technique

ControlNet

ControlNet is a neural-network architecture that conditions a diffusion image model on extra spatial inputs — edges, depth, pose, segmentation — for precise control over output structure.

ControlNet (Zhang et al., 2023) adds a parallel conditioning branch to a pretrained diffusion model so it can accept a structural hint alongside the text prompt. Common control types: Canny edges, depth maps, OpenPose skeletons, normal maps, scribbles, line art. The model then generates images that match both the text prompt and the spatial structure. ControlNet is the production workflow for AI photography, AI animation, and architectural visualisation — anywhere the composition has to be exact. The richest ControlNet ecosystem is on Stable Diffusion 3.5; Flux ControlNets are catching up; Midjourney has no first-party ControlNet.

When to use controlnet

Common mistakes

FAQ

What is controlnet?

ControlNet is a neural-network architecture that conditions a diffusion image model on extra spatial inputs — edges, depth, pose, segmentation — for precise control over output structure.

When should I use controlnet?

Locking a specific pose, edge map, or depth structure across generations. Architectural visualisation, product photography, character keyframing.

What are the most common mistakes with controlnet?

Using a misaligned control input — produces broken hybrid output. Combining too many ControlNets at once — model loses coherence.

Sources

Last updated: 2026-06-01. Raw markdown: https://promtable.com/glossary/controlnet.md.