{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-space-prithivmlmods--qwen-image-edit-2511-loras-fast","slug":"prithivmlmods--qwen-image-edit-2511-loras-fast","name":"Qwen-Image-Edit-2511-LoRAs-Fast","type":"model","url":"https://huggingface.co/spaces/prithivMLmods/Qwen-Image-Edit-2511-LoRAs-Fast","page_url":"https://unfragile.ai/prithivmlmods--qwen-image-edit-2511-loras-fast","categories":["image-generation"],"tags":["gradio","mcp-server","region:us"],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-space-prithivmlmods--qwen-image-edit-2511-loras-fast__cap_0","uri":"capability://image.visual.lora.based.image.inpainting.and.region.editing","name":"lora-based image inpainting and region editing","description":"Performs targeted image editing within user-specified regions using Low-Rank Adaptation (LoRA) fine-tuned models layered on top of Qwen's base image generation architecture. The system accepts an input image, a text prompt describing desired edits, and a mask or region specification, then applies LoRA weights to selectively modify only the masked areas while preserving surrounding context through attention-based blending. This approach avoids full model retraining by injecting learned low-rank decompositions into the diffusion model's cross-attention layers.","intents":["Edit specific regions of an image without affecting the rest of the composition","Apply style or content changes to masked areas using natural language descriptions","Quickly iterate on image edits using pre-trained LoRA adapters without waiting for full model inference","Preserve background and non-target areas while modifying foreground objects or regions"],"best_for":["Content creators and designers needing fast iterative image editing","Teams building image editing workflows that require region-specific control","Developers prototyping AI-powered design tools with minimal latency requirements"],"limitations":["LoRA adapters are task-specific; editing quality depends on which LoRA weights are loaded and their training data","Mask quality and precision directly impact edit boundaries; imprecise masks cause artifacts at region edges","No built-in semantic understanding of image content; relies entirely on text prompt + mask combination","Batch editing of multiple regions in sequence may accumulate artifacts from previous edits"],"requires":["Input image in standard formats (PNG, JPG, WebP)","Text prompt describing desired edit","Binary mask or region coordinates specifying edit area","Pre-loaded LoRA weights compatible with Qwen image model","GPU with sufficient VRAM (typically 8GB+ for inference)"],"input_types":["image (PNG, JPG, WebP)","text (natural language prompt)","binary mask or region coordinates"],"output_types":["image (PNG, JPG)","metadata (edit parameters, LoRA weights used)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-prithivmlmods--qwen-image-edit-2511-loras-fast__cap_1","uri":"capability://tool.use.integration.multi.lora.weight.composition.and.switching","name":"multi-lora weight composition and switching","description":"Manages a library of pre-trained LoRA adapters that can be dynamically loaded, composed, or switched during inference without reloading the base Qwen model. The system maintains a registry of available LoRA weights (e.g., 'style-transfer', 'object-removal', 'detail-enhancement'), allows users to select which adapter(s) to apply, and blends their contributions through weighted combination in the model's attention layers. This architecture enables rapid experimentation across different editing capabilities without the overhead of full model reloading.","intents":["Switch between different editing styles or capabilities without restarting the model","Combine multiple LoRA adapters to apply compound edits (e.g., style + detail enhancement)","Discover and select from available editing capabilities through a UI or API","Benchmark different LoRA weights to find the best fit for a specific editing task"],"best_for":["Interactive design tools where users need to try multiple editing styles rapidly","Research teams exploring LoRA composition and blending strategies","Production systems requiring flexible editing capabilities without model reloading overhead"],"limitations":["LoRA composition is additive; conflicting adapters may produce unpredictable results","No automatic conflict detection between incompatible LoRA weights","Switching LoRAs requires recomputation of attention layers; not zero-cost but faster than full model reload","Limited to LoRA adapters; other fine-tuning methods (QLoRA, full LoRA) not supported"],"requires":["Pre-trained LoRA weight files in compatible format (typically .safetensors or .pt)","LoRA registry or manifest defining available adapters and their metadata","Base Qwen model loaded in memory","Mechanism to inject LoRA weights into model's linear layers (e.g., via peft library)"],"input_types":["LoRA adapter identifier (string)","optional: composition weights for blending multiple adapters"],"output_types":["confirmation of loaded LoRA(s)","metadata about active adapters"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-prithivmlmods--qwen-image-edit-2511-loras-fast__cap_2","uri":"capability://image.visual.gradio.based.interactive.image.editing.interface","name":"gradio-based interactive image editing interface","description":"Exposes the LoRA-based image editing pipeline through a Gradio web UI hosted on HuggingFace Spaces, providing real-time image upload, mask drawing/upload, text prompt input, LoRA selection, and live preview of edits. The interface handles file I/O, parameter validation, and streaming results back to the browser using Gradio's reactive component system. Users interact through drag-and-drop image upload, canvas-based mask drawing or mask file upload, text input for edit prompts, and dropdown/radio selection for LoRA adapters.","intents":["Upload an image and interactively edit specific regions without writing code","Draw or upload a mask to define the edit region visually","Experiment with different LoRA adapters and prompts in real-time","Download edited images directly from the browser"],"best_for":["Non-technical designers and content creators","Rapid prototyping and demos of image editing capabilities","Teams evaluating LoRA-based editing before integration into production systems"],"limitations":["Gradio UI is stateless between sessions; no persistent editing history or project management","Mask drawing tools in Gradio are basic; complex masks require external tools and file upload","No batch processing; each image must be edited individually through the UI","Inference latency depends on HuggingFace Spaces compute allocation; may be slow during high traffic","No authentication or access control; public space is open to all users"],"requires":["Web browser with JavaScript enabled","Image file (PNG, JPG, WebP) under typical size limits (~10MB)","Internet connection to HuggingFace Spaces","No local GPU required; inference runs on Spaces backend"],"input_types":["image (drag-drop or file upload)","binary mask (canvas drawing or file upload)","text (natural language prompt)","categorical (LoRA adapter selection)"],"output_types":["image (edited result, downloadable)","metadata (edit parameters, inference time)"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-prithivmlmods--qwen-image-edit-2511-loras-fast__cap_3","uri":"capability://image.visual.mask.guided.diffusion.based.image.inpainting","name":"mask-guided diffusion-based image inpainting","description":"Implements inpainting by conditioning the Qwen diffusion model on both a text prompt and a binary mask, where masked regions are iteratively denoised from noise while unmasked regions are frozen or gently guided to maintain consistency with the original image. The process uses classifier-free guidance to balance adherence to the text prompt against preservation of the original image context. LoRA weights modulate the diffusion process to specialize the model for specific editing tasks without altering the base inpainting mechanism.","intents":["Replace or modify content within a masked region while preserving the surrounding image","Inpaint missing or corrupted areas of an image based on a text description","Maintain photorealistic consistency between edited and original regions","Control the strength of edits through guidance scale and LoRA weight parameters"],"best_for":["Image restoration and content-aware fill workflows","Object replacement and removal tasks","Photorealistic image editing where boundary blending is critical"],"limitations":["Inpainting quality depends heavily on mask precision; soft or feathered masks may cause visible seams","Diffusion-based inpainting is slower than GAN-based alternatives (typically 5-30 seconds per image)","Semantic understanding is limited to text prompt; no object detection or automatic mask generation","Boundary artifacts may occur if the prompt conflicts with surrounding image content","Requires careful tuning of guidance scale to balance prompt adherence vs. consistency"],"requires":["Input image in standard format","Binary or soft mask defining edit region (0-255 or 0-1 range)","Text prompt describing desired content","Diffusion model (Qwen) with inpainting capability","GPU for inference (CPU inference is prohibitively slow)"],"input_types":["image (RGB or RGBA)","binary mask (grayscale, 0-255 or 0-1)","text (natural language prompt)","numeric (guidance scale, number of diffusion steps)"],"output_types":["image (inpainted result)","metadata (inference time, guidance parameters used)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-prithivmlmods--qwen-image-edit-2511-loras-fast__cap_4","uri":"capability://automation.workflow.fast.inference.optimization.through.model.quantization.and.caching","name":"fast inference optimization through model quantization and caching","description":"The 'Fast' variant applies inference optimizations including model quantization (likely INT8 or FP16), attention computation caching, and LoRA weight pre-loading to reduce latency. The system may use techniques like flash attention, KV-cache reuse across diffusion steps, or quantized LoRA weights to minimize memory bandwidth and computation. These optimizations are transparent to the user but enable faster edit cycles on resource-constrained hardware.","intents":["Get faster feedback on image edits to improve iteration speed","Run inference on lower-spec GPUs or CPU-only environments","Reduce cost of inference on cloud platforms by lowering compute requirements","Enable real-time or near-real-time editing workflows"],"best_for":["Interactive design tools where latency directly impacts user experience","Cost-sensitive deployments on cloud platforms","Edge devices or resource-constrained environments"],"limitations":["Quantization may reduce edit quality or introduce subtle artifacts, especially with aggressive quantization levels","Caching strategies assume similar input patterns; unusual masks or prompts may not benefit from optimizations","Memory savings come at the cost of slightly reduced model expressiveness","Optimization techniques are model-specific; may not transfer to other diffusion architectures"],"requires":["Quantized model weights or quantization-aware training","GPU with support for optimized operations (e.g., flash attention support)","Sufficient RAM for KV-cache storage (typically 2-4GB for typical batch sizes)"],"input_types":["image","mask","text prompt","LoRA adapter selection"],"output_types":["image (edited result)","metadata (inference time, quantization level used)"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-prithivmlmods--qwen-image-edit-2511-loras-fast__cap_5","uri":"capability://tool.use.integration.batch.image.editing.via.api.or.programmatic.interface","name":"batch image editing via api or programmatic interface","description":"Exposes the LoRA-based image editing pipeline through a programmatic API (likely REST or gRPC) that accepts batches of images with corresponding masks and prompts, processes them sequentially or in parallel, and returns edited images. The API abstracts away Gradio UI concerns and enables integration into larger workflows, CI/CD pipelines, or batch processing jobs. Requests include image data, mask, prompt, LoRA adapter selection, and optional inference parameters.","intents":["Process multiple images in batch without manual UI interaction","Integrate image editing into automated content pipelines","Build custom applications on top of the editing capability","Programmatically control inference parameters and LoRA selection"],"best_for":["Developers building image editing into larger applications","Content production teams running batch editing jobs","Researchers experimenting with different LoRA combinations programmatically"],"limitations":["API availability depends on HuggingFace Spaces infrastructure; no SLA or uptime guarantee","Batch processing may queue requests; latency depends on server load","No persistent job tracking or result storage; results must be downloaded immediately","API documentation may be minimal; requires reverse-engineering from Gradio interface or source code"],"requires":["API endpoint URL (likely derived from HuggingFace Spaces URL)","Image data in supported format (base64-encoded or multipart upload)","Mask and prompt data","Optional: API key if authentication is required"],"input_types":["image (base64 or binary)","mask (base64 or binary)","text (prompt)","categorical (LoRA selection)"],"output_types":["image (edited result, base64 or binary)","metadata (inference time, parameters used)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":21,"verified":false,"data_access_risk":"high","permissions":["Input image in standard formats (PNG, JPG, WebP)","Text prompt describing desired edit","Binary mask or region coordinates specifying edit area","Pre-loaded LoRA weights compatible with Qwen image model","GPU with sufficient VRAM (typically 8GB+ for inference)","Pre-trained LoRA weight files in compatible format (typically .safetensors or .pt)","LoRA registry or manifest defining available adapters and their metadata","Base Qwen model loaded in memory","Mechanism to inject LoRA weights into model's linear layers (e.g., via peft library)","Web browser with JavaScript enabled"],"failure_modes":["LoRA adapters are task-specific; editing quality depends on which LoRA weights are loaded and their training data","Mask quality and precision directly impact edit boundaries; imprecise masks cause artifacts at region edges","No built-in semantic understanding of image content; relies entirely on text prompt + mask combination","Batch editing of multiple regions in sequence may accumulate artifacts from previous edits","LoRA composition is additive; conflicting adapters may produce unpredictable results","No automatic conflict detection between incompatible LoRA weights","Switching LoRAs requires recomputation of attention layers; not zero-cost but faster than full model reload","Limited to LoRA adapters; other fine-tuning methods (QLoRA, full LoRA) not supported","Gradio UI is stateless between sessions; no persistent editing history or project management","Mask drawing tools in Gradio are basic; complex masks require external tools and file upload","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--qwen-image-edit-2511-loras-fast","compare_url":"https://unfragile.ai/compare?artifact=prithivmlmods--qwen-image-edit-2511-loras-fast"}},"signature":"ymWCnFyNUGHVGQnfWtAC4uuWFVm5lCPj+r42Og3k9Bh9fMVlNnI5zLPUGCN6pspHIv71Tdc8W+eXYcm4Drq4Aw==","signedAt":"2026-06-20T11:07:34.422Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/prithivmlmods--qwen-image-edit-2511-loras-fast","artifact":"https://unfragile.ai/prithivmlmods--qwen-image-edit-2511-loras-fast","verify":"https://unfragile.ai/api/v1/verify?slug=prithivmlmods--qwen-image-edit-2511-loras-fast","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"}}