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StyleGAN - Official TensorFlow Implementation. Contribute to NVlabs/stylegan development by creating an account on GitHub.
17 de jun. de 2020 · Learn how to use StyleGAN2, a generative adversarial network (GAN) that can create realistic and diverse images, on NVIDIA GPUs. Explore the architecture, training, and applications of StyleGAN2 for content creation and rendering.
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).Equivariance metrics (eqt50k_int, eqt50k_frac, eqr50k).
StyleGAN3 is a new generative model that eliminates the "texture sticking" issue in GANs by using continuous signals and avoiding aliasing. It also achieves translation and rotation equivariance, and produces high-quality synthesis of faces, objects, and scenes.
StyleGAN es una red generativa antagónica. Se la utiliza para la generación de imágenes, principalmente de rostros. Nvidia lo presentó en diciembre de 2018 y publicó el código en febrero de 2019.
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.
Learn how to train StyleGAN 2, an improved generator architecture for generative adversarial networks, with less than 500 lines of code. Compare StyleGAN 2 with Progressive GAN and StyleGAN, and see the differences in image quality and style mixing.
