneous application of multiple clothing items (e.g., shirt + jacket + pants) onto a single person by managing layer ordering, occlusion handling, and ensuring visual coherence across overlapping garments. The system tracks which garments occlude others and regenerates affected regions to maintain realistic fabric interactions and shadows.","intents":["I want to show a complete outfit (top, bottom, outerwear) on a model in a single image","I need to test how different garment combinations look together before manufacturing","I want to let customers build and preview full looks by layering multiple items"],"best_for":["fashion retailers with large SKU catalogs needing outfit composition","styling apps that recommend complete looks","brands testing coordinated collections"],"limitations":["Layering more than 3-4 garments may introduce visual artifacts at occlusion boundaries","Requires careful ordering specification to avoid physically impossible configurations","Inference time scales roughly linearly with number of garments (1 garment ~10s, 3 garments ~25-30s)","May hallucinate unrealistic fabric interactions if garments have conflicting textures"],"requires":["Multiple garment images (one per clothing item)","Base person/model image","Specification of garment layer order (which items appear in front)"],"input_types":["image (person)","image array (multiple garments)"],"output_types":["image (composite outfit visualization)"],"categories":["image-visual","e-commerce"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-kwai-kolors--kolors-virtual-try-on__cap_2","uri":"capability://image.visual.pose.aware.garment.transfer.with.anatomical.adaptation","name":"pose-aware garment transfer with anatomical adaptation","description":"Automatically adapts garment fit and draping to match the target person's pose, body proportions, and posture by analyzing skeletal keypoints and body shape priors. The system deforms the garment texture in latent space according to detected pose changes, ensuring clothing appears naturally fitted rather than floating or clipping through the body.","intents":["I want to show how a garment fits when a person is sitting, standing, or in motion","I need to test clothing on diverse body types and ensure it adapts realistically to different proportions","I want to generate try-on images that match a specific pose reference provided by the customer"],"best_for":["fashion e-commerce platforms supporting dynamic pose selection","fitness/activewear brands testing clothing on athletes in motion","size-inclusive retailers demonstrating fit across body types"],"limitations":["Pose estimation accuracy depends on image clarity and body visibility (fails with heavy occlusion)","Extreme or unusual poses may produce unrealistic garment deformation","Body proportion estimation is approximate and may not perfectly match actual measurements","Requires visible full-body or at least torso+limbs for reliable pose detection"],"requires":["Person image with visible body (minimum torso, ideally full-body)","Garment image","Optional: explicit pose keypoints or reference pose image"],"input_types":["image (person with visible pose)","image (garment)"],"output_types":["image (garment adapted to person's pose)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-kwai-kolors--kolors-virtual-try-on__cap_3","uri":"capability://image.visual.background.aware.garment.rendering.with.lighting.consistency","name":"background-aware garment rendering with lighting consistency","description":"Generates garment imagery that respects the background environment and lighting conditions of the target person's photo, ensuring shadows, reflections, and color temperature match the scene. The system analyzes ambient lighting direction and intensity, then conditions the garment generation to produce shadows and highlights consistent with detected light sources.","intents":["I want try-on images to look photorealistic by matching the lighting of the original person photo","I need to preserve the background context while inserting a garment so the result looks like a single coherent photo","I want to avoid the 'pasted' look where the garment appears to be from a different photo shoot"],"best_for":["premium e-commerce platforms prioritizing photorealism","fashion brands creating catalog imagery with consistent lighting","social commerce platforms where realism drives conversion"],"limitations":["Lighting estimation is approximate and may fail with complex multi-source lighting","Highly textured or patterned backgrounds may confuse the model and produce artifacts","Extreme lighting conditions (very dark, very bright, colored gels) may not be accurately matched","Background preservation is not perfect; may introduce subtle color shifts at garment boundaries"],"requires":["Person image with visible lighting context (background and shadows visible)","Garment image","Ideally: images taken in consistent lighting conditions"],"input_types":["image (person with background and lighting context)","image (garment)"],"output_types":["image (garment rendered with matched lighting)"],"categories":["image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-kwai-kolors--kolors-virtual-try-on__cap_4","uri":"capability://automation.workflow.batch.virtual.try.on.processing.with.api.integration","name":"batch virtual try-on processing with api integration","description":"Provides a Gradio-based web interface and underlying API that accepts batch requests for virtual try-on generation, enabling integration with e-commerce platforms and inventory management systems. Supports queuing, progress tracking, and asynchronous processing to handle multiple try-on requests without blocking.","intents":["I want to integrate virtual try-on into my e-commerce platform's product pages","I need to generate try-on images for thousands of product SKUs across multiple models","I want to offer customers a real-time try-on tool without building the ML infrastructure myself"],"best_for":["e-commerce developers integrating try-on as a feature","fashion retailers with large catalogs needing batch processing","third-party platforms building try-on as a service"],"limitations":["HuggingFace Spaces free tier has CPU-only inference (slow) or limited GPU hours","No persistent storage; results are temporary unless explicitly downloaded","Rate limiting on free tier may cause queuing for high-traffic scenarios","Batch processing is sequential on free tier, not parallel"],"requires":["Internet connection to access HuggingFace Spaces","Garment and person images in supported formats (PNG, JPG)","Optional: API key for programmatic access (if available)"],"input_types":["image (garment)","image (person)"],"output_types":["image (try-on result)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-kwai-kolors--kolors-virtual-try-on__cap_5","uri":"capability://image.visual.garment.segmentation.and.region.specific.synthesis","name":"garment segmentation and region-specific synthesis","description":"Automatically identifies and isolates different regions of the garment (sleeves, collar, main body, buttons, etc.) and synthesizes each region independently before compositing, allowing fine-grained control over which parts are modified. Uses semantic segmentation masks to ensure only relevant garment regions are regenerated when adapting to a new person.","intents":["I want to preserve specific garment details (like logos or embroidery) while adapting the fit","I need to ensure buttons, zippers, and other hardware are rendered correctly on the target person","I want to selectively modify only certain parts of a garment (e.g., sleeve length) while keeping others unchanged"],"best_for":["brands with detailed garments requiring precise detail preservation","retailers offering customization options (e.g., sleeve length, collar style)","quality-focused e-commerce platforms where detail accuracy matters"],"limitations":["Segmentation accuracy depends on garment complexity; intricate designs may be misclassified","Region-specific synthesis adds computational overhead (~20-30% slower than full-image synthesis)","Boundary artifacts may appear where regions are composited together","Requires training data with detailed garment annotations for optimal performance"],"requires":["Garment image with clear, distinct regions","Person image","Optional: pre-computed segmentation masks for faster processing"],"input_types":["image (garment)","image (person)"],"output_types":["image (try-on with preserved garment details)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-kwai-kolors--kolors-virtual-try-on__cap_6","uri":"capability://data.processing.analysis.size.and.fit.prediction.with.body.measurement.inference","name":"size and fit prediction with body measurement inference","description":"Estimates the target person's body measurements (chest, waist, hip, inseam, etc.) from their image by analyzing silhouette and proportions, then uses these measurements to predict how a garment will fit. Provides feedback on whether the garment will be too loose, too tight, or well-fitted based on the person's estimated size and the garment's known dimensions.","intents":["I want to predict whether a specific size will fit a customer based on their photo","I need to recommend the correct size to a customer before they purchase","I want to show customers how a garment will fit (tight, loose, perfect) without them trying it on"],"best_for":["e-commerce platforms offering size recommendations","retailers reducing return rates by predicting fit before purchase","size-inclusive brands helping customers find their size"],"limitations":["Body measurement estimation is approximate (±5-10% error typical) and depends on image quality and pose","Requires knowledge of garment dimensions (which may not be available for all products)","Does not account for fabric stretch, personal fit preferences, or styling choices","Accuracy varies significantly across body types and clothing categories","Requires full-body or at least torso+limbs visibility for reliable measurement"],"requires":["Person image with visible body (full-body preferred)","Garment specifications (size chart, known dimensions)","Optional: historical fit data for the brand/garment type"],"input_types":["image (person)"],"output_types":["structured data (estimated measurements, fit prediction)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-kwai-kolors--kolors-virtual-try-on__cap_7","uri":"capability://image.visual.model.diversity.and.representation.with.body.type.adaptation","name":"model diversity and representation with body type adaptation","description":"Supports virtual try-on across diverse body types, sizes, and skin tones by training on inclusive datasets and using body-type-aware conditioning in the diffusion model. Ensures garments are rendered realistically on different body shapes without artifacts or bias, and adapts garment fit proportionally to match each body type's unique proportions.","intents":["I want to show customers how garments look on body types similar to theirs","I need to ensure my try-on system works fairly across all body sizes and skin tones","I want to build trust with customers by showing realistic representations of diverse bodies"],"best_for":["inclusive fashion retailers committed to size diversity","brands addressing representation gaps in e-commerce","platforms serving global markets with diverse customer bases"],"limitations":["Model quality may vary across underrepresented body types if training data is imbalanced","Extreme sizes (very small or very large) may have lower synthesis quality","Skin tone representation depends on training data diversity; potential for bias if not carefully curated","Requires larger, more diverse training datasets than single-body-type models"],"requires":["Person image of any body type, size, or skin tone","Garment image","Training data representing diverse body types (for model development)"],"input_types":["image (person of any body type)"],"output_types":["image (garment adapted to person's body type)"],"categories":["image-visual","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"low","permissions":["Input image of garment (PNG/JPG, recommended 512x768px or higher)","Input image of person/model (PNG/JPG, full-body or torso visible)","GPU access recommended for sub-5-second inference (HuggingFace Spaces provides free T4)","Multiple garment images (one per clothing item)","Base person/model image","Specification of garment layer order (which items appear in front)","Person image with visible body (minimum torso, ideally full-body)","Garment image","Optional: explicit pose keypoints or reference pose image","Person image with visible lighting context (background and shadows visible)"],"failure_modes":["Requires clear, well-lit images of both garment and person for optimal results","May struggle with complex garment details like intricate patterns or embellishments","Performance degrades with extreme poses or occlusions","Inference latency ~10-30 seconds per image on CPU, faster on GPU","Layering more than 3-4 garments may introduce visual artifacts at occlusion boundaries","Requires careful ordering specification to avoid physically impossible configurations","Inference time scales roughly linearly with number of garments (1 garment ~10s, 3 garments ~25-30s)","May hallucinate unrealistic fabric interactions if garments have conflicting textures","Pose estimation accuracy depends on image clarity and body visibility (fails with heavy occlusion)","Extreme or unusual poses may produce unrealistic garment deformation","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.26,"ecosystem":0.36,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"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":"2026-05-03T14:22:48.012Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=kwai-kolors--kolors-virtual-try-on","compare_url":"https://unfragile.ai/compare?artifact=kwai-kolors--kolors-virtual-try-on"}},"signature":"wZD8K3ulZCMVH8O9g70l2Bow83ucLitrahRrC7jpxj/po+vpv8v08PYXxFJ+waFjpAa4mx5DVc/Hjc3NYQQJCw==","signedAt":"2026-06-20T07:29:00.695Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/kwai-kolors--kolors-virtual-try-on","artifact":"https://unfragile.ai/kwai-kolors--kolors-virtual-try-on","verify":"https://unfragile.ai/api/v1/verify?slug=kwai-kolors--kolors-virtual-try-on","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"}}