ControlNet is a neural network structure to control diffusion models by adding extra conditions. It was developed by the Hugging Face team and released in 2022. ControlNet allows users to generate images from text prompts with more control over the composition and style of the image.

In the context of text-to-image diffusion models, ControlNet works by copying the weights of a pre-trained diffusion model into two copies: a "locked" copy and a "trainable" copy. The "locked" copy preserves the original model, while the "trainable" copy is trained to learn the desired conditions. This allows users to train ControlNet on a small dataset of image pairs to generate images with specific characteristics, without destroying the original diffusion model.

ControlNet has been used to generate a wide variety of creative and impressive images, including realistic portraits, landscapes, and scenes from movies and video games. It is a powerful tool for artists and designers, and it has the potential to revolutionize the way that images are created.