one generating the image at the lowest resolution, then the generated image is scaled up to , and fed to the next level to generate an image at a higher resolution, and so on. The discriminator is decomposed into a pyramid as well. StyleGAN series The StyleGAN family is a series of architectures published by Nvidia's research division. Progressive GAN Progressive GAN is a method for training GAN for large-scale image generation stably, by growing a GAN generator from small to large scale in a pyramidal fashion. Like SinGAN, it decomposes the generator as, and the discriminator as . During training, at first only are used in a GAN game to generate 4x4 images. Then are added to reach the second stage of GAN game, to generate 8x8 images, and so on, until we reach a GAN game to generate 1024x1024 images. To avoid shock between stages of the GAN game, each new layer is "blended in" (Figure 2 of the paper). For example, this is how the second stage GAN game starts: Just before, the GAN game consists of the pair generating and discriminating 4x4 images. Just after, the GAN game consists of the pair generating and discriminating 8x8 images. Here, the functions are image up- and down-sampling functions, and is a blend-in factor (much like an alpha in image composing) that smoothly glides from 0 to 1. StyleGAN-1 StyleGAN-1 is designed as a combination of Progressive GAN with neural style transfer. The key architectural choice of StyleGAN-1 is a progressive growth mechanism, similar to Progressive GAN. Each generated image starts as a constant array, and repeatedly passed through style blocks. Each style block applies a "style latent vector" via affine transform ("adaptive instance normalization"), similar to how neural style transfer uses Gramian matrix. It then adds noise, and normalize (subtract the mean, then divide by the variance). At training time, usually only one style latent vector is used per image generated, but sometimes two ("mixing regularization") in order to encourage each style block to independently perform its stylization without expecting help from other style blocks (since they might receive an entirely different style latent vector). After training, multiple style latent vectors can be fed into each style block. Those fed to the lower layers control the large-scale styles, and those fed to the higher layers control the fine-detail styles. Style-mixing between two images can be performed as well.