ControlNet is a neural network architecture that adds precise spatial control to image generation models by conditioning the generation process on input images that define structural, compositional, or stylistic constraints. It allows creators to guide AI image generation with reference images that specify elements such as edge maps, depth maps, pose structures, or segmentation masks, giving far more control over the output than text prompts alone can provide.

The system works by training additional neural network modules that sit alongside a base diffusion model such as Stable Diffusion, processing control images that define the spatial structure the generated output should follow. For example, feeding a pose skeleton into ControlNet ensures that the generated character matches that exact pose, or providing an edge-detected line drawing ensures the final image respects those structural boundaries. Multiple ControlNet modules can be stacked and used simultaneously, allowing creators to specify pose, depth, and composition all at once for highly directed generation.

ControlNet has become one of the most influential tools in the AI image generation community since its release, particularly for creators who need reliable control over composition and structure rather than leaving those elements up to the model's interpretation. It bridges the gap between the creative freedom of AI generation and the precision required for professional workflows where specific compositional requirements must be met.