  Recently, StyleGAN has enabled various image manipulation and editing tasks
thanks to the high-quality generation and the disentangled latent space.
However, additional architectures or task-specific training paradigms are
usually required for different tasks. In this work, we take a deeper look at
the spatial properties of StyleGAN. We show that with a pretrained StyleGAN
along with some operations, without any additional architecture, we can perform
comparably to the state-of-the-art methods on various tasks, including image
blending, panorama generation, generation from a single image, controllable and
local multimodal image to image translation, and attributes transfer. The
proposed method is simple, effective, efficient, and applicable to any existing
pretrained StyleGAN model.
