ControlNet is a neural network structure to control diffusion models by adding extra conditions. It clones the weights of a large diffusion model into a 'trainable copy' and a 'locked copy'. The 'trainable copy' learns task-specific conditions while preserving the original model's generation capabilities. This allows users to control image generation based on various input conditions, such as edge maps, segmentation maps, pose skeletons, and depth maps, offering precise control over the output image.
This tech insight summary was produced by Sumble. We provide rich account intelligence data.
On our web app, we make a lot of our data available for browsing at no cost.
We have two paid products, Sumble Signals and Sumble Enrich, that integrate with your internal sales systems.