  This paper studies the problem of StyleGAN inversion, which plays an
essential role in enabling the pretrained StyleGAN to be used for real facial
image editing tasks. This problem has the high demand for quality and
efficiency. Existing optimization-based methods can produce high quality
results, but the optimization often takes a long time. On the contrary,
forward-based methods are usually faster but the quality of their results is
inferior. In this paper, we present a new feed-forward network for StyleGAN
inversion, with significant improvement in terms of efficiency and quality. In
our inversion network, we introduce: 1) a shallower backbone with multiple
efficient heads across scales; 2) multi-layer identity loss and multi-layer
face parsing loss to the loss function; and 3) multi-stage refinement.
Combining these designs together forms a simple and efficient baseline method
which exploits all benefits of optimization-based and forward-based methods.
Quantitative and qualitative results show that our method performs better than
existing forward-based methods and comparably to state-of-the-art
optimization-based methods, while maintaining the high efficiency as well as
forward-based methods. Moreover, a number of real image editing applications
demonstrate the efficacy of our method. Our project page is
~\url{https://wty-ustc.github.io/inversion}.
