{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-bytedance--infiniteyou","slug":"bytedance--infiniteyou","name":"InfiniteYou","type":"repo","url":"https://bytedance.github.io/InfiniteYou/","page_url":"https://unfragile.ai/bytedance--infiniteyou","categories":["image-generation"],"tags":["diffusers","diffusion","diffusion-transformer","dit","face","flux","iccv2025","identity-preserving","image-editing","image-generation","personalization","pytorch","research","text-to-image"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-bytedance--infiniteyou__cap_0","uri":"capability://image.visual.identity.preserved.text.to.image.generation.with.dit.backbone","name":"identity-preserved text-to-image generation with dit backbone","description":"Generates photorealistic images from text prompts while preserving a person's identity from reference photos. Uses InfUFluxPipeline to orchestrate the FLUX Diffusion Transformer base model, injecting identity features extracted from reference images via InfuseNet's residual connections throughout the diffusion process. The pipeline coordinates face analysis, identity feature extraction, and controlled diffusion sampling to balance text-image alignment with identity similarity.","intents":["Generate diverse photos of a specific person in different contexts, poses, and styles while maintaining their facial identity","Create photorealistic variations of a person's appearance without face copy-pasting artifacts","Transform a person's appearance according to text prompts (e.g., 'in a business suit', 'as a superhero') while keeping their identity intact"],"best_for":["Content creators building personalized photo generation workflows","Researchers exploring identity-preserving diffusion models","Teams building face-aware image generation applications"],"limitations":["Requires high VRAM (24GB+ for full precision; 16GB with memory optimizations like flash-attention and 8-bit quantization)","Identity preservation quality degrades with low-quality or heavily filtered reference images","Text prompt understanding may conflict with identity preservation in edge cases (e.g., requesting extreme style changes)","Inference latency ~10-30 seconds per image depending on hardware and optimization settings"],"requires":["Python 3.9+","PyTorch 2.0+","CUDA 11.8+ or compatible GPU with 16GB+ VRAM","Hugging Face transformers library","Pre-trained FLUX.1 model weights (~24GB)"],"input_types":["reference image (JPEG/PNG, 512x512 to 1024x1024 recommended)","text prompt (string, 10-200 tokens optimal)","optional control image for pose/composition guidance"],"output_types":["generated image (PNG, 768x768 to 1024x1024)","identity similarity score (float 0-1)","generation metadata (seed, guidance scale, steps)"],"categories":["image-visual","personalization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-bytedance--infiniteyou__cap_1","uri":"capability://image.visual.dual.stage.model.selection.for.identity.aesthetics.tradeoff","name":"dual-stage model selection for identity-aesthetics tradeoff","description":"Provides two pre-trained model variants (aes_stage2 and sim_stage1) that represent different points on the identity-preservation vs. aesthetic-quality spectrum. The aes_stage2 variant applies supervised fine-tuning (SFT) to improve text-image alignment and visual aesthetics, while sim_stage1 prioritizes identity similarity. Users can select the variant at runtime based on their specific use case requirements.","intents":["Choose between identity-focused or aesthetics-focused generation based on application needs","Understand the tradeoff between preserving exact facial features vs. generating visually polished results","Experiment with both variants to find the optimal balance for a specific use case"],"best_for":["Developers building applications where identity preservation is critical (e.g., personal photo generation)","Teams needing aesthetic quality for commercial use (e.g., marketing, social media)","Researchers studying the identity-aesthetics tradeoff in generative models"],"limitations":["No continuous interpolation between variants; must choose one or run both sequentially","SFT in aes_stage2 may slightly reduce identity similarity compared to sim_stage1","Both variants require full model loading; no lightweight distilled versions available","Model selection is static per generation; cannot dynamically adjust during inference"],"requires":["Model weights for both variants (~24GB each, or ~24GB total if sharing backbone)","Sufficient VRAM to load selected variant","Configuration parameter to specify model variant at pipeline initialization"],"input_types":["model variant identifier (string: 'aes_stage2' or 'sim_stage1')","reference image","text prompt"],"output_types":["generated image","variant metadata (which model was used)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-bytedance--infiniteyou__cap_10","uri":"capability://image.visual.multi.concept.personalization.via.omnicontrol.composition","name":"multi-concept personalization via omnicontrol composition","description":"Supports composition with OmniControl for multi-concept personalization, enabling simultaneous control over multiple identity-related or style-related concepts in a single generation. The pipeline can integrate OmniControl's multi-concept conditioning alongside InfuseNet's identity injection, allowing users to generate images that preserve identity while also incorporating other personalized concepts (e.g., specific clothing, accessories, or artistic styles).","intents":["Generate images that preserve identity while incorporating multiple personalized concepts simultaneously","Combine identity preservation with style or object-specific personalization","Explore the interaction between identity and multi-concept conditioning"],"best_for":["Advanced users building complex personalization workflows","Researchers studying multi-concept composition in generative models","Teams needing fine-grained control over multiple aspects of generated images"],"limitations":["OmniControl integration is mentioned but not fully documented; implementation details are unclear","Potential conflicts between identity preservation and multi-concept guidance; no automatic conflict resolution","Computational overhead of combining InfuseNet + OmniControl is not quantified","Requires understanding of both InfiniteYou and OmniControl APIs; steep learning curve"],"requires":["OmniControl package installed","OmniControl model weights","Configuration for multi-concept inputs (not fully specified in docs)"],"input_types":["reference image for identity (image)","reference images/concepts for OmniControl (images or concept embeddings)","text prompt (string)","concept weights (floats, 0.0-1.0)"],"output_types":["generated image with identity and multi-concept conditioning applied","metadata indicating which concepts were used and their weights"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-bytedance--infiniteyou__cap_11","uri":"capability://image.visual.configurable.diffusion.sampling.with.guidance.scale.and.step.control","name":"configurable diffusion sampling with guidance scale and step control","description":"Exposes diffusion sampling parameters (guidance scale, number of steps, sampler type) as user-configurable options within the InfUFluxPipeline. Users can adjust these parameters to control the balance between identity preservation, text-prompt adherence, and generation quality. Higher guidance scales strengthen text-prompt following; more steps improve quality but increase latency. The pipeline supports multiple sampler implementations (e.g., DDIM, Euler, DPM++).","intents":["Fine-tune the balance between identity preservation and text-prompt adherence","Trade off generation quality against inference speed by adjusting step count","Experiment with different sampler algorithms to find optimal results for specific prompts"],"best_for":["Researchers optimizing generation quality and identity preservation","Developers building adaptive systems that adjust sampling parameters based on user feedback","Advanced users who understand diffusion sampling and want fine-grained control"],"limitations":["No automatic tuning of guidance scale; users must manually experiment to find optimal values","Guidance scale may conflict with identity preservation if set too high (text prompt overrides identity)","Increasing steps linearly increases inference time; no adaptive step scheduling","Different samplers may produce different quality-speed tradeoffs; no guidance on which to use"],"requires":["Configuration parameters: 'guidance_scale' (float, typically 7.5-15.0), 'num_steps' (int, typically 20-50), 'sampler' (string: 'ddim', 'euler', 'dpm++', etc.)"],"input_types":["guidance_scale (float, 0.0-20.0)","num_inference_steps (int, 1-100)","sampler_name (string)","optional: seed (int, for reproducibility)"],"output_types":["generated image","sampling metadata (steps taken, sampler used, guidance scale applied)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-bytedance--infiniteyou__cap_12","uri":"capability://image.visual.reproducible.generation.with.seed.control.and.deterministic.inference","name":"reproducible generation with seed control and deterministic inference","description":"Supports seed-based reproducibility for image generation, enabling users to generate identical images by specifying the same seed, reference image, prompt, and parameters. The pipeline manages random number generation across PyTorch, NumPy, and other libraries to ensure deterministic behavior. This is critical for debugging, evaluation, and creating consistent results across different runs.","intents":["Reproduce exact generation results for debugging or evaluation","Create consistent results for A/B testing or user studies","Enable version control and comparison of generation parameters"],"best_for":["Researchers running controlled experiments and evaluations","Teams building production systems where consistency is important","Developers debugging generation issues"],"limitations":["Determinism may not be guaranteed across different PyTorch versions or hardware (GPU vs CPU)","Floating-point precision differences can cause minor variations in output even with same seed","Seed control does not guarantee identical results if model weights or architecture change","No built-in seed management for batch generation; users must manually track seeds"],"requires":["Seed parameter (int, typically 0-2^31-1)","Consistent PyTorch version and CUDA version across runs","Same hardware (GPU model) for guaranteed reproducibility"],"input_types":["seed (int)"],"output_types":["generated image (deterministic given same inputs)","seed metadata (seed value used)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-bytedance--infiniteyou__cap_2","uri":"capability://image.visual.face.detection.and.identity.feature.extraction.from.reference.images","name":"face detection and identity feature extraction from reference images","description":"Analyzes reference photos to detect faces and extract identity-relevant features that are injected into the diffusion process. The Face Analysis Module performs face detection (likely using MTCNN or similar), extracts facial embeddings or feature vectors, and passes these to InfuseNet for integration into the generation pipeline. This enables the system to understand and preserve the identity characteristics of the reference person.","intents":["Automatically detect and extract identity features from user-provided reference photos without manual annotation","Handle multiple faces in a reference image and select the primary/largest face for identity preservation","Validate that a reference image contains a detectable face before attempting generation"],"best_for":["Applications requiring automatic face detection without user intervention","Systems processing user-uploaded photos where face presence is not guaranteed","Workflows where identity features must be extracted once and reused across multiple generations"],"limitations":["Fails silently or with poor results on heavily occluded, rotated (>45°), or low-resolution (<64x64) faces","Single-face detection; behavior is undefined for multi-face images (typically selects largest face)","No explicit handling of profile views or non-frontal faces; identity preservation degrades with extreme angles","Extraction is deterministic; no option to manually adjust or weight identity features"],"requires":["Reference image with clearly visible face (frontal or near-frontal preferred)","Face detection model (weights not explicitly documented; likely bundled with FLUX)","Minimum image resolution ~256x256 for reliable detection"],"input_types":["image file (JPEG/PNG)","image tensor (torch.Tensor, shape [3, H, W])"],"output_types":["face bounding box (x1, y1, x2, y2)","identity feature vector (embedding, dimension not specified in docs)","face detection confidence score (float 0-1)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-bytedance--infiniteyou__cap_3","uri":"capability://image.visual.residual.connection.based.identity.feature.injection.into.dit.latent.space","name":"residual-connection-based identity feature injection into dit latent space","description":"InfuseNet injects identity features into the FLUX Diffusion Transformer via residual connections at multiple layers of the model, rather than concatenating embeddings or using cross-attention. During the diffusion process, identity feature vectors are transformed and added to the DiT's hidden states at strategic points, allowing identity information to flow through the generation without disrupting the model's ability to follow text prompts. This architectural pattern preserves identity semantically within the learned representation space.","intents":["Embed identity information into the diffusion process in a way that doesn't conflict with text-prompt guidance","Maintain identity consistency across diverse generated poses, styles, and contexts","Avoid face copy-pasting artifacts that occur with naive blending or concatenation approaches"],"best_for":["Researchers implementing identity-aware diffusion models","Teams building personalized image generation systems where semantic identity preservation is critical","Developers extending FLUX with identity-conditioning capabilities"],"limitations":["Requires modification of the base FLUX model architecture; not compatible with unmodified FLUX checkpoints","Residual injection adds ~5-10% computational overhead per diffusion step","Identity feature dimension must match DiT hidden dimension; requires careful tuning of projection layers","No ablation study provided in documentation; unclear which layers benefit most from identity injection"],"requires":["Custom InfuseNet module implementation (provided in repository)","Modified FLUX model with residual connection hooks at specified layers","Identity feature vectors pre-extracted from reference images (dimension ~768 or ~1024)"],"input_types":["identity feature vector (torch.Tensor, shape [batch_size, feature_dim])","DiT hidden states at each layer (torch.Tensor, shape [batch_size, seq_len, hidden_dim])","diffusion timestep (int or torch.Tensor)"],"output_types":["modified hidden states with identity information injected (torch.Tensor, same shape as input)","residual connection weights (for interpretability, optional)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-bytedance--infiniteyou__cap_4","uri":"capability://image.visual.memory.optimized.inference.with.configurable.precision.and.attention.mechanisms","name":"memory-optimized inference with configurable precision and attention mechanisms","description":"Provides multiple memory optimization strategies to enable inference on GPUs with limited VRAM (16GB or less). Supports flash-attention for reduced memory footprint during attention computation, 8-bit quantization for model weights, gradient checkpointing, and selective layer freezing. Users can enable/disable optimizations via configuration parameters, trading off memory usage against inference speed and generation quality.","intents":["Run identity-preserved image generation on consumer GPUs (RTX 4060, RTX 4070) with 12-16GB VRAM","Reduce memory overhead when generating multiple images in sequence","Balance memory constraints against inference latency and output quality"],"best_for":["Solo developers and small teams with limited GPU budgets","Researchers prototyping on consumer hardware before scaling to data centers","Production systems where cost-per-inference is critical"],"limitations":["Flash-attention reduces memory by ~30-40% but adds ~5-10% latency overhead","8-bit quantization may reduce generation quality slightly (not quantified in docs)","Gradient checkpointing is only relevant during training; not applicable to inference","No automatic selection of optimal optimization strategy; users must manually configure based on hardware"],"requires":["PyTorch 2.0+ with flash-attention support (or xformers library as fallback)","CUDA compute capability 7.5+ for 8-bit quantization","Configuration file or CLI flags to enable/disable optimizations"],"input_types":["optimization configuration (dict or YAML with keys: 'use_flash_attention', 'use_8bit_quantization', etc.)","target VRAM budget (int, in GB)"],"output_types":["optimized model (torch.nn.Module with modifications applied)","memory usage estimate (dict with keys: 'model_weights', 'activations', 'total_gb')","inference speed estimate (float, seconds per image)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-bytedance--infiniteyou__cap_5","uri":"capability://image.visual.plug.and.play.lora.and.controlnet.integration.for.style.and.pose.control","name":"plug-and-play lora and controlnet integration for style and pose control","description":"Supports optional composition with LoRA (Low-Rank Adaptation) modules for style transfer (e.g., Realism, Anti-blur LoRAs) and ControlNet for explicit pose, composition, or style guidance. These extensions are loaded and applied within the InfUFluxPipeline without modifying the core identity-preservation logic, allowing users to layer additional control signals on top of identity-preserved generation. The pipeline handles LoRA weight merging and ControlNet conditioning at the appropriate diffusion steps.","intents":["Apply style transfer (e.g., photorealistic, artistic) to identity-preserved generations","Control pose and composition of generated images using reference pose images","Combine identity preservation with multi-concept personalization (e.g., via OmniControl)"],"best_for":["Content creators needing fine-grained control over style and pose while preserving identity","Teams building customizable photo generation workflows","Researchers exploring the composition of identity preservation with other conditioning methods"],"limitations":["LoRA and ControlNet add computational overhead (~10-20% per extension); stacking multiple extensions degrades performance","No automatic conflict resolution if LoRA/ControlNet guidance conflicts with identity preservation or text prompts","Requires manual tuning of LoRA scale and ControlNet guidance weight; no adaptive weighting","Compatibility with all LoRA/ControlNet variants not guaranteed; tested primarily with FLUX-compatible versions"],"requires":["Optional LoRA weights (e.g., Realism LoRA, Anti-blur LoRA) in SAFETENSORS format","Optional ControlNet model (e.g., FLUX ControlNet for pose control)","Configuration parameters: 'lora_path', 'lora_scale', 'controlnet_path', 'controlnet_conditioning_scale'"],"input_types":["LoRA file path (string, SAFETENSORS format)","LoRA scale (float, 0.0-1.0)","ControlNet file path (string)","ControlNet conditioning image (torch.Tensor or PIL.Image)","ControlNet guidance scale (float, 0.0-1.0)"],"output_types":["generated image with LoRA/ControlNet effects applied","metadata indicating which extensions were used and their parameters"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-bytedance--infiniteyou__cap_6","uri":"capability://automation.workflow.command.line.interface.for.batch.and.scripted.image.generation","name":"command-line interface for batch and scripted image generation","description":"Provides a test.py CLI script that enables programmatic and batch image generation without GUI overhead. Users specify reference images, text prompts, model variants, and optimization settings via command-line arguments or configuration files. The CLI handles model loading, inference orchestration, and output saving, making it suitable for automated workflows, CI/CD pipelines, and server-side generation.","intents":["Generate images in batch from a list of reference photos and prompts","Integrate identity-preserved generation into automated workflows or APIs","Script image generation for testing, evaluation, or dataset creation"],"best_for":["Backend developers building image generation APIs or services","Researchers running large-scale evaluation or dataset generation experiments","DevOps engineers integrating generation into CI/CD pipelines"],"limitations":["No progress reporting or streaming output; full inference must complete before results are available","Error handling is basic; failures in batch processing may not be granular (e.g., one bad image stops the batch)","No built-in logging or monitoring; users must implement their own instrumentation","CLI argument parsing is likely basic; complex configurations may require YAML/JSON config files"],"requires":["Python 3.9+","InfiniteYou package installed","test.py script in repository root"],"input_types":["reference image path (string or list of strings)","text prompt (string or list of strings)","model variant (string: 'aes_stage2' or 'sim_stage1')","optional: configuration file (YAML/JSON)","optional: output directory (string)"],"output_types":["generated image files (PNG, saved to output directory)","metadata JSON (generation parameters, timing, etc.)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-bytedance--infiniteyou__cap_7","uri":"capability://image.visual.interactive.gradio.web.interface.for.real.time.generation.and.preview","name":"interactive gradio web interface for real-time generation and preview","description":"Provides an interactive web UI (app.py) built with Gradio that enables real-time image generation with live preview, parameter adjustment, and result gallery. Users upload reference images, enter text prompts, select model variants and optimization settings, and see generated results immediately. The interface handles model loading, inference, and result caching to provide responsive user experience.","intents":["Explore identity-preserved generation interactively without coding","Adjust parameters (model variant, guidance scales, etc.) and see results in real-time","Share generation results and parameters with collaborators via shareable Gradio links"],"best_for":["Non-technical users and content creators exploring the tool","Researchers prototyping and evaluating generation quality","Teams collaborating on image generation with shared Gradio instances"],"limitations":["Gradio interface adds ~500ms-1s overhead per request (serialization, HTTP, etc.)","No built-in user authentication; not suitable for production multi-user systems without additional security","Result caching is in-memory; no persistence across server restarts","Limited customization of UI layout without modifying app.py source code"],"requires":["Gradio 3.0+","InfiniteYou package installed","app.py script in repository root","Web browser for accessing the interface"],"input_types":["reference image (uploaded via file picker)","text prompt (text input field)","model variant (dropdown: 'aes_stage2' or 'sim_stage1')","optional: optimization settings (checkboxes/sliders)"],"output_types":["generated image (displayed in UI)","generation metadata (displayed as text or JSON)","downloadable image file (PNG)"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-bytedance--infiniteyou__cap_8","uri":"capability://image.visual.comfyui.node.integration.for.node.based.visual.workflow.composition","name":"comfyui node integration for node-based visual workflow composition","description":"Provides native ComfyUI nodes that integrate InfiniteYou into ComfyUI's node-based workflow system. Users can compose identity-preserved generation workflows visually by connecting nodes for image loading, identity extraction, prompt input, and generation. The integration handles model loading, parameter passing, and result routing within ComfyUI's execution graph.","intents":["Build complex image generation workflows visually without coding","Combine identity-preserved generation with other ComfyUI nodes (e.g., upscaling, post-processing)","Leverage ComfyUI's workflow persistence and sharing capabilities"],"best_for":["VFX artists and motion designers familiar with node-based tools","Teams building complex image generation pipelines with multiple processing stages","Users who prefer visual workflow composition over scripting"],"limitations":["Requires ComfyUI installation and setup; adds dependency on ComfyUI version compatibility","Node parameters are limited to ComfyUI's supported types (strings, numbers, images); complex configurations may be cumbersome","Debugging node-based workflows is harder than script-based debugging","Performance may be slower than direct Python API due to ComfyUI's execution overhead"],"requires":["ComfyUI installation (version not specified; likely requires recent version)","InfiniteYou package installed in ComfyUI's Python environment","Custom node files (provided in repository) placed in ComfyUI's custom_nodes directory"],"input_types":["reference image (ComfyUI IMAGE type)","text prompt (ComfyUI STRING type)","model variant (ComfyUI COMBO type with options)","optional: control image (ComfyUI IMAGE type)"],"output_types":["generated image (ComfyUI IMAGE type)","metadata (ComfyUI STRING type, JSON-formatted)"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-bytedance--infiniteyou__cap_9","uri":"capability://image.visual.base.model.replacement.and.variant.compatibility","name":"base model replacement and variant compatibility","description":"Supports swapping the underlying FLUX base model with alternative variants (e.g., FLUX.1-schnell for faster inference) while maintaining identity-preservation capabilities. The InfUFluxPipeline is designed to be model-agnostic at the base level, allowing users to substitute different FLUX checkpoints without modifying the InfuseNet identity injection logic. This enables tradeoffs between inference speed, quality, and memory usage.","intents":["Use faster FLUX variants (e.g., schnell) for real-time or interactive generation","Experiment with different base model versions to find optimal quality-speed tradeoff","Adapt to future FLUX model releases without retraining InfuseNet"],"best_for":["Teams needing to optimize inference speed for production systems","Researchers exploring the impact of base model choice on identity preservation","Developers building adaptive systems that switch models based on latency requirements"],"limitations":["InfuseNet weights are trained on a specific FLUX variant; switching base models may degrade identity preservation quality","No automatic retraining or fine-tuning of InfuseNet for new base models; users must use pre-trained weights","Compatibility is not guaranteed for all FLUX variants; only tested with FLUX.1 and FLUX.1-schnell","Different base models may have different latent space dimensions, requiring InfuseNet projection layer adjustments"],"requires":["Alternative FLUX model weights (e.g., FLUX.1-schnell checkpoint)","Configuration parameter to specify base model path","Matching InfuseNet weights (or retraining if base model architecture differs significantly)"],"input_types":["base model path (string, path to FLUX checkpoint)","base model type (string: 'flux.1-dev', 'flux.1-schnell', etc.)"],"output_types":["loaded model (torch.nn.Module)","model metadata (architecture, parameter count, etc.)"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":42,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","PyTorch 2.0+","CUDA 11.8+ or compatible GPU with 16GB+ VRAM","Hugging Face transformers library","Pre-trained FLUX.1 model weights (~24GB)","Model weights for both variants (~24GB each, or ~24GB total if sharing backbone)","Sufficient VRAM to load selected variant","Configuration parameter to specify model variant at pipeline initialization","OmniControl package installed","OmniControl model weights"],"failure_modes":["Requires high VRAM (24GB+ for full precision; 16GB with memory optimizations like flash-attention and 8-bit quantization)","Identity preservation quality degrades with low-quality or heavily filtered reference images","Text prompt understanding may conflict with identity preservation in edge cases (e.g., requesting extreme style changes)","Inference latency ~10-30 seconds per image depending on hardware and optimization settings","No continuous interpolation between variants; must choose one or run both sequentially","SFT in aes_stage2 may slightly reduce identity similarity compared to sim_stage1","Both variants require full model loading; no lightweight distilled versions available","Model selection is static per generation; cannot dynamically adjust during inference","OmniControl integration is mentioned but not fully documented; implementation details are unclear","Potential conflicts between identity preservation and multi-concept guidance; no automatic conflict resolution","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.5272749177296192,"quality":0.35,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.6,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"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":"2026-05-24T12:16:21.549Z","last_scraped_at":"2026-05-03T13:58:44.860Z","last_commit":"2025-08-22T21:07:45Z"},"community":{"stars":2678,"forks":289,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=bytedance--infiniteyou","compare_url":"https://unfragile.ai/compare?artifact=bytedance--infiniteyou"}},"signature":"iKmSi+p3ChUvvWergpEY+ljOs7r/fAgyaDGHfYUWDfVOeFQRMAFeV1PCvaf22oy/dYtykrYZG8h8Te0SRNEBCw==","signedAt":"2026-06-20T15:04:16.753Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/bytedance--infiniteyou","artifact":"https://unfragile.ai/bytedance--infiniteyou","verify":"https://unfragile.ai/api/v1/verify?slug=bytedance--infiniteyou","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"}}