GANs have a key (learned) loss called a discriminator which tells it whether or not the generated output looks real or not. GANs took many years of research to

GANs have a key (learned) loss called a discriminator which tells it whether or not the generated output looks real or not. GANs took many years of research to get to the point where they could get to the point where they could generate realistic, full sized images. Progress in diffusion models has been much faster. But you can add additional guidance to diffusion models, for instance, (possibly pre-trained) discriminative models which detect if certain objects classes such as faces look abnormal or malformed. We’re still quite early in the evolution of this family of models.