Abstract: We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature.

Alias-free generator architecture and training configurations (stylegan3-t, stylegan3-r). Tools for interactive visualization (visualizer.py), spectral analysis (avg_spectra.py), and video generation (gen_video.py). Equivariance metrics (eqt50k_int, eqt50k_frac, eqr50k).

Feb 25, 2025 · StyleGAN is an advanced generative model developed by NVIDIA designed to generate highly realistic images such as faces of people who don’t exist. Unlike traditional image generation methods StyleGAN provides a unique advantage by giving you control over various levels of image features.

An image generated using StyleGAN that looks like a portrait of a young woman. This image was generated by an artificial neural network based on an analysis of a large number of photographs.

Jun 17, 2020 · This new project called StyleGAN2, presented at CVPR 2020, uses transfer learning to generate a seemingly infinite numbers of portraits in an infinite variety of painting styles. The work builds on the team’s previously published StyleGAN project. Shown in this new demo, the resulting model allows the user to create and fluidly explore portraits.

Abstract: The style-based GAN architecture (StyleGAN) yields state-of-the-art results in data-driven unconditional generative image modeling. We expose and analyze several of its characteristic artifacts, and propose changes in both model …

Jul 9, 2024 · StyleGAN is a state-of-the-art GAN (Generative Adversarial Network), a type of Deep Learning (DL) model, that has been around for some time, developed by a team of researchers including Ian Goodfellow in 2014.

May 10, 2020 · The StyleGAN is an extension of the progressive growing GAN that is an approach for training generator models capable of synthesizing very large high-quality images via the incremental expansion of both discriminator and generator models from small to large images during the training process.

StyleGAN 2. This is a PyTorch implementation of the paper Analyzing and Improving the Image Quality of StyleGAN which introduces StyleGAN 2. StyleGAN 2 is an improvement over StyleGAN from the paper A Style-Based Generator Architecture for Generative Adversarial Networks.

Jul 12, 2024 · StyleGAN is a groundbreaking paper that offers high-quality and realistic pictures and allows for superior control and knowledge of generated photographs, making it even more lenient than before to generate convincing fake images.