{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-space-prithivmlmods--flux-lora-dlc","slug":"prithivmlmods--flux-lora-dlc","name":"FLUX-LoRA-DLC","type":"model","url":"https://huggingface.co/spaces/prithivMLmods/FLUX-LoRA-DLC","page_url":"https://unfragile.ai/prithivmlmods--flux-lora-dlc","categories":["model-training"],"tags":["gradio","mcp-server","region:us"],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-space-prithivmlmods--flux-lora-dlc__cap_0","uri":"capability://automation.workflow.lora.adapter.training.on.flux.image.generation.model","name":"lora adapter training on flux image generation model","description":"Enables fine-tuning of FLUX text-to-image model weights through Low-Rank Adaptation (LoRA), a parameter-efficient training technique that freezes base model weights and trains only low-rank decomposition matrices. The implementation uses gradient-based optimization on image generation tasks, allowing users to customize model behavior for specific visual styles, subjects, or artistic directions without full model retraining. Training state is managed through HuggingFace Spaces infrastructure with Gradio UI for parameter configuration.","intents":["Train a custom FLUX model variant on my specific visual style or subject matter without GPU resources","Create reusable LoRA weights that adapt FLUX for consistent character/art style generation","Fine-tune FLUX on domain-specific imagery (product photography, architectural renders, etc.) with minimal computational overhead"],"best_for":["Individual artists and creators wanting personalized image generation without full model training","Teams building custom visual content pipelines with consistent style requirements","Researchers experimenting with parameter-efficient fine-tuning on large diffusion models"],"limitations":["LoRA rank and alpha hyperparameters significantly impact quality vs training speed tradeoff — no automated tuning guidance provided","Training convergence depends heavily on dataset quality and size; insufficient examples (< 50 images) may cause overfitting","No built-in validation metrics or early stopping — requires manual monitoring of generated outputs during training","Inference latency overhead from LoRA adapter loading is minimal but not quantified in documentation"],"requires":["HuggingFace account with Spaces access","Training dataset of 20-500 images in supported formats (PNG, JPG, WebP)","Web browser with JavaScript enabled for Gradio interface","Patience for training time (typically 30 minutes to several hours depending on dataset size and LoRA rank)"],"input_types":["image collection (PNG, JPG, WebP format)","text prompts describing desired training direction","hyperparameter values (learning rate, LoRA rank, training steps, batch size)"],"output_types":["LoRA weight file (.safetensors format)","Generated sample images during training preview","Training metadata and configuration JSON"],"categories":["automation-workflow","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-prithivmlmods--flux-lora-dlc__cap_1","uri":"capability://automation.workflow.web.based.lora.training.interface.with.real.time.preview","name":"web-based lora training interface with real-time preview","description":"Provides a Gradio-based UI running on HuggingFace Spaces that exposes LoRA training parameters (rank, learning rate, steps, batch size) and generates preview images at configurable intervals during training. The interface handles file uploads for training datasets, manages training job lifecycle (start/pause/resume), and displays loss curves or training metrics in real-time. State is persisted in the Spaces environment with outputs downloadable as .safetensors files.","intents":["Monitor training progress visually without SSH/CLI access","Experiment with different LoRA hyperparameters through an interactive form","Download trained LoRA weights directly from the browser for immediate use in other tools"],"best_for":["Non-technical creators who prefer visual interfaces over command-line tools","Rapid prototyping of LoRA configurations without local GPU setup","Teams collaborating on model training with shared Spaces links"],"limitations":["Gradio interface adds ~500ms latency per interaction due to client-server round-trips","No persistent storage between Spaces restarts — training state is lost if session expires","Preview generation during training consumes additional GPU memory, potentially slowing training","Limited to single-GPU training; no distributed training support across multiple Spaces instances"],"requires":["Modern web browser (Chrome, Firefox, Safari, Edge)","Stable internet connection for Gradio WebSocket communication","HuggingFace Spaces quota (free tier has compute limits)"],"input_types":["image files via drag-and-drop or file picker","numeric parameters via sliders and text inputs","text descriptions via textarea"],"output_types":["interactive HTML form with live preview pane","downloadable .safetensors files","training logs and metrics visualization"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-prithivmlmods--flux-lora-dlc__cap_2","uri":"capability://data.processing.analysis.lora.weight.export.and.compatibility.management","name":"lora weight export and compatibility management","description":"Manages trained LoRA adapter export in .safetensors format with embedded metadata (training config, model version, LoRA rank/alpha values). The system ensures compatibility by storing model architecture information and version tags, allowing exported weights to be loaded into compatible FLUX inference pipelines. Export includes optional quantization or compression options to reduce file size for distribution.","intents":["Export trained LoRA weights in a portable format that works with other FLUX tools and inference engines","Share LoRA adapters with team members or publish to model hubs with full metadata","Ensure trained weights remain compatible across FLUX model updates and versions"],"best_for":["Creators building reusable model libraries for internal or public distribution","Teams managing multiple LoRA variants across different FLUX versions","Researchers publishing reproducible fine-tuning results with full configuration"],"limitations":["No automatic version compatibility checking — users must manually verify LoRA rank matches target FLUX model",".safetensors format is immutable after export; retraining required to modify weights","Metadata embedding adds ~5-10% file size overhead compared to raw weight tensors","No built-in encryption or access control for exported weights"],"requires":["Completed LoRA training session","Target FLUX model version specification","Sufficient disk space for .safetensors file (typically 50-500 MB depending on rank)"],"input_types":["trained LoRA state from training session","metadata JSON with training configuration","optional compression level parameter"],"output_types":[".safetensors file with embedded metadata","configuration JSON for reproducibility","optional compressed archive (.zip or .tar.gz)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-prithivmlmods--flux-lora-dlc__cap_3","uri":"capability://data.processing.analysis.dataset.preparation.and.augmentation.for.lora.training","name":"dataset preparation and augmentation for lora training","description":"Provides utilities to preprocess uploaded image datasets for LoRA training, including resizing to FLUX-compatible dimensions (typically 768x768 or 1024x1024), format conversion (PNG/JPG to standardized format), and optional augmentation (random crops, flips, color jitter). The system validates image quality, filters corrupted files, and generates captions or prompts for each image using vision-language models or user-provided text. Augmentation parameters are configurable to control dataset diversity without manual image editing.","intents":["Prepare raw image collections for training without manual preprocessing in external tools","Automatically generate training captions/prompts from images using vision models","Augment small datasets to improve LoRA generalization and reduce overfitting"],"best_for":["Users with unstructured image collections lacking captions or consistent sizing","Teams wanting to standardize dataset preparation across multiple LoRA training runs","Researchers studying the impact of augmentation strategies on LoRA convergence"],"limitations":["Auto-caption generation quality depends on vision model accuracy; may require manual correction for domain-specific terminology","Aggressive augmentation can introduce artifacts or distort important visual details in small datasets","No deduplication or similarity filtering — duplicate images in dataset are not automatically detected","Resizing to fixed dimensions may distort aspect ratios; no smart cropping or padding options"],"requires":["Image dataset with 20+ images (minimum for meaningful LoRA training)","Supported formats: PNG, JPG, WebP, TIFF","Optional: vision-language model API key for auto-captioning (e.g., CLIP, LLaVA)"],"input_types":["image files (PNG, JPG, WebP, TIFF)","optional CSV with existing captions","augmentation parameters (crop ratio, flip probability, color jitter strength)"],"output_types":["preprocessed image dataset in standardized format","caption/prompt file (JSON or CSV) for each image","augmentation report with statistics (dataset size, aspect ratio distribution)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-prithivmlmods--flux-lora-dlc__cap_4","uri":"capability://automation.workflow.training.monitoring.and.loss.visualization","name":"training monitoring and loss visualization","description":"Tracks training metrics (loss, learning rate schedule, gradient norms) during LoRA training and visualizes them in real-time through interactive plots (loss curves, learning rate decay, validation metrics if applicable). The system logs training events to a structured format (JSON or CSV) for post-training analysis and reproducibility. Metrics are displayed in the Gradio interface with configurable refresh intervals, and historical training runs can be compared side-by-side.","intents":["Monitor training convergence in real-time to detect divergence or overfitting early","Compare hyperparameter configurations by analyzing loss curves across multiple training runs","Export training logs for external analysis or documentation in research papers"],"best_for":["Researchers and practitioners iterating on LoRA hyperparameters","Teams maintaining training experiment logs for reproducibility","Users debugging training instability or poor convergence"],"limitations":["Real-time metric computation adds ~50-100ms overhead per training step","Visualization refresh rate is limited by Gradio's update frequency; high-frequency metrics may be downsampled","No automatic anomaly detection for training failures; users must manually inspect plots","Metrics are stored in Spaces ephemeral storage; lost if session expires without explicit export"],"requires":["Active training session","Gradio interface with WebSocket connection for real-time updates","Optional: external storage (HuggingFace Hub, S3) for persistent metric logging"],"input_types":["training loop events (loss, gradients, learning rate)","optional validation dataset for validation metrics","metric configuration (which metrics to track, logging frequency)"],"output_types":["interactive loss curve plots (Plotly or similar)","structured training logs (JSON/CSV)","summary statistics (final loss, convergence rate, training time)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-prithivmlmods--flux-lora-dlc__cap_5","uri":"capability://image.visual.inference.with.trained.lora.adapters","name":"inference with trained lora adapters","description":"Loads trained LoRA weights and applies them to the base FLUX model for image generation, merging low-rank adapter matrices with frozen base weights during inference. The system supports prompt-based generation with optional negative prompts, seed control for reproducibility, and guidance scale adjustment for prompt adherence. LoRA inference is implemented as a forward pass modification that adds adapter outputs to base model activations, with minimal latency overhead compared to base model inference.","intents":["Generate images using trained LoRA adapters without retraining or model merging","Test LoRA quality and consistency across different prompts and seeds","Integrate trained LoRA into downstream image generation pipelines"],"best_for":["Validating LoRA training quality before deployment","Creating consistent visual outputs for content generation workflows","Experimenting with prompt engineering for LoRA-adapted models"],"limitations":["LoRA inference requires loading both base model and adapter weights; total memory footprint is base model size + LoRA rank * hidden dim","Inference latency is ~5-10% slower than base model due to adapter computation","No built-in batch inference optimization; single-image generation at a time in web interface","Seed reproducibility depends on FLUX implementation; may vary across hardware or software versions"],"requires":["Trained LoRA weights in .safetensors format","Base FLUX model weights (loaded from HuggingFace Hub or local cache)","GPU with sufficient VRAM for base model + adapter (typically 8GB+ for full precision)"],"input_types":["text prompt (string)","optional negative prompt (string)","optional seed (integer)","guidance scale (float, typically 1.0-20.0)","LoRA weight path (.safetensors file)"],"output_types":["generated image (PNG or JPEG)","generation metadata (seed, prompt, guidance scale, inference time)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":21,"verified":false,"data_access_risk":"high","permissions":["HuggingFace account with Spaces access","Training dataset of 20-500 images in supported formats (PNG, JPG, WebP)","Web browser with JavaScript enabled for Gradio interface","Patience for training time (typically 30 minutes to several hours depending on dataset size and LoRA rank)","Modern web browser (Chrome, Firefox, Safari, Edge)","Stable internet connection for Gradio WebSocket communication","HuggingFace Spaces quota (free tier has compute limits)","Completed LoRA training session","Target FLUX model version specification","Sufficient disk space for .safetensors file (typically 50-500 MB depending on rank)"],"failure_modes":["LoRA rank and alpha hyperparameters significantly impact quality vs training speed tradeoff — no automated tuning guidance provided","Training convergence depends heavily on dataset quality and size; insufficient examples (< 50 images) may cause overfitting","No built-in validation metrics or early stopping — requires manual monitoring of generated outputs during training","Inference latency overhead from LoRA adapter loading is minimal but not quantified in documentation","Gradio interface adds ~500ms latency per interaction due to client-server round-trips","No persistent storage between Spaces restarts — training state is lost if session expires","Preview generation during training consumes additional GPU memory, potentially slowing training","Limited to single-GPU training; no distributed training support across multiple Spaces instances","No automatic version compatibility checking — users must manually verify LoRA rank matches target FLUX model",".safetensors format is immutable after export; retraining required to modify weights","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.22,"ecosystem":0.38999999999999996,"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=prithivmlmods--flux-lora-dlc","compare_url":"https://unfragile.ai/compare?artifact=prithivmlmods--flux-lora-dlc"}},"signature":"1JVS6BDTuyUWodg6jkxgAzfqrRDPByBn/sfGtFvnpiPAAwKS+KOh3x+dL4ahZlzfqJeqxQnEEMNnMlU/Rj4TDQ==","signedAt":"2026-06-20T06:58:51.132Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/prithivmlmods--flux-lora-dlc","artifact":"https://unfragile.ai/prithivmlmods--flux-lora-dlc","verify":"https://unfragile.ai/api/v1/verify?slug=prithivmlmods--flux-lora-dlc","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"}}