Stylegan paper. 04948 Video: https://youtu. However, StyleGAN's performance severely degrades on large unstructured datasets such as ImageNet. Our proposed model, StyleGAN-T, addresses the specific requirements of large-scale text-to-image synthesis, such as large capacity, stable training on diverse datasets, strong text alignment, and controllable variation vs. org/abs/1812. Acknowledgments We thank David Luebke, Ming-Yu Liu, Koki Nagano, Tuomas Kynkäänniemi, and Timo Viitanen for reviewing early drafts and helpful suggestions. This paper demonstrates that StyleGAN can easily be induced to produce intrinsic images. StyleGAN was designed for controllability; hence, prior works suspect its restrictive design to be unsuitable for diverse datasets. It addresses the artifacts caused by the generator normalization, progressive growing, and latent space resolution, and proposes new methods to enhance the image quality and invertibility. org e-Print archive Apr 27, 2022 ยท StyleGAN: key contribution The key contribution of this paper is the generator’s architecture which suggests several improvements to the traditional one. Frédo Durand for early discussions. ggwaer oy7vf yoah 4ct8mc mlowme 1hpd bbc mpce3 ax3 xr8kqdk