{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-space-jasperai--flux.1-dev-controlnet-upscaler","slug":"jasperai--flux.1-dev-controlnet-upscaler","name":"Flux.1-dev-Controlnet-Upscaler","type":"model","url":"https://huggingface.co/spaces/jasperai/Flux.1-dev-Controlnet-Upscaler","page_url":"https://unfragile.ai/jasperai--flux.1-dev-controlnet-upscaler","categories":["image-generation"],"tags":["gradio","upscaler","super-resolution","controlnet","flux.1-dev","region:us"],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-space-jasperai--flux.1-dev-controlnet-upscaler__cap_0","uri":"capability://image.visual.controlnet.guided.image.upscaling.with.structural.preservation","name":"controlnet-guided image upscaling with structural preservation","description":"Combines Flux.1-dev diffusion model with ControlNet conditioning to upscale images while preserving spatial structure and composition. Uses ControlNet as a control signal injected into the diffusion process to guide generation toward maintaining the original image's layout, edges, and semantic content during super-resolution. The architecture chains low-level structural guidance (via ControlNet) with Flux.1-dev's generative capabilities to produce high-fidelity upscaled outputs that respect the input image's geometric constraints.","intents":["Upscale low-resolution images while maintaining exact spatial structure and composition","Enhance image detail without hallucinating or distorting the original layout","Generate high-resolution versions of images with semantic awareness of content positioning"],"best_for":["Content creators needing lossless upscaling with structural fidelity","Developers building image enhancement pipelines requiring composition-aware super-resolution","Teams working with legacy or low-resolution image assets that need detail recovery"],"limitations":["Inference latency likely 30-60 seconds per image due to iterative diffusion sampling (typical for Flux.1-dev)","ControlNet conditioning may over-constrain generation in regions with ambiguous or low-detail content","Upscaling factor appears fixed (likely 2x or 4x) — no dynamic scaling control exposed","Requires GPU memory for Flux.1-dev + ControlNet dual-model inference (~20-24GB VRAM typical)"],"requires":["Input image (PNG, JPG, WebP format)","GPU access (HuggingFace Spaces provides free tier with rate limits)","Web browser or API client for Gradio interface"],"input_types":["image (raster: PNG, JPG, WebP)"],"output_types":["image (raster: PNG or JPG, upscaled resolution)"],"categories":["image-visual","deep-learning-inference"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-jasperai--flux.1-dev-controlnet-upscaler__cap_1","uri":"capability://automation.workflow.gradio.based.web.interface.for.image.upload.and.real.time.preview","name":"gradio-based web interface for image upload and real-time preview","description":"Exposes the upscaling model through a Gradio web UI hosted on HuggingFace Spaces, enabling drag-and-drop image upload, real-time processing feedback, and side-by-side before/after preview. Gradio automatically generates the HTTP interface, handles file serialization, manages session state, and provides browser-based interaction without requiring local GPU or software installation. The interface abstracts the underlying Flux.1-dev + ControlNet inference pipeline into a simple input-output form.","intents":["Upload and upscale images without local GPU or command-line setup","Preview upscaling results immediately in a browser","Share upscaling capability with non-technical users via a public URL"],"best_for":["Non-technical end users wanting quick image upscaling without installation","Developers prototyping image enhancement features before integration","Teams needing a shareable demo or MVP for image upscaling"],"limitations":["Gradio interface adds ~2-5 second overhead for file upload/serialization per request","HuggingFace Spaces free tier enforces rate limiting and session timeouts (typically 48 hours idle)","No batch processing — single image per request","No API authentication or usage tracking — public endpoint vulnerable to abuse/DoS","File size limits enforced by Spaces (typically 100MB max upload)"],"requires":["Web browser with JavaScript enabled","Internet connection to HuggingFace Spaces","Image file (PNG, JPG, WebP) under 100MB"],"input_types":["image (uploaded via browser file picker)"],"output_types":["image (displayed in browser, downloadable)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-jasperai--flux.1-dev-controlnet-upscaler__cap_2","uri":"capability://automation.workflow.batch.image.processing.with.asynchronous.queue.management","name":"batch image processing with asynchronous queue management","description":"Processes multiple image upscaling requests sequentially through a shared GPU queue managed by HuggingFace Spaces infrastructure. Requests are enqueued, processed in order, and results cached or streamed back to clients. The Gradio backend handles concurrent request serialization, GPU memory management, and prevents out-of-memory crashes by queuing excess requests. This enables multiple users to submit images simultaneously without blocking or crashing the inference server.","intents":["Submit multiple images for upscaling without waiting for each to complete","Handle concurrent user requests on a single GPU without server crashes","Monitor processing queue status and estimated wait times"],"best_for":["Shared demo environments with multiple concurrent users","Batch upscaling workflows where users submit images and check back later","Teams evaluating model performance across diverse image datasets"],"limitations":["Sequential GPU processing means total time = sum of individual inference times (no parallelization across GPUs)","Queue wait time grows linearly with number of concurrent users (no priority queuing)","HuggingFace Spaces free tier may evict long-running jobs after 48 hours","No persistent queue — requests lost if Spaces instance restarts","No webhook/callback mechanism — users must poll for results"],"requires":["HuggingFace Spaces infrastructure (provided by Spaces runtime)","Network connectivity to poll queue status"],"input_types":["image (queued for processing)"],"output_types":["image (returned when queue position reached)","queue status (JSON: position, estimated wait time)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-jasperai--flux.1-dev-controlnet-upscaler__cap_3","uri":"capability://image.visual.flux.1.dev.diffusion.model.inference.with.multi.step.sampling","name":"flux.1-dev diffusion model inference with multi-step sampling","description":"Executes the Flux.1-dev text-to-image diffusion model with iterative denoising steps (typically 20-50 steps) to generate or enhance images. The model uses a flow-matching training objective and operates in latent space, progressively refining noise into coherent image features. Each sampling step applies the ControlNet conditioning signal to guide generation toward the structural constraints of the input image, balancing fidelity to the original with detail enhancement.","intents":["Generate high-quality upscaled images with semantic understanding of content","Leverage Flux.1-dev's superior image quality vs older diffusion models (SDXL, SD1.5)","Apply learned priors from Flux.1-dev training to enhance low-resolution inputs"],"best_for":["Developers needing state-of-the-art image generation quality for upscaling","Teams prioritizing output quality over inference speed","Applications where perceptual quality matters more than latency"],"limitations":["Inference time 30-60 seconds per image (vs 5-10 seconds for ESRGAN) due to iterative sampling","Requires 20-24GB GPU VRAM for Flux.1-dev + ControlNet dual-model loading","Sampling is non-deterministic (even with fixed seed, slight variations occur across runs)","Model weights are large (~24GB) — first-run download may take 10+ minutes","No fine-tuning or LoRA support exposed in this demo"],"requires":["GPU with 20GB+ VRAM (A100, H100, RTX 4090, or equivalent)","PyTorch 2.0+ with CUDA support","Flux.1-dev model weights (auto-downloaded from HuggingFace Hub)"],"input_types":["image (low-resolution input for upscaling guidance)"],"output_types":["image (high-resolution upscaled output)"],"categories":["image-visual","deep-learning-inference"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-jasperai--flux.1-dev-controlnet-upscaler__cap_4","uri":"capability://image.visual.controlnet.spatial.conditioning.for.structure.preserving.generation","name":"controlnet spatial conditioning for structure-preserving generation","description":"Injects structural guidance into the Flux.1-dev diffusion process via ControlNet, a lightweight adapter network that conditions each denoising step on the input image's spatial features (edges, depth, pose, or other control signals). ControlNet operates by extracting control embeddings from the input image and concatenating them with the diffusion model's internal representations at multiple scales, enabling fine-grained control over generation without modifying the base model weights. This allows upscaling to respect the original composition while enhancing detail.","intents":["Upscale images while preserving exact spatial layout and edge structure","Prevent hallucination or distortion of object positions during super-resolution","Guide generation toward semantically consistent detail enhancement"],"best_for":["Applications requiring composition-aware upscaling (e.g., product photos, architectural images)","Workflows where spatial fidelity is critical (e.g., medical imaging, technical documentation)","Developers building structure-preserving image enhancement pipelines"],"limitations":["ControlNet conditioning adds ~5-10% latency overhead per inference step","Over-constraining with ControlNet may reduce detail enhancement in ambiguous regions","ControlNet type (edge, depth, pose, etc.) is fixed in this demo — no user control over conditioning mode","Requires additional model weights (~1-2GB per ControlNet variant) loaded alongside Flux.1-dev","No ablation or confidence scoring for how strongly ControlNet influences each region"],"requires":["ControlNet weights compatible with Flux.1-dev (auto-downloaded)","Input image with clear spatial structure (works best with well-defined edges/objects)"],"input_types":["image (source for ControlNet conditioning signal extraction)"],"output_types":["image (upscaled with structure preserved)"],"categories":["image-visual","deep-learning-inference"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-space-jasperai--flux.1-dev-controlnet-upscaler__cap_5","uri":"capability://automation.workflow.huggingface.spaces.deployment.with.automatic.gpu.allocation","name":"huggingface spaces deployment with automatic gpu allocation","description":"Deploys the Flux.1-dev + ControlNet upscaler as a containerized Gradio app on HuggingFace Spaces, which automatically provisions GPU resources, manages dependencies, and handles scaling. Spaces uses Docker containers to isolate the application, automatically pulls model weights from the HuggingFace Hub on first run, and provides a public HTTPS endpoint. The free tier includes ephemeral GPU access with rate limiting; paid tiers offer persistent GPUs and higher concurrency.","intents":["Deploy a working upscaler demo without managing servers or cloud infrastructure","Share the upscaler with a public URL without authentication setup","Iterate on the model/interface without DevOps overhead"],"best_for":["Researchers and developers prototyping models quickly","Teams building demos or MVPs without dedicated DevOps","Open-source projects needing free hosting for community access"],"limitations":["Free tier has 48-hour idle timeout — app shuts down if unused, cold-start adds 30-60 seconds on next request","Rate limiting on free tier (typically 1 request per 5 seconds per user)","No persistent storage — model cache and intermediate results lost on restart","Limited to HuggingFace's supported runtimes (Python 3.9+, specific CUDA versions)","No custom domain or SSL certificate control on free tier","Spaces instances may be evicted during high-traffic periods on free tier"],"requires":["HuggingFace account (free or paid)","Git repository with Gradio app code and requirements.txt","Model weights accessible from HuggingFace Hub (public or with token)"],"input_types":["application code (Python + Gradio)"],"output_types":["public HTTPS endpoint (Spaces URL)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":22,"verified":false,"data_access_risk":"low","permissions":["Input image (PNG, JPG, WebP format)","GPU access (HuggingFace Spaces provides free tier with rate limits)","Web browser or API client for Gradio interface","Web browser with JavaScript enabled","Internet connection to HuggingFace Spaces","Image file (PNG, JPG, WebP) under 100MB","HuggingFace Spaces infrastructure (provided by Spaces runtime)","Network connectivity to poll queue status","GPU with 20GB+ VRAM (A100, H100, RTX 4090, or equivalent)","PyTorch 2.0+ with CUDA support"],"failure_modes":["Inference latency likely 30-60 seconds per image due to iterative diffusion sampling (typical for Flux.1-dev)","ControlNet conditioning may over-constrain generation in regions with ambiguous or low-detail content","Upscaling factor appears fixed (likely 2x or 4x) — no dynamic scaling control exposed","Requires GPU memory for Flux.1-dev + ControlNet dual-model inference (~20-24GB VRAM typical)","Gradio interface adds ~2-5 second overhead for file upload/serialization per request","HuggingFace Spaces free tier enforces rate limiting and session timeouts (typically 48 hours idle)","No batch processing — single image per request","No API authentication or usage tracking — public endpoint vulnerable to abuse/DoS","File size limits enforced by Spaces (typically 100MB max upload)","Sequential GPU processing means total time = sum of individual inference times (no parallelization across GPUs)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.22,"ecosystem":0.48000000000000004,"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":"2026-05-24T12:16:22.766Z","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=jasperai--flux.1-dev-controlnet-upscaler","compare_url":"https://unfragile.ai/compare?artifact=jasperai--flux.1-dev-controlnet-upscaler"}},"signature":"o+XbzZNLEs/62xUyrfa92V4kw6eek3uZFAsyvGJXGkUikIBT0t4EdaXlLUOAdK07qvfKJO0bztAw9mB25pkWCA==","signedAt":"2026-06-22T09:23:45.269Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/jasperai--flux.1-dev-controlnet-upscaler","artifact":"https://unfragile.ai/jasperai--flux.1-dev-controlnet-upscaler","verify":"https://unfragile.ai/api/v1/verify?slug=jasperai--flux.1-dev-controlnet-upscaler","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"}}