  Real-world image manipulation has achieved fantastic progress in recent years
as a result of the exploration and utilization of GAN latent spaces. GAN
inversion is the first step in this pipeline, which aims to map the real image
to the latent code faithfully. Unfortunately, the majority of existing GAN
inversion methods fail to meet at least one of the three requirements listed
below: high reconstruction quality, editability, and fast inference. We present
a novel two-phase strategy in this research that fits all requirements at the
same time. In the first phase, we train an encoder to map the input image to
StyleGAN2 $\mathcal{W}$-space, which was proven to have excellent editability
but lower reconstruction quality. In the second phase, we supplement the
reconstruction ability in the initial phase by leveraging a series of
hypernetworks to recover the missing information during inversion. These two
steps complement each other to yield high reconstruction quality thanks to the
hypernetwork branch and excellent editability due to the inversion done in the
$\mathcal{W}$-space. Our method is entirely encoder-based, resulting in
extremely fast inference. Extensive experiments on two challenging datasets
demonstrate the superiority of our method.
