{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-space-sczhou--codeformer","slug":"sczhou--codeformer","name":"CodeFormer","type":"webapp","url":"https://huggingface.co/spaces/sczhou/CodeFormer","page_url":"https://unfragile.ai/sczhou--codeformer","categories":["automation"],"tags":["gradio","region:us"],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-space-sczhou--codeformer__cap_0","uri":"capability://image.visual.blind.face.restoration.with.generative.priors","name":"blind face restoration with generative priors","description":"Restores degraded or low-quality facial images using a transformer-based architecture with codebook-based generative priors. The system decomposes restoration into content tokens (structural information) and quality tokens (texture/detail), enabling recovery of fine facial features from heavily compressed, blurry, or artifact-laden inputs. Uses a multi-scale feature extraction pipeline with cross-attention mechanisms to align degraded input features with learned high-quality facial priors stored in a learned codebook.","intents":["restore old, blurry, or heavily compressed photographs of faces","enhance low-resolution facial images for identification or archival purposes","remove compression artifacts and noise from webcam or surveillance footage","upscale and denoise facial regions in batch image processing workflows"],"best_for":["photo restoration professionals and archivists","developers building image enhancement pipelines","researchers evaluating generative face restoration methods","teams processing legacy or degraded facial image datasets"],"limitations":["Restoration quality degrades significantly for faces smaller than 64x64 pixels or with extreme pose variations >45 degrees","No built-in batch processing — processes one image at a time through the web interface","Inference latency ~2-5 seconds per image on CPU, requires GPU for real-time performance","May introduce hallucinated facial details inconsistent with original image intent in heavily degraded inputs","Limited to frontal or near-frontal face orientations; side profiles and extreme angles produce artifacts"],"requires":["Input image with detectable face (minimum 32x32 pixels recommended)","Modern web browser with WebGL support for Gradio interface","GPU access recommended (NVIDIA CUDA 11.0+ or equivalent) for sub-second inference"],"input_types":["image/jpeg","image/png","image/webp","image/bmp"],"output_types":["image/png (restored face image)","image/jpeg (optional compressed output)"],"categories":["image-visual","generative-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-sczhou--codeformer__cap_1","uri":"capability://image.visual.multi.scale.facial.feature.extraction.and.alignment","name":"multi-scale facial feature extraction and alignment","description":"Extracts hierarchical facial features from degraded input images at multiple scales (coarse structure → fine details) and aligns them with learned high-quality facial priors through cross-attention mechanisms. The architecture uses progressive feature refinement, where coarse features guide fine-grained restoration, preventing misalignment and structural distortion. Implements spatial attention to focus restoration effort on facial regions (eyes, mouth, nose) most sensitive to quality degradation.","intents":["ensure restored faces maintain consistent identity and structural integrity","prioritize restoration of perceptually important facial regions (eyes, mouth)","handle images with non-uniform degradation (e.g., blur in one region, compression artifacts in another)","enable fine-grained control over restoration intensity per facial region"],"best_for":["developers building identity-preserving image enhancement tools","researchers studying facial feature alignment in generative models","applications requiring high-fidelity face restoration (forensics, archival)"],"limitations":["Alignment quality depends on face detection accuracy — fails silently if face detector misses or misaligns the face region","Multi-scale processing adds computational overhead; no option to disable for faster inference on high-quality inputs","Cross-attention mechanism requires sufficient GPU memory; batch processing of large images may cause OOM errors","Spatial attention maps are not exposed to users — no visibility into which regions are prioritized"],"requires":["Face detection model (built-in, requires ~50MB VRAM)","GPU with minimum 2GB VRAM for multi-scale feature extraction","Input image with clearly visible facial region (minimum 64x64 pixels)"],"input_types":["image/jpeg","image/png"],"output_types":["image/png (restored image with aligned features)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-sczhou--codeformer__cap_2","uri":"capability://image.visual.codebook.based.generative.prior.lookup.and.synthesis","name":"codebook-based generative prior lookup and synthesis","description":"Maintains a learned codebook of high-quality facial feature representations (discrete latent codes) trained on clean facial image distributions. During restoration, degraded input features are mapped to nearest codebook entries, and high-quality features are synthesized by interpolating or selecting from the codebook. This approach constrains the restoration to plausible facial variations, preventing hallucination of unrealistic features. The codebook is trained via vector quantization, enabling discrete latent space search.","intents":["constrain restoration to realistic facial variations learned from high-quality data","prevent hallucination of non-facial artifacts or unrealistic features","enable fast inference through codebook lookup instead of iterative refinement","provide interpretability by examining which codebook entries are selected for each image"],"best_for":["applications requiring high-confidence facial restoration without hallucination","researchers studying discrete latent representations in generative models","teams building identity-critical systems (forensics, verification)"],"limitations":["Codebook size is fixed at training time; cannot adapt to new facial variations without retraining","Codebook entries are not human-interpretable — no way to inspect or modify learned priors","Quantization to discrete codes may lose fine-grained details present in continuous latent spaces","Codebook collapse risk during training — some codes may be unused, reducing effective capacity","No mechanism to weight or blend multiple codebook entries; selection is deterministic"],"requires":["Pre-trained codebook (included in model weights, ~100MB)","Vector quantization layer in model architecture","GPU for fast codebook lookup (CPU inference is feasible but slow)"],"input_types":["degraded facial image features (internal representation)"],"output_types":["high-quality facial features (synthesized from codebook)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-sczhou--codeformer__cap_3","uri":"capability://automation.workflow.web.based.interactive.restoration.interface.with.real.time.preview","name":"web-based interactive restoration interface with real-time preview","description":"Provides a Gradio-based web interface for uploading degraded facial images and viewing restoration results in real-time. The interface handles image upload, preprocessing (face detection, alignment), model inference, and side-by-side comparison visualization. Gradio manages HTTP request/response handling, file storage, and browser rendering without requiring local installation. The interface includes sliders or toggles for controlling restoration intensity or quality parameters.","intents":["restore facial images without installing software or managing dependencies","compare original and restored images side-by-side in a browser","batch process multiple images through a simple web form","share restoration results via shareable links or downloads"],"best_for":["non-technical users wanting to restore photos without coding","teams prototyping facial restoration workflows","researchers demonstrating model capabilities to stakeholders"],"limitations":["No persistent storage — uploaded images are deleted after session ends","Single-image processing only; no batch API for programmatic access","Inference latency visible to users (2-5 seconds) may feel slow for interactive workflows","File upload size limited by HuggingFace Spaces (typically 50MB per file)","No authentication or access control — public demo accessible to anyone","Cannot customize model parameters (e.g., restoration strength) through UI"],"requires":["Modern web browser (Chrome, Firefox, Safari, Edge)","Internet connection to access HuggingFace Spaces","Image file in supported format (JPEG, PNG, WebP, BMP)"],"input_types":["image/jpeg","image/png","image/webp","image/bmp"],"output_types":["image/png (downloadable restored image)","image/jpeg (optional compressed format)"],"categories":["automation-workflow","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-sczhou--codeformer__cap_4","uri":"capability://image.visual.automatic.face.detection.and.region.of.interest.extraction","name":"automatic face detection and region-of-interest extraction","description":"Detects facial regions in input images using a pre-trained face detector (likely MTCNN, RetinaFace, or similar), extracts bounding boxes, and crops/aligns the face region for restoration. The detector handles multiple faces, extreme poses, and occlusions with configurable confidence thresholds. Extracted face regions are normalized (resized, centered) before feeding to the restoration model, ensuring consistent input dimensions and reducing computational overhead.","intents":["automatically identify and isolate facial regions from complex images","handle images with multiple faces or non-facial content","normalize face orientation and size for consistent restoration quality","reduce processing overhead by focusing restoration on face regions only"],"best_for":["batch processing workflows with diverse image compositions","applications requiring automatic face localization without manual annotation","teams building end-to-end image enhancement pipelines"],"limitations":["Face detection fails on extreme poses (>45 degrees), severe occlusions, or very small faces (<32 pixels)","Multiple faces in one image are processed sequentially, increasing total latency","No user control over detection confidence threshold — cannot adjust sensitivity","Face alignment assumes frontal or near-frontal orientation; side profiles may be misaligned","Detector may produce false positives on non-face objects (e.g., masks, sculptures)"],"requires":["Pre-trained face detector model (included, ~50MB)","Input image with at least one detectable face","GPU recommended for fast detection (CPU inference ~500ms per image)"],"input_types":["image/jpeg","image/png","image/webp"],"output_types":["face bounding boxes (internal)","aligned face crops (internal)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-sczhou--codeformer__cap_5","uri":"capability://image.visual.quality.aware.restoration.with.content.quality.token.decomposition","name":"quality-aware restoration with content-quality token decomposition","description":"Decomposes the restoration task into two parallel streams: content tokens (capturing facial structure, identity, pose) and quality tokens (capturing texture, fine details, surface properties). This decomposition allows the model to preserve identity while selectively enhancing quality, preventing over-smoothing or hallucination. Content tokens are extracted from the degraded input and refined using priors; quality tokens are synthesized from the codebook. The two streams are recombined to produce the final restored image.","intents":["restore image quality without altering facial identity or structure","selectively enhance texture and detail while preserving original facial geometry","prevent over-smoothing or loss of distinctive facial features during restoration","enable independent control over content preservation vs. quality enhancement"],"best_for":["applications requiring identity-preserving restoration (forensics, archival)","researchers studying disentangled representations in generative models","teams building facial image enhancement with strict identity constraints"],"limitations":["Decomposition adds model complexity and inference latency (~20-30% overhead vs. single-stream)","No user-facing control to adjust content-quality trade-off; decomposition is fixed at training time","Content token extraction may fail on severely degraded images where structure is ambiguous","Quality token synthesis is constrained by codebook; cannot generate details beyond learned variations","Recombination of content and quality streams may introduce artifacts at boundaries"],"requires":["Model architecture with dual-stream encoder (included in pre-trained weights)","GPU for efficient parallel stream processing","Input image with detectable facial structure"],"input_types":["degraded facial image"],"output_types":["restored facial image with preserved identity and enhanced quality"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":23,"verified":false,"data_access_risk":"high","permissions":["Input image with detectable face (minimum 32x32 pixels recommended)","Modern web browser with WebGL support for Gradio interface","GPU access recommended (NVIDIA CUDA 11.0+ or equivalent) for sub-second inference","Face detection model (built-in, requires ~50MB VRAM)","GPU with minimum 2GB VRAM for multi-scale feature extraction","Input image with clearly visible facial region (minimum 64x64 pixels)","Pre-trained codebook (included in model weights, ~100MB)","Vector quantization layer in model architecture","GPU for fast codebook lookup (CPU inference is feasible but slow)","Modern web browser (Chrome, Firefox, Safari, Edge)"],"failure_modes":["Restoration quality degrades significantly for faces smaller than 64x64 pixels or with extreme pose variations >45 degrees","No built-in batch processing — processes one image at a time through the web interface","Inference latency ~2-5 seconds per image on CPU, requires GPU for real-time performance","May introduce hallucinated facial details inconsistent with original image intent in heavily degraded inputs","Limited to frontal or near-frontal face orientations; side profiles and extreme angles produce artifacts","Alignment quality depends on face detection accuracy — fails silently if face detector misses or misaligns the face region","Multi-scale processing adds computational overhead; no option to disable for faster inference on high-quality inputs","Cross-attention mechanism requires sufficient GPU memory; batch processing of large images may cause OOM errors","Spatial attention maps are not exposed to users — no visibility into which regions are prioritized","Codebook size is fixed at training time; cannot adapt to new facial variations without retraining","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":"2026-05-24T12:16:23.325Z","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=sczhou--codeformer","compare_url":"https://unfragile.ai/compare?artifact=sczhou--codeformer"}},"signature":"HHT1R5OgAlbi3jQAQlB1dVR4ntc8E5dSbaleyXLcjdrFkw6piHKLVF3wXy9Jm3IBnDwljPLuWbphXm9Opb9+Ag==","signedAt":"2026-06-22T02:17:22.934Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/sczhou--codeformer","artifact":"https://unfragile.ai/sczhou--codeformer","verify":"https://unfragile.ai/api/v1/verify?slug=sczhou--codeformer","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"}}