ControlNet

Neural network architecture enabling precise spatial control over image generation

Image GenerationFree

ControlNet is a neural network architecture and technique developed by Lvmin Zhang that adds precise spatial conditioning to diffusion models, allowing users to control image generation using edge maps, depth maps, pose skeletons, segmentation masks, and other structural inputs. This enables consistent character poses, maintaining scene layouts, and architectural precision in AI-generated images. As a widely-used extension for Stable Diffusion, ControlNet revolutionized how artists use AI image generation for controlled, professional outputs. Artists, designers, and animators use ControlNet for production-quality image generation with spatial precision.

Key Features

• ✓Pose control
• ✓Depth conditioning
• ✓Edge detection
• ✓Segmentation input
• ✓Stable Diffusion integration