# Scribble Detection Canonical page: https://fuser.studio/models/scribble-detection Scribble Detection in Fuser turns a photo into a hand-drawn style outline with HED or PiDi. Feed it to ControlNet to restyle an image loosely. ## Specs | Spec | Value | | --- | --- | | Versions | Holistically-Nested Edge Detection, PiDi | | Inputs | Image | | Output | image | | Cost | 5 credits per run | Extract organic contour maps and hand-drawn line sketches from source photos for flexible ControlNet spatial conditioning [Extract scribble map](https://app.fuser.studio) ## How Scribble Detection works Convert photographic source images into expressive outline conditioning maps in three straightforward actions. ### Upload source photograph Provide a well-lit photograph with clear separation between the subject and background. ### Select detection algorithm Choose PiDi for clean ink-like edges or HED for smooth volumetric shading contours. ### Configure edge safety Toggle Safe Mode to quantize gradients and eliminate source color bleed in downstream renders. ## What Scribble Detection is good at High-contrast contour extraction powered by dual edge-detection algorithms and edge-quantization controls. ### Dual edge-detection engines Switch between PiDi for crisp, low-noise ink outlines and HED for soft, charcoal-like gradient maps that capture subtle volumetric depth. ### Quantized Safe Mode processing Enable Safe Mode to quantize continuous edge gradients into discrete stepped strokes, preventing source color bleed and ghosting in downstream generative renders. ### Volumetric shape preservation Preserve essential structural envelopes and volumetric boundaries from complex photographs, giving generative pipelines layout anchors without locking in surface textures. ### Flexible spatial conditioning Isolate organic sketch boundaries that retain subject framing and dynamic poses, providing high-level spatial guidance for generative re-renderings. ## Made with Scribble Detection Explore how organic sketch maps capture photographic composition while leaving textures open for creative reimagining. ### Loose fabric drape and dynamic silhouette extraction ### Sharp mechanical boundary extraction with PiDiNet ### Atmospheric motion and framing guidance ### Organic sculptural contour capture with HED ### Quantized outline definition for product restyling ## What people build with Scribble Detection Creative directors, visual artists, and designers rely on scribble maps to preserve composition while reinventing visual style. ### Fashion lookbook restyling Convert runway model photography into organic scribble maps to explore novel fabric textures, colorways, and seasonal styling while locking silhouette poses. ### Product concept iteration Transform physical mockups or prototype photos into clean contour guides to experiment with exotic finishes, metals, and composite materials. ### Architectural visualization Extract structural envelopes from site photos to render alternate weather conditions, evening illumination, or avant-garde cladding materials. ### Cinematic storyboarding Turn production stills and location scouts into expressive scribble maps for rapid scene restyling, lighting redesigns, and mood exploration. ### Brand campaign adaptations Capture the core geometry of brand assets to rapidly generate themed seasonal campaign imagery without distorting hero product placement. ## Frequently Asked Questions ### What is the difference between the PiDi and HED detection models? PiDi (Pixel Difference Networks) extracts crisp, clean outlines with minimal background noise, making it the best default choice for structural layout control. HED (Holistically-Nested Edge Detection) captures multi-scale, softer gradient maps resembling charcoal sketches, preserving subtle volumetric forms and facial expressions at the cost of slight background noise. ### What does Safe Mode do in Scribble Detection? Safe Mode quantizes smooth grayscale edge gradients into stepped, distinct outlines resembling felt-tip marker strokes. This pre-quantization prevents ghosting artifacts where colors and textures from the source photograph bleed into downstream generations. Disable Safe Mode when you need maximum fine detail, as quantization can lead downstream models to hallucinate textures across gradient gaps. ### What types of source images produce the best results? Well-lit photographs with strong subject-to-background contrast yield the cleanest scribble maps. Avoid uploading existing black-and-white sketches or clean line art, as re-processing them causes double-edge detection artifacts and muddy outlines. ### When should I use Scribble Detection instead of Canny or Lineart? Reach for Scribble Detection when you want to restyle a composition while allowing downstream generative models full freedom to reinvent textures, lighting, and materials. Switch to Canny or Lineart detectors when you require rigid, millimeter-precise edge tracking for architectural blueprints or typography. ## Try Scribble Detection on Fuser Extract organic contour maps and hand-drawn line sketches from source photos for flexible ControlNet spatial conditioning [Extract scribble map](https://app.fuser.studio) ## Related models - [Gemini Image](https://fuser.studio/models/gemini-image.md) - [GPT Image](https://fuser.studio/models/gpt-image.md) - [Seedream](https://fuser.studio/models/seedream.md) - [FLUX.3 Image](https://fuser.studio/models/flux-3-image.md) - [FLUX.2](https://fuser.studio/models/flux-2.md) - [ESRGAN Upscaler](https://fuser.studio/models/esrgan-upscaler.md) Node reference: [Scribble Detection docs](https://docs.fuser.studio/docs/nodes/image/scribble-detection.md)