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StyleGAN - Official TensorFlow Implementation. Contribute to NVlabs/stylegan development by creating an account on GitHub.
This repository is an updated version of stylegan2-ada-pytorch, with several new features: 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 ).
17 de jun. de 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.
The below video compares StyleGAN3’s internal activations to those of StyleGAN2 (top). Our alias-free translation (middle) and rotation (bottom) equivariant networks build the image in a radically different manner from what appear to be multi-scale phase signals that follow the features seen in the final image.
StyleGAN is a generative adversarial network (GAN) introduced by Nvidia researchers in December 2018, and made source available in February 2019. StyleGAN depends on Nvidia's CUDA software, GPUs, and Google's TensorFlow, or Meta AI's PyTorch, which supersedes TensorFlow as the official implementation library in later StyleGAN versions.
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 architecture and training methods to address them.
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.
