ControlNet is a neural network structure designed to provide additional control over text-to-image diffusion models by incorporating extra conditions. It achieves this by duplicating the weights of neural network blocks into a 'locked' copy and a 'trainable' copy. The 'trainable' copy learns the new condition, while the 'locked' copy preserves the original model's integrity. This architecture allows for the integration of various conditioning inputs, such as edges, depth, segmentation, and human poses, enabling more precise and consistent image generation without compromising the quality of the pretrained diffusion models.