{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-space-yanze--pulid-flux","slug":"yanze--pulid-flux","name":"PuLID-FLUX","type":"model","url":"https://huggingface.co/spaces/yanze/PuLID-FLUX","page_url":"https://unfragile.ai/yanze--pulid-flux","categories":["automation"],"tags":["gradio","region:us"],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-space-yanze--pulid-flux__cap_0","uri":"capability://image.visual.identity.preserving.face.generation.with.flux.backbone","name":"identity-preserving face generation with flux backbone","description":"Generates photorealistic images with consistent identity preservation by injecting identity embeddings into FLUX diffusion model's latent space. Uses PuLID (Personalized Latent ID) mechanism to encode facial identity features as compact embeddings that guide the diffusion process without full fine-tuning, enabling rapid identity-consistent generation across diverse prompts and styles while maintaining FLUX's native image quality and coherence.","intents":["Generate multiple variations of a person's face in different contexts without retraining the model","Create consistent character appearances across a series of AI-generated images","Preserve facial identity while applying style transfers or compositional changes","Avoid expensive per-identity fine-tuning while maintaining identity fidelity"],"best_for":["character designers and game developers needing consistent NPC/character generation","content creators producing multi-image narratives with consistent protagonists","teams building personalized AI avatar systems without per-user model training","researchers exploring identity-aware generative models"],"limitations":["Requires clear, frontal facial reference image for optimal identity encoding — profile or occluded faces degrade consistency","Identity preservation quality degrades with extreme style prompts that conflict with learned identity features","No built-in face detection or automatic cropping — requires manual region selection or preprocessing","Latent injection approach may cause subtle artifacts at identity-style boundaries in some compositional prompts","Single reference image per identity — multi-image enrollment not supported for improved robustness"],"requires":["Reference image containing clear facial features (minimum ~256x256 pixels)","Text prompt describing desired generation context/style","HuggingFace Spaces environment or local FLUX + PuLID implementation","GPU with minimum 8GB VRAM for inference (16GB+ recommended for batch generation)"],"input_types":["image (reference face photo, JPEG/PNG)","text (generation prompt)","optional: region mask or bounding box for face localization"],"output_types":["image (generated photo, PNG/JPEG)","optional: identity embedding vector (latent representation)"],"categories":["image-visual","generative-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-yanze--pulid-flux__cap_1","uri":"capability://image.visual.interactive.face.region.selection.and.masking","name":"interactive face region selection and masking","description":"Provides Gradio-based UI for users to upload reference images, manually select or draw bounding boxes around facial regions, and optionally refine masks for precise identity encoding. The interface handles image preprocessing, region extraction, and passes cropped/masked regions to the identity embedding encoder, enabling non-technical users to prepare reference faces without external image editing tools.","intents":["Upload a photo and quickly isolate the face region for identity encoding without external tools","Refine face detection by manually adjusting bounding boxes when automatic detection fails","Test multiple face crops from the same image to find optimal identity representation","Prepare batch reference images with consistent framing for reproducible identity embeddings"],"best_for":["non-technical end users generating personalized images via web interface","rapid prototyping workflows where manual region selection is faster than training detection models","scenarios with challenging face detection (extreme angles, occlusion, artistic photos)"],"limitations":["Manual region selection introduces user-dependent variability — same face may produce different embeddings based on crop boundaries","No automatic face detection fallback — users must manually select regions for every reference image","Gradio interface runs synchronously — batch processing multiple reference images requires sequential uploads","No persistent session storage — reference images and embeddings are discarded after generation"],"requires":["Web browser with JavaScript enabled","Reference image file (JPEG/PNG, <50MB)","HuggingFace Spaces access or local Gradio server"],"input_types":["image (uploaded reference photo)","mouse interaction (bounding box drawing or region selection)"],"output_types":["cropped image (extracted face region)","coordinates (bounding box or mask polygon)"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-yanze--pulid-flux__cap_2","uri":"capability://image.visual.prompt.guided.identity.consistent.image.synthesis","name":"prompt-guided identity-consistent image synthesis","description":"Accepts freeform text prompts describing desired image composition, style, and context, then synthesizes images that maintain the identity from the reference face while respecting the semantic content of the prompt. Uses FLUX's native text-to-image diffusion pipeline with identity embeddings injected as additional conditioning signals, enabling flexible creative control without identity loss or style collapse.","intents":["Generate the same person in different outfits, settings, or artistic styles","Create narrative sequences where a character appears consistently across multiple scenes","Explore how a person's identity translates to different visual contexts (e.g., fantasy, sci-fi, historical)","Maintain identity while applying complex compositional or stylistic requirements"],"best_for":["creative professionals (game designers, concept artists, storyboard creators) needing consistent character generation","content creators producing AI-assisted visual narratives","teams building personalized avatar or character generation systems"],"limitations":["Prompt quality directly impacts identity preservation — vague or conflicting prompts may degrade consistency","Extreme style transfers (e.g., 'oil painting', 'cartoon') may override identity features if style dominates prompt weighting","No explicit control over identity strength — users cannot dial identity preservation up/down independently","Prompt injection or adversarial prompts may cause identity leakage or unexpected behavior","Generation time scales with prompt complexity and image resolution (typically 30-60 seconds per image)"],"requires":["Valid text prompt (English language, <1000 characters recommended)","Pre-computed identity embedding from reference face","GPU with sufficient VRAM for FLUX inference"],"input_types":["text (generation prompt)","embedding vector (identity representation from reference face)"],"output_types":["image (generated photo, 768x768 or 1024x1024 pixels typical)"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-yanze--pulid-flux__cap_3","uri":"capability://image.visual.batch.image.generation.with.identity.consistency","name":"batch image generation with identity consistency","description":"Enables sequential generation of multiple images from a single reference identity and varying prompts, with each generation using the same pre-computed identity embedding to ensure visual consistency across the batch. Gradio interface queues requests and manages GPU memory between generations, allowing users to explore multiple creative variations without re-encoding the reference face.","intents":["Generate 5-10 variations of a character in different poses or expressions from a single reference","Create a series of images for a storyboard or comic sequence with consistent protagonist","Rapidly iterate on prompt variations to find the best composition while maintaining identity","Export a batch of consistent character images for use in games, animations, or media"],"best_for":["content creators and designers needing multiple consistent character variations","iterative creative workflows where users test multiple prompts sequentially","teams building character asset libraries with consistent visual identity"],"limitations":["Gradio's synchronous request handling means batch generation is sequential, not parallel — 10 images may take 5-10 minutes","No built-in progress tracking or request queuing visualization — users cannot see estimated completion time","GPU memory is not explicitly managed between requests — may cause OOM errors on smaller GPUs if generation resolution is high","No batch export or download functionality — users must manually save each generated image","Identity embedding is recomputed for each batch if reference image is re-uploaded, wasting computation"],"requires":["Reference face image (uploaded once per batch)","List of text prompts (one per desired image)","Stable internet connection (Spaces may timeout on slow connections)","GPU with 8GB+ VRAM for sequential generation"],"input_types":["image (reference face, single upload)","text (multiple prompts, one per generation)"],"output_types":["images (multiple generated photos, one per prompt)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-yanze--pulid-flux__cap_4","uri":"capability://data.processing.analysis.identity.embedding.extraction.and.caching","name":"identity embedding extraction and caching","description":"Encodes reference face images into compact identity embeddings (typically 256-512 dimensional vectors) using a learned encoder network, then caches these embeddings in memory or optionally exports them for reuse across multiple generation sessions. The encoder is trained to capture identity-specific features while being invariant to pose, lighting, and expression variations in the reference image.","intents":["Extract a reusable identity representation from a reference photo for consistent multi-session generation","Cache identity embeddings to avoid re-encoding the same face across multiple generation runs","Export embeddings for use in downstream applications or other FLUX-based systems","Analyze identity embedding space to understand what features are captured"],"best_for":["production systems where identity embeddings are precomputed and cached for performance","researchers studying identity representation in generative models","teams building identity-aware systems that need to persist embeddings across sessions"],"limitations":["Embedding quality depends on reference image quality — low-resolution or heavily occluded faces produce poor embeddings","Embeddings are not human-interpretable — no way to inspect or modify specific identity features","No built-in versioning or embedding comparison — cannot easily determine if two embeddings represent the same identity","Embeddings are tied to the specific encoder model — switching encoder versions breaks backward compatibility","No persistence layer in Spaces demo — embeddings are lost when session ends"],"requires":["Reference face image (clear, frontal preferred)","Pre-trained identity encoder model (included in PuLID)","GPU for encoding (typically <1 second per image)"],"input_types":["image (reference face photo)"],"output_types":["embedding vector (typically 256-512 dimensions, float32)","optional: embedding metadata (reference image path, timestamp, quality metrics)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-yanze--pulid-flux__cap_5","uri":"capability://image.visual.multi.prompt.identity.consistency.validation","name":"multi-prompt identity consistency validation","description":"Generates images from the same identity embedding using semantically diverse prompts (e.g., different poses, expressions, clothing, backgrounds) and visually compares outputs to validate that identity is preserved across varied contexts. Enables users to assess embedding quality and identify cases where identity is lost or degraded due to prompt-identity conflicts.","intents":["Validate that a reference face embedding produces consistent identity across diverse prompts","Identify problematic prompts that cause identity loss or artifacts","Compare identity preservation quality across different reference images or encoder versions","Demonstrate identity consistency to stakeholders or end users"],"best_for":["quality assurance teams validating identity preservation in production systems","researchers evaluating identity encoder robustness","users iterating on reference images to find optimal identity representation"],"limitations":["No automated metrics for identity consistency — validation is purely visual and subjective","Requires manual inspection of generated images — no quantitative similarity scoring","Diverse prompts may take longer to generate (30-60 seconds per image) — validation is time-consuming","No side-by-side comparison UI in Spaces demo — users must manually compare images","Identity loss is often subtle and context-dependent — difficult to identify root causes"],"requires":["Pre-computed identity embedding","Set of diverse test prompts (5-10 recommended for thorough validation)","GPU for generation"],"input_types":["embedding vector (identity representation)","text (diverse test prompts)"],"output_types":["images (generated photos for visual comparison)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":21,"verified":false,"data_access_risk":"high","permissions":["Reference image containing clear facial features (minimum ~256x256 pixels)","Text prompt describing desired generation context/style","HuggingFace Spaces environment or local FLUX + PuLID implementation","GPU with minimum 8GB VRAM for inference (16GB+ recommended for batch generation)","Web browser with JavaScript enabled","Reference image file (JPEG/PNG, <50MB)","HuggingFace Spaces access or local Gradio server","Valid text prompt (English language, <1000 characters recommended)","Pre-computed identity embedding from reference face","GPU with sufficient VRAM for FLUX inference"],"failure_modes":["Requires clear, frontal facial reference image for optimal identity encoding — profile or occluded faces degrade consistency","Identity preservation quality degrades with extreme style prompts that conflict with learned identity features","No built-in face detection or automatic cropping — requires manual region selection or preprocessing","Latent injection approach may cause subtle artifacts at identity-style boundaries in some compositional prompts","Single reference image per identity — multi-image enrollment not supported for improved robustness","Manual region selection introduces user-dependent variability — same face may produce different embeddings based on crop boundaries","No automatic face detection fallback — users must manually select regions for every reference image","Gradio interface runs synchronously — batch processing multiple reference images requires sequential uploads","No persistent session storage — reference images and embeddings are discarded after generation","Prompt quality directly impacts identity preservation — vague or conflicting prompts may degrade consistency","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":0.35,"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":"2026-05-24T12:16:23.325Z","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=yanze--pulid-flux","compare_url":"https://unfragile.ai/compare?artifact=yanze--pulid-flux"}},"signature":"5zl3+VnD3puZfiLwEfLFnlPaChmg09k5hZSUdSLMnpnH/LjzpCMXONusGbGwIrIz55KVlF3mIi1QOJhPvaEwAg==","signedAt":"2026-06-21T04:07:05.524Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/yanze--pulid-flux","artifact":"https://unfragile.ai/yanze--pulid-flux","verify":"https://unfragile.ai/api/v1/verify?slug=yanze--pulid-flux","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"}}