{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_jpgrm","slug":"jpgrm","name":"JPGRM","type":"product","url":"https://jpgrm.com","page_url":"https://unfragile.ai/jpgrm","categories":["image-generation"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_jpgrm__cap_0","uri":"capability://image.visual.brush.based.interactive.object.selection.and.masking","name":"brush-based interactive object selection and masking","description":"Provides a freehand brush tool for users to paint selections directly on the image canvas, converting brush strokes into binary masks that define removal regions. The interface likely uses canvas-based stroke detection (tracking mouse/touch events) to build a raster mask in real-time, which is then passed to the inpainting backend. This approach prioritizes ease-of-use over precision, requiring minimal training for casual users.","intents":["I need to quickly select an object in a photo without learning complex selection tools","I want real-time visual feedback as I paint over the area I want removed","I need a mobile-friendly way to mark regions for removal without desktop software"],"best_for":["casual users and hobbyists without Photoshop experience","mobile users editing photos on tablets or phones","rapid prototyping workflows where speed matters more than pixel-perfect precision"],"limitations":["Brush-based selection is inherently imprecise at object boundaries; fine details and hair/fur are difficult to isolate cleanly","No support for advanced selection refinement (feathering, edge detection, or intelligent boundary detection)","Stroke-based masks cannot easily be edited after initial painting without restarting the selection"],"requires":["Modern web browser with HTML5 Canvas support (Chrome, Firefox, Safari, Edge)","Mouse, trackpad, or touch input device","Minimum 2MB free memory for canvas rendering"],"input_types":["image (JPEG, PNG, WebP)","user brush strokes (mouse/touch coordinates and pressure)"],"output_types":["binary mask (raster)","coordinates for inpainting region"],"categories":["image-visual","user-interface"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_jpgrm__cap_1","uri":"capability://image.visual.resolution.preserving.inpainting.with.diffusion.based.fill","name":"resolution-preserving inpainting with diffusion-based fill","description":"Applies a diffusion model (likely Stable Diffusion or similar open-source variant) to the masked region, generating contextually coherent content that matches the surrounding image without downsampling the original resolution. The architecture likely encodes the full-resolution image and mask, runs the diffusion process at native resolution or with minimal upsampling, and blends the inpainted region back into the original. This preserves fine details in non-masked areas.","intents":["I need to remove an object but keep the rest of the photo sharp and detailed","I want output that I can print or use in professional contexts without quality loss","I need to remove objects from high-resolution photos (4K, 8K) without degradation"],"best_for":["users with high-resolution source images (2K+) who cannot tolerate downsampling","photographers and content creators requiring publication-ready output","users comparing against free tools that aggressively compress to reduce server load"],"limitations":["Struggles with complex, textured backgrounds (brick, foliage, water); diffusion models tend to hallucinate or blur texture patterns","Processing time increases significantly with resolution; 4K images may take 30-60 seconds vs. 5-10 seconds for 1080p","No multi-pass refinement or iterative inpainting; single-pass generation can leave visible seams or unnatural transitions","Inpainting quality degrades when the masked region is large relative to image size (>30% of pixels)"],"requires":["GPU-accelerated inference backend (NVIDIA CUDA or AMD ROCm recommended for <30s processing)","Minimum 4GB VRAM for full-resolution processing; 8GB+ recommended for 4K","Stable Diffusion model weights (~4GB) or equivalent diffusion model"],"input_types":["image (JPEG, PNG, WebP) up to 8K resolution","binary mask (same dimensions as input image)"],"output_types":["image (JPEG, PNG, WebP) at original input resolution","optional confidence map showing inpainting certainty per pixel"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_jpgrm__cap_2","uri":"capability://image.visual.server.side.gpu.accelerated.inpainting.inference","name":"server-side gpu-accelerated inpainting inference","description":"Executes the diffusion model on remote GPU infrastructure (likely NVIDIA A100 or similar), receiving the masked image and returning inpainted output. The backend likely batches requests, manages model caching, and implements request queuing to handle concurrent users. This architecture trades latency for scalability and cost-efficiency compared to client-side inference.","intents":["I want to remove objects without installing software or GPU drivers locally","I need consistent, reproducible results across different devices","I want the service to handle heavy computation without draining my device's battery"],"best_for":["web-based users without local GPU access","mobile users who cannot run inference locally","teams needing centralized, auditable processing"],"limitations":["Network latency adds 500ms-2s per request (upload + processing + download)","Server-side processing is slower than local GPU for single images; unclear if batching is implemented","Free tier likely has rate limiting or queue delays during peak hours","No offline capability; requires active internet connection","Privacy concern: images are uploaded to remote servers; no guarantee of deletion after processing"],"requires":["Active internet connection (minimum 5 Mbps for reasonable upload/download speeds)","JPGRM account (free or paid tier)","API endpoint access (browser-based or REST API if available)"],"input_types":["image (JPEG, PNG, WebP) with binary mask","optional parameters (inference steps, guidance scale, seed)"],"output_types":["inpainted image (JPEG, PNG, WebP)","processing metadata (inference time, model version)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_jpgrm__cap_3","uri":"capability://automation.workflow.freemium.access.with.watermark.free.free.tier.output","name":"freemium access with watermark-free free tier output","description":"Implements a freemium pricing model where free-tier users can perform unlimited object removal without watermarks applied to output images. The backend likely tracks usage via session cookies or anonymous user IDs, enforcing soft limits (e.g., file size caps, monthly processing quotas) without hard paywalls. Paid tiers likely unlock higher resolution processing, faster queue priority, or batch processing capabilities.","intents":["I want to try object removal without paying upfront","I need watermark-free output for casual social media use","I want to evaluate the tool's quality before committing to a subscription"],"best_for":["casual users and hobbyists with low-volume editing needs","price-sensitive users comparing multiple tools","content creators testing the tool for portfolio work"],"limitations":["Free tier limitations are not clearly documented (file size caps, monthly quotas, resolution limits are unclear)","No persistent account or project history on free tier; each session is isolated","Free tier likely has longer processing queues during peak hours","Unclear if free-tier data is retained for model training or analytics"],"requires":["Web browser (no account required for basic usage)","Optional: JPGRM account for saved projects or higher quotas"],"input_types":["image (JPEG, PNG, WebP)"],"output_types":["watermark-free image (JPEG, PNG, WebP)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_jpgrm__cap_4","uri":"capability://image.visual.browser.based.real.time.image.preview.and.editing","name":"browser-based real-time image preview and editing","description":"Renders the original image and inpainted result in the browser using HTML5 Canvas or WebGL, allowing users to see before/after comparisons and adjust brush selections without server round-trips. The interface likely implements a split-view or toggle mechanism to compare masked regions with inpainted output. This provides immediate visual feedback and reduces iteration time.","intents":["I want to see the result before committing to the removal","I need to compare the original and edited versions side-by-side","I want to refine my selection if the first attempt doesn't look right"],"best_for":["users who prefer interactive, immediate feedback over batch processing","mobile users editing on tablets with touch interfaces","users with limited technical knowledge who benefit from visual confirmation"],"limitations":["Browser rendering performance degrades with very large images (>8K); may cause UI lag or crashes on low-end devices","No persistent undo/redo history; refreshing the page loses all edits","Limited color correction or post-processing tools; only object removal is available","No layer-based editing; all changes are flattened into a single output"],"requires":["Modern web browser with HTML5 Canvas or WebGL support","Minimum 2GB RAM for smooth rendering of high-resolution images","GPU acceleration recommended for 4K+ images"],"input_types":["image (JPEG, PNG, WebP)","user interactions (brush strokes, zoom, pan)"],"output_types":["rendered preview (Canvas/WebGL framebuffer)","downloadable image (JPEG, PNG, WebP)"],"categories":["image-visual","user-interface"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_jpgrm__cap_5","uri":"capability://image.visual.artifact.prone.inpainting.on.complex.backgrounds","name":"artifact-prone inpainting on complex backgrounds","description":"The diffusion-based inpainting model struggles with textured, complex, or non-uniform backgrounds (brick, foliage, water, fabric patterns), often producing visible artifacts, blur, or hallucinated textures that don't match the surrounding context. This is a known limitation of single-pass diffusion inpainting; the model lacks sufficient context or guidance to reconstruct fine texture details. The architecture does not implement multi-pass refinement, context-aware guidance, or texture synthesis to mitigate this.","intents":["I need to remove an object from a photo with a complex background","I want the inpainted region to seamlessly blend with surrounding textures","I need professional-grade results on challenging images"],"best_for":["NOT suitable for complex backgrounds; best for simple, uniform backgrounds","NOT suitable for professional photography or publication-ready output on challenging images"],"limitations":["Visible artifacts, blur, or texture mismatches on brick, foliage, water, or patterned backgrounds","No multi-pass refinement or iterative inpainting to improve results","No texture synthesis or context-aware guidance to match surrounding patterns","Large masked regions (>30% of image) are more prone to artifacts","No manual healing or blending tools to fix inpainting errors"],"requires":["Simple, uniform backgrounds for acceptable results","Small to medium masked regions (<30% of image)"],"input_types":["image with complex background (JPEG, PNG, WebP)"],"output_types":["inpainted image with visible artifacts or blur (JPEG, PNG, WebP)"],"categories":["image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_jpgrm__cap_6","uri":"capability://image.visual.limited.editing.capabilities.beyond.object.removal","name":"limited editing capabilities beyond object removal","description":"The tool is narrowly focused on object removal via inpainting and does not provide additional editing features such as inpainting variations, healing tools, clone stamp, content-aware fill adjustments, or post-processing (color correction, sharpening, etc.). The architecture is a single-purpose tool optimized for one task, not a general-purpose image editor.","intents":["I need to remove an object and then fine-tune the result","I want to try multiple inpainting variations and pick the best one","I need to fix artifacts or blend the inpainted region more naturally"],"best_for":["NOT suitable for users needing comprehensive editing capabilities","NOT suitable for professional workflows requiring post-processing"],"limitations":["No inpainting variations or multi-sample generation; single deterministic output per mask","No healing, clone stamp, or content-aware fill tools","No color correction, sharpening, or other post-processing","No layer-based editing or non-destructive workflows","No batch processing or automation for multiple images"],"requires":["Acceptance of single-purpose tool limitations","External image editor (Photoshop, GIMP) for post-processing"],"input_types":["image (JPEG, PNG, WebP)"],"output_types":["inpainted image (JPEG, PNG, WebP)"],"categories":["image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_jpgrm__cap_7","uri":"capability://automation.workflow.slow.processing.times.with.unclear.performance.characteristics","name":"slow processing times with unclear performance characteristics","description":"The tool exhibits slow processing times (exact latency not documented) compared to modern alternatives, likely due to server-side GPU inference overhead, network latency, and lack of optimization for common image sizes. The architecture does not appear to implement request batching, model caching, or progressive rendering to improve throughput. Free-tier users likely experience longer queue delays during peak hours.","intents":["I need to remove an object quickly without waiting","I want to process multiple images in a reasonable timeframe","I need predictable performance for batch workflows"],"best_for":["NOT suitable for time-sensitive workflows or real-time applications"],"limitations":["Processing time is slow and not clearly documented; likely 30-60 seconds for 4K images","No clear SLA or performance guarantees for free tier","Free-tier users likely experience queue delays during peak hours","No batch processing or parallel processing for multiple images","No progressive rendering or streaming output; full image must be processed before display"],"requires":["Patience for processing delays","Acceptance of variable performance based on server load"],"input_types":["image (JPEG, PNG, WebP)"],"output_types":["inpainted image (JPEG, PNG, WebP) after processing delay"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_jpgrm__cap_8","uri":"capability://automation.workflow.undocumented.free.tier.usage.limits.and.quotas","name":"undocumented free-tier usage limits and quotas","description":"The free tier has unclear limitations on file size, monthly processing quotas, or image resolution, making it difficult for users to predict when they will hit limits or be forced to upgrade. The backend likely enforces soft limits (e.g., max 10 images/month, max 5MB file size) but does not communicate these clearly in the UI or documentation. This creates friction and reduces user trust.","intents":["I want to understand the free tier limitations before using the tool","I need to know if I will hit usage limits for my workflow","I want to plan my usage to avoid unexpected paywalls"],"best_for":["NOT suitable for users who need transparent pricing and clear limits"],"limitations":["Free-tier limits are not clearly documented (file size, monthly quota, resolution)","Users may hit limits unexpectedly and be forced to upgrade","No clear communication of remaining quota or usage statistics","Unclear if limits are per-session, per-day, or per-month","No API documentation for programmatic usage or quota checking"],"requires":["Acceptance of unclear limits and potential surprise paywalls"],"input_types":["image (JPEG, PNG, WebP)"],"output_types":["inpainted image (JPEG, PNG, WebP) or quota-exceeded error"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"low","permissions":["Modern web browser with HTML5 Canvas support (Chrome, Firefox, Safari, Edge)","Mouse, trackpad, or touch input device","Minimum 2MB free memory for canvas rendering","GPU-accelerated inference backend (NVIDIA CUDA or AMD ROCm recommended for <30s processing)","Minimum 4GB VRAM for full-resolution processing; 8GB+ recommended for 4K","Stable Diffusion model weights (~4GB) or equivalent diffusion model","Active internet connection (minimum 5 Mbps for reasonable upload/download speeds)","JPGRM account (free or paid tier)","API endpoint access (browser-based or REST API if available)","Web browser (no account required for basic usage)"],"failure_modes":["Brush-based selection is inherently imprecise at object boundaries; fine details and hair/fur are difficult to isolate cleanly","No support for advanced selection refinement (feathering, edge detection, or intelligent boundary detection)","Stroke-based masks cannot easily be edited after initial painting without restarting the selection","Struggles with complex, textured backgrounds (brick, foliage, water); diffusion models tend to hallucinate or blur texture patterns","Processing time increases significantly with resolution; 4K images may take 30-60 seconds vs. 5-10 seconds for 1080p","No multi-pass refinement or iterative inpainting; single-pass generation can leave visible seams or unnatural transitions","Inpainting quality degrades when the masked region is large relative to image size (>30% of pixels)","Network latency adds 500ms-2s per request (upload + processing + download)","Server-side processing is slower than local GPU for single images; unclear if batching is implemented","Free tier likely has rate limiting or queue delays during peak hours","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.67,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:31.446Z","last_scraped_at":"2026-04-05T13:23:42.560Z","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=jpgrm","compare_url":"https://unfragile.ai/compare?artifact=jpgrm"}},"signature":"lyKWHaWo6HZOP9TYDGqjxVT1uzR0P5k5UwQxrzO/cbAzNndCATPg6pnCZvXv3GEL00ftt8tN4pzkpTF3iIWLCg==","signedAt":"2026-06-22T02:36:17.655Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/jpgrm","artifact":"https://unfragile.ai/jpgrm","verify":"https://unfragile.ai/api/v1/verify?slug=jpgrm","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"}}