{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-space-lllyasviel--ic-light","slug":"lllyasviel--ic-light","name":"IC-Light","type":"webapp","url":"https://huggingface.co/spaces/lllyasviel/IC-Light","page_url":"https://unfragile.ai/lllyasviel--ic-light","categories":["automation"],"tags":["gradio","region:us"],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-space-lllyasviel--ic-light__cap_0","uri":"capability://image.visual.relighting.aware.image.inpainting.with.spatial.control","name":"relighting-aware image inpainting with spatial control","description":"Performs intelligent image inpainting that respects lighting conditions by using a diffusion-based approach with spatial conditioning maps. The system accepts a base image, a mask defining regions to modify, and optional lighting direction hints, then generates photorealistic inpainted content that matches the scene's illumination. This works by encoding spatial information as additional conditioning inputs to a latent diffusion model, allowing the network to understand which areas need modification and how lighting should flow across the scene.","intents":["I need to remove unwanted objects from photos while preserving realistic lighting and shadows","I want to fill in missing regions of an image with content that matches the existing lighting direction","I need to inpaint multiple areas of a photo and have them all respect the same light source"],"best_for":["photographers and image editors working with product or portrait photography","content creators needing quick object removal without manual shadow painting","developers building image editing pipelines that need lighting-aware inpainting"],"limitations":["Inpainting quality degrades with very large masked regions (>40% of image) due to diffusion model training data distribution","Lighting direction hints are approximate and may not perfectly match complex multi-light setups","Processing time scales with image resolution; 1024x1024 images take 15-30 seconds on standard GPU","No real-time preview during generation — full inference required for each iteration"],"requires":["Input image in JPEG, PNG, or WebP format","Binary mask or region selection defining inpaint area","GPU with minimum 6GB VRAM for reasonable inference speed","Modern web browser supporting WebGL for Gradio interface"],"input_types":["image (JPEG, PNG, WebP)","binary mask or region coordinates","optional lighting direction vector (x, y, z coordinates)"],"output_types":["image (PNG with alpha channel)","inpainted region with lighting-matched content"],"categories":["image-visual","generative-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-lllyasviel--ic-light__cap_1","uri":"capability://image.visual.interactive.mask.based.region.selection.and.refinement","name":"interactive mask-based region selection and refinement","description":"Provides a web-based drawing interface for users to define inpaint regions through freehand painting, polygon selection, or brush-based masking. The interface uses HTML5 Canvas for real-time mask visualization with adjustable brush size and opacity, allowing users to iteratively refine which areas of the image should be modified. The mask is converted to a binary tensor and passed to the inpainting model as a conditioning signal.","intents":["I want to precisely select which parts of an image to inpaint using a brush tool","I need to adjust my selection before running inpainting to avoid over-masking or under-masking","I want to see a preview of my mask overlaid on the image before processing"],"best_for":["non-technical users who prefer visual selection over coordinate input","iterative workflows where mask refinement is part of the creative process","mobile and tablet users needing touch-friendly selection tools"],"limitations":["Canvas-based drawing can be imprecise on high-DPI displays without proper scaling","No automatic edge detection or intelligent selection — purely manual brush-based","Mask refinement requires re-running the full inpainting pipeline; no incremental updates","Touch input on tablets may have latency issues depending on browser and device"],"requires":["Modern web browser with HTML5 Canvas support","JavaScript enabled for interactive drawing","Mouse or touch input device"],"input_types":["image (displayed in canvas)","brush strokes (x, y coordinates with pressure/opacity)"],"output_types":["binary mask tensor","visual mask overlay (semi-transparent)"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-lllyasviel--ic-light__cap_2","uri":"capability://image.visual.lighting.direction.parameter.configuration.and.preview","name":"lighting direction parameter configuration and preview","description":"Exposes lighting direction as an adjustable 3D vector (or spherical coordinates) through UI sliders or input fields, allowing users to specify the direction from which light should appear to come in the inpainted region. The system converts these parameters into a conditioning tensor that guides the diffusion model's generation process. Users can preview how different lighting angles affect the inpainting result through iterative generation.","intents":["I want to control the direction of light in the inpainted area to match the rest of the image","I need to experiment with different lighting angles to find the most natural-looking result","I want to ensure shadows and highlights in the inpainted region align with the scene's primary light source"],"best_for":["professional photographers and retouchers who understand 3D lighting concepts","product photographers needing consistent lighting across multiple inpaint operations","developers building custom image editing tools with lighting control"],"limitations":["Lighting parameters are approximate and may not perfectly match complex multi-light or HDRI-based scenes","No automatic light detection from the original image — must be manually specified","Spherical coordinate system may be unintuitive for users unfamiliar with 3D graphics","Changes to lighting parameters require full re-inference; no real-time preview"],"requires":["Understanding of 3D lighting concepts (azimuth, elevation, or x/y/z vectors)","GPU for inference to test different lighting parameters"],"input_types":["numeric parameters (azimuth angle, elevation angle, or x/y/z vector components)","range constraints (e.g., azimuth 0-360 degrees)"],"output_types":["conditioning tensor encoding lighting direction","inpainted image with lighting-matched content"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-lllyasviel--ic-light__cap_3","uri":"capability://automation.workflow.batch.image.processing.with.queued.inference","name":"batch image processing with queued inference","description":"Supports processing multiple images sequentially through a queue-based system, where users can upload several images with their corresponding masks and lighting parameters, and the system processes them in order on available GPU resources. The Gradio interface manages the queue, displaying progress for each image and allowing users to cancel or reorder jobs. This is implemented using Gradio's built-in queue system with configurable concurrency limits.","intents":["I want to process 10+ product photos with the same lighting and inpainting settings without manually submitting each one","I need to batch-process images overnight and check results in the morning without babysitting the interface","I want to apply the same inpainting operation to a series of similar images with minimal manual intervention"],"best_for":["content creators and photographers processing large photo libraries","e-commerce teams preparing product images at scale","batch processing workflows where latency is acceptable but throughput matters"],"limitations":["Queue processing is sequential by default; parallel processing requires multiple GPU instances","No persistent job storage — queue is lost if the Gradio app restarts","Large batch sizes (>100 images) may exceed available GPU memory if images are high-resolution","No progress estimation or ETA calculation; users cannot predict completion time"],"requires":["Multiple images in supported format (JPEG, PNG, WebP)","Corresponding masks for each image","GPU with sufficient VRAM for concurrent inference (if parallelized)"],"input_types":["list of images","list of masks (one per image)","shared or per-image lighting parameters"],"output_types":["list of inpainted images","processing status for each image"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-lllyasviel--ic-light__cap_4","uri":"capability://image.visual.diffusion.model.inference.with.gpu.acceleration","name":"diffusion model inference with gpu acceleration","description":"Executes the core inpainting diffusion model (likely a fine-tuned variant of Stable Diffusion or similar) on GPU hardware, performing iterative denoising steps to generate inpainted content. The system loads the model weights into VRAM, accepts conditioning inputs (mask, lighting direction), and runs the forward pass for a configurable number of diffusion steps (typically 20-50). This is implemented using PyTorch with CUDA/ROCm backends for GPU acceleration.","intents":["I need to generate photorealistic inpainted content that matches the scene's lighting and context","I want to control the number of diffusion steps to balance quality vs. speed","I need inference to complete in reasonable time (seconds, not minutes) for interactive workflows"],"best_for":["users with access to NVIDIA or AMD GPUs (CUDA or ROCm compatible)","workflows where inference latency must be <30 seconds per image","applications requiring photorealistic output quality over speed"],"limitations":["Requires GPU with minimum 6GB VRAM; CPU-only inference is impractically slow (>5 minutes per image)","Model weights are large (2-4GB); initial load time is 10-30 seconds","Inference time scales linearly with diffusion steps; doubling steps doubles latency","Memory usage spikes during inference; concurrent requests may cause OOM errors on smaller GPUs"],"requires":["NVIDIA GPU with CUDA 11.8+ or AMD GPU with ROCm 5.0+","PyTorch 2.0+ with GPU support","6GB+ VRAM for model weights and intermediate activations","Sufficient disk space for model weights (2-4GB)"],"input_types":["image tensor (3-channel RGB, normalized to [-1, 1])","binary mask tensor (same spatial dimensions as image)","lighting direction vector (3D or spherical coordinates)","diffusion step count (integer, typically 20-50)"],"output_types":["inpainted image tensor (3-channel RGB, same dimensions as input)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-lllyasviel--ic-light__cap_5","uri":"capability://tool.use.integration.web.based.interface.with.gradio.framework.integration","name":"web-based interface with gradio framework integration","description":"Provides a user-friendly web interface built with Gradio, a Python framework for rapidly prototyping ML applications. The interface includes image upload, mask drawing canvas, lighting parameter sliders, and result display, all without requiring custom HTML/CSS/JavaScript. Gradio automatically handles form submission, file I/O, and result rendering, while the backend Python code defines the processing logic. The app is deployed on HuggingFace Spaces, which provides free GPU resources and automatic scaling.","intents":["I want to use IC-Light without installing software or writing code","I need a shareable link to demo the inpainting capability to colleagues or clients","I want to experiment with different parameters through a visual interface without command-line tools"],"best_for":["non-technical users and stakeholders who need to try the tool without setup","researchers and developers prototyping ideas quickly without building custom UIs","teams sharing demos or MVPs without deploying to production infrastructure"],"limitations":["Gradio interface is less customizable than a native web app; styling options are limited","HuggingFace Spaces has rate limiting and may queue requests during high traffic","No persistent authentication or user accounts; all sessions are anonymous","File uploads are limited to ~500MB per request; batch processing is limited by session timeout"],"requires":["Web browser with JavaScript enabled","Internet connection to access HuggingFace Spaces","No local installation or dependencies required"],"input_types":["image file upload (drag-and-drop or file picker)","interactive canvas input (mouse/touch drawing)","numeric slider inputs (lighting parameters)","dropdown selections (diffusion step count, etc.)"],"output_types":["rendered image in web browser","downloadable PNG file with transparency"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":23,"verified":false,"data_access_risk":"high","permissions":["Input image in JPEG, PNG, or WebP format","Binary mask or region selection defining inpaint area","GPU with minimum 6GB VRAM for reasonable inference speed","Modern web browser supporting WebGL for Gradio interface","Modern web browser with HTML5 Canvas support","JavaScript enabled for interactive drawing","Mouse or touch input device","Understanding of 3D lighting concepts (azimuth, elevation, or x/y/z vectors)","GPU for inference to test different lighting parameters","Multiple images in supported format (JPEG, PNG, WebP)"],"failure_modes":["Inpainting quality degrades with very large masked regions (>40% of image) due to diffusion model training data distribution","Lighting direction hints are approximate and may not perfectly match complex multi-light setups","Processing time scales with image resolution; 1024x1024 images take 15-30 seconds on standard GPU","No real-time preview during generation — full inference required for each iteration","Canvas-based drawing can be imprecise on high-DPI displays without proper scaling","No automatic edge detection or intelligent selection — purely manual brush-based","Mask refinement requires re-running the full inpainting pipeline; no incremental updates","Touch input on tablets may have latency issues depending on browser and device","Lighting parameters are approximate and may not perfectly match complex multi-light or HDRI-based scenes","No automatic light detection from the original image — must be manually specified","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.22,"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=lllyasviel--ic-light","compare_url":"https://unfragile.ai/compare?artifact=lllyasviel--ic-light"}},"signature":"nFmk/GMYesvn86ap3+Z4hu3aeYl5m1YqtdDtDZ2mOjwm+imIBPRpwbUnxRWOfdO286Z4i+89ODHMCY++YpQ3Cg==","signedAt":"2026-06-21T01:42:35.510Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/lllyasviel--ic-light","artifact":"https://unfragile.ai/lllyasviel--ic-light","verify":"https://unfragile.ai/api/v1/verify?slug=lllyasviel--ic-light","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"}}