This repository implements a technique for improving GAN training by leveraging knowledge from pretrained vision models. The approach selects the most accurate vision models by probing linear separability between real and fake samples in their embeddings, then progressively adds them to a discriminator ensemble. The method has been extended to work with BigGAN and StyleGAN3 architectures, achieving state-of-the-art results on LSUN benchmarks.