  We propose Image2StyleGAN++, a flexible image editing framework with many
applications. Our framework extends the recent Image2StyleGAN in three ways.
First, we introduce noise optimization as a complement to the $W^+$ latent
space embedding. Our noise optimization can restore high-frequency features in
images and thus significantly improves the quality of reconstructed images,
e.g. a big increase of PSNR from 20 dB to 45 dB. Second, we extend the global
$W^+$ latent space embedding to enable local embeddings. Third, we combine
embedding with activation tensor manipulation to perform high-quality local
edits along with global semantic edits on images. Such edits motivate various
high-quality image editing applications, e.g. image reconstruction, image
inpainting, image crossover, local style transfer, image editing using
scribbles, and attribute level feature transfer. Examples of the edited images
are shown across the paper for visual inspection.
