Scribble Detection

  • HED
  • PiDiNet
  • Fake Scribble

Extract organic hand-drawn sketch outlines and soft contours from photos to guide downstream ControlNet generation

Extract scribble map
Scribble Detection

How Scribble Detection works

Transforming any photograph or rendering into an expressive scribble sketch takes three simple configuration steps.

Upload reference image

Upload reference image

Provide a clear photo, render, or character illustration with defined subject contours.

Select architecture and safety mode

Select architecture and safety mode

Choose HED for textured gradient strokes or PiDi for clean line art, and toggle Safe Mode to control edge quantization.

Generate soft-edge scribble map

Generate soft-edge scribble map

Receive a high-contrast chalk-and-marker outline map ready to pipe directly into ControlNet workflows.

What Scribble Detection is good at

HED and PiDiNet architectures capture natural stroke weights and soft gradients, turning complex visual references into flexible ControlNet conditioning maps.

HED Soft-Edge Extraction

HED Soft-Edge Extraction

Capture organic stroke weights and subtle grayscale gradations using Holistically-Nested Edge Detection, preserving delicate hair strands and fabric folds.

PiDi Noise-Filtered Outlines

PiDi Noise-Filtered Outlines

Deploy Pixel Difference Networks to strip away low-level background noise and deliver clean, continuous marker curves for complex compositions.

Safe Mode Quantization

Safe Mode Quantization

Toggle Safe Mode to quantize soft grayscales into discrete marker steps, preventing downstream ControlNet models from hallucinating faint gradients into physical geometry.

Organic ControlNet Conditioning

Organic ControlNet Conditioning

Produce pliable hand-drawn sketch maps that grant generative diffusion pipelines creative latitude for recoloring, restyling, and pose-guided rendering.

Made with Scribble Detection

Explore how extracted scribble sketches retain core structural geometry while granting generative models full stylistic freedom across products, fashion, and cinema.

Heavy equipment contours extracted from documentary archival footage

Heavy equipment contours extracted from documentary archival footage

Clean structural curves isolated for furniture restyling workflows

Clean structural curves isolated for furniture restyling workflows

Fluid human posture transformed into dynamic chalk-like marker strokes

Fluid human posture transformed into dynamic chalk-like marker strokes

Hard-surface product silhouettes simplified into guiding edge maps

Hard-surface product silhouettes simplified into guiding edge maps

Architectural volumes converted to loose structural sketch boundaries

Architectural volumes converted to loose structural sketch boundaries

What people build with Scribble Detection

Concept artists, fashion designers, and visual effects directors use scribble detection to lock down pose and proportion while reimagining style from scratch.

Fashion Concept Restyling

01

Extract fluid fabric drapery and mannequin silhouettes from lookbooks to restyle garments, textiles, and patterns without losing organic posture.

Industrial Design Iteration

02

Turn product mockups and physical prototypes into loose scribble maps to explore novel CMF concepts rapidly in ControlNet.

Storyboarding and Keyframe Guidance

03

Convert live-action film stills into hand-drawn scribble guides to maintain spatial blocking while reimagining cinematic lighting and artistic styles.

Album Art and Poster Illustration

04

Transform complex photographic imagery into graphic marker sketches to build high-impact print layouts, screenprints, and illustrated record sleeves.

Architectural Concept Ideation

05

Translate rough volume mockups into soft scribble maps, giving generative models room to explore facade materials, vegetation, and lighting moods.

Frequently Asked Questions

Scribble Detection uses HED (Holistically-Nested Edge Detection) and PiDiNet (Pixel Difference Networks) algorithms to generate soft, sketch-like edge maps for ControlNet. While traditional edge preprocessors produce rigid binary outlines, these models extract gradient-rich, organic contours that resemble hand-drawn marker or chalk sketches.

Use scribble detection when your downstream generation requires creative flexibility rather than strict pixel-level alignment. Scribble maps excel at organic subjects like human poses, draped fabric, character hair, and conceptual sketches where hard-edge detectors like Canny or Lineart produce overly stiff, mechanical results.

Safe Mode quantizes soft grayscale gradients into discrete marker steps to prevent downstream models from over-interpreting subtle shadows as physical objects. Enabling Safe Mode creates a cleaner, high-contrast sketch suitable for busy scenes, while disabling it captures delicate, low-contrast textures like fine hair or translucent cloth.

Avoid using scribble detection for precise geometric borders, vector graphics, typography, or architectural CAD mockups. The preprocessor intentionally produces loose, stylized strokes with variable weights, which will introduce organic distortion into rigid technical designs.

Switch the detector model from HED to PiDi if your input contains detailed background clutter. PiDiNet automatically filters out high-frequency low-level noise to deliver clean, continuous curves, whereas HED is designed to preserve micro-textures and soft gradient shading.

Try Scribble Detection on Fuser

Extract organic hand-drawn sketch outlines and soft contours from photos to guide downstream ControlNet generation

Extract scribble map