1. [Publications](/publications) 2. Analyzing and Improving the Image Quality of StyleGAN # Analyzing and Improving the Image Quality of StyleGAN ![](/sites/default/files/styles/wide/public/publications/stylegan2-teaser-512x128.png?itok=nNOOKQ1z) 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. In particular, we redesign generator normalization, revisit progressive growing, and regularize the generator to encourage good conditioning in the mapping from latent vectors to images. In addition to improving image quality, this path length regularizer yields the additional benefit that the generator becomes significantly easier to invert. This makes it possible to reliably detect if an image is generated by a particular network. We furthermore visualize how well the generator utilizes its output resolution, and identify a capacity problem, motivating us to train larger models for additional quality improvements. Overall, our improved model redefines the state of the art in unconditional image modeling, both in terms of existing distribution quality metrics as well as perceived image quality. ## Authors [Tero Karras](/person/tero-karras) [Samuli Laine](/person/samuli-laine) [Miika Aittala](/person/miika-aittala) [Janne Hellsten](/person/janne-hellsten) Jaakko Lehtinen (NVIDIA and Aalto University) [Timo Aila](/person/timo-aila) ## Publication Date Sunday, June 14, 2020 ## Published in [CVPR 2020](http://cvpr2020.thecvf.com/) ## Research Area [Artificial Intelligence and Machine Learning ](/research-area/machine-learning-artificial-intelligence) [Computer Graphics](/research-area/computer-graphics) [Generative AI](/research-area/generative-ai) ## External Links [Paper (ArXiv)](http://arxiv.org/abs/1912.04958) [Video (YouTube)](https://youtu.be/c-NJtV9Jvp0) [Code (GitHub)](https://github.com/NVlabs/stylegan2) ## Copyright Copyright © 2019, NVIDIA Corporation. All rights reserved. This work is made available under the Nvidia Source Code License-NC. To view a copy of this license, visit