  Existing GAN inversion methods fail to provide latent codes for reliable
reconstruction and flexible editing simultaneously. This paper presents a
transformer-based image inversion and editing model for pretrained StyleGAN
which is not only with less distortions, but also of high quality and
flexibility for editing. The proposed model employs a CNN encoder to provide
multi-scale image features as keys and values. Meanwhile it regards the style
code to be determined for different layers of the generator as queries. It
first initializes query tokens as learnable parameters and maps them into W+
space. Then the multi-stage alternate self- and cross-attention are utilized,
updating queries with the purpose of inverting the input by the generator.
Moreover, based on the inverted code, we investigate the reference- and
label-based attribute editing through a pretrained latent classifier, and
achieve flexible image-to-image translation with high quality results.
Extensive experiments are carried out, showing better performances on both
inversion and editing tasks within StyleGAN.
