ConSinGAN is an official implementation of a WACV-2021 paper that trains GANs on individual images rather than large datasets. The model trains iteratively at increasing resolutions, adding convolutional layers as it scales up, and trains only recent layers while using smaller learning rates for earlier layers. It supports unconditional generation, image harmonization, animation, and editing tasks.