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This paper studies the problem of StyleGAN inversion, which plays anessential role in enabling the pretrained StyleGAN to be used for real imageediting tasks. The goal of StyleGAN inversion is to find the exact latent codeof the given image in the latent space of StyleGAN. This problem has a highdemand for quality and efficiency. Existing optimization-based methods canproduce high-quality results, but the optimization often takes a long time. Onthe contrary, forward-based methods are usually faster but the quality of theirresults is inferior. In this paper, we present a new feed-forward network"E2Style" for StyleGAN inversion, with significant improvement in terms ofefficiency and effectiveness. In our inversion network, we introduce: 1) ashallower backbone with multiple efficient heads across scales; 2) multi-layeridentity loss and multi-layer face parsing loss to the loss function; and 3)multi-stage refinement. Combining these designs together forms an effective andefficient method that exploits all benefits of optimization-based andforward-based methods. Quantitative and qualitative results show that ourE2Style performs better than existing forward-based methods and comparably tostate-of-the-art optimization-based methods while maintaining the highefficiency as well as forward-based methods. Moreover, a number of real imageediting applications demonstrate the efficacy of our E2Style. Our code isavailable at \url{https://github.com/wty-ustc/e2style}

E2Style: Improve the Efficiency and Effectiveness of StyleGAN Inversion