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SDXL Image Generation Guided by a Canny Edge Map

High-fidelity image generation from a canny edge map + text prompt using SDXL. A dedicated canny ControlNet, sharper structural adherence than the general-purpose Union variant for edge-based tasks. Pair with the Canny Edge Annotator to turn a source photo into the edge map this model expects.

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What it does

ControlNet XL Canny generates a new image that matches the outlines of a canny edge map while following a text prompt for the style, mood, and content. It is a ControlNet adapter on top of Stable Diffusion XL (SDXL): instead of leaving SDXL to imagine the layout from scratch, you give it the edge structure of a reference photo (or a sketch / line art), and SDXL fills in the rest. The result is image generation with strong structural control, same composition, completely new style. This is a dedicated canny model (trained only for canny), which gives sharper structural adherence than the more general ControlNet-Union variant on edge-based tasks. The xinsir release was trained on 10M+ carefully captioned images and outperforms other open-source canny models (diffusers/controlnet-canny-sdxl-1.0, TheMistoAI/MistoLine) on both aesthetic score and perceptual control.

Problem it solves

• Composition-locked generation – Keep the exact layout, perspective, and outlines of a reference image while changing everything else (style, lighting, materials, era)
• Sketch / line art to image – Turn a hand-drawn sketch or a clean line drawing into a fully rendered image
• Style transfer with structure intact – Reimagine a scene in watercolor, anime, photorealism, etc., without losing the original geometry
• Architectural / product visualization – Generate alternate renderings of a building, room, or product where the structural lines must be respected
• Stronger control than Union-Canny – When the structural fidelity of edges matters most, this dedicated model adheres more sharply than the general Union variant
• High-resolution output – Native 1024×1024 generation that matches SDXL quality

Input/Output

• Input:
  • Canny edge map (grayscale image with white edges on black background) — typically produced by the Canny Edge Annotator node from a source photo
  • Text prompt describing the desired image (style, content, mood)
  • Negative prompt (optional) — what to avoid (e.g., "blurry, low quality, watermark")
• Options: Strength of canny edge guidance (0.0 – 2.0, default 1.0)
  • 0.0 = ignore the edges entirely · 2.0 = strictly follow them
  • 0.8 – 1.2 works well for most structural tasks
  • Number of diffusion steps (20 – 30, default 25)
    • 20 – 30 gives good quality
  • Classifier-free guidance (CFG) scale (default 5)
    • 5.0 – 7.5 is typical for SDXL
  • Random seed (-1 for random, or a fixed integer for reproducibility)
  • Output image width (512 / 768 / 1024) — the control image is resized to match
  • Output image height (512 / 768 / 1024) — the control image is resized to match
  • Advanced — Negative prompt (leave empty for no negative guidance)
• Output: Generated image
  • Up to 1024 × 1024 (native SDXL resolution; bucket resolutions also supported)
  • Photorealistic or stylized depending on the prompt
  • Structure follows the canny edge map; everything else follows the text prompt

Accuracy & Speed

Technical Details

Compliance & Provenance