Comparative Evaluation of ControlNet Variants for Structured Image Generation in Diffusion Models
Abstract
Text-to-image diffusion models like Stable Diffusion have gotten really good at turning text prompts into impressive images. But just using the prompt doesn’t always let you control where objects go, how they’re arranged, or how consistent the poses look. That makes things tricky when you need structure like for design work or anything that needs a specific layout. ControlNet steps in here, adding extra inputs that steer the image generation process with details like edges, depth, human poses, or even semantic segmentation. It’s a big improvement, but honestly, there hasn’t been much head-to-head comparison of all the different versions of ControlNet in the research so far.So, in this paper, we dig into that gap. We review and compare the main ControlNet variants that work with Stable Diffusion: Canny edge-based, scribble-based, OpenPose-based, depth-based, and segmentation-based controls. We test them all under the same conditions—same prompts, same random seeds, same settings—so the comparison’s fair. For evaluation, we use numbers (like SSIM, LPIPS, and CLIP-based text–image alignment scores) and just plain looking at the images. We also check how long each variant takes to run, since speed matters too.What did we find? There’s a clear trade-off: edge and depth controls hang onto structure better, but if you want more flexibility or creativity, scribble and pose controls do a better job. Based on what we saw, we lay out some practical advice on picking the right…
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