The process begins with training the neural network on a dataset of images. The network learns the features, textures, shapes, and structures that define the images. Once trained, it can create new images by manipulating the learned features. For example, GANs use a two-part system— a generator and a discriminator— where the generator creates images and the discriminator evaluates them for authenticity.