  Editing real images authentically while also achieving cross-domain editing
remains a challenge. Recent studies have focused on converting real images into
latent codes and accomplishing image editing by manipulating these codes.
However, merely manipulating the latent codes would constrain the edited images
to the generator's image domain, hindering the attainment of diverse editing
goals. In response, we propose an innovative image editing method called
HyperEditor, which utilizes weight factors generated by hypernetworks to
reassign the weights of the pre-trained StyleGAN2's generator. Guided by CLIP's
cross-modal image-text semantic alignment, this innovative approach enables us
to simultaneously accomplish authentic attribute editing and cross-domain style
transfer, a capability not realized in previous methods. Additionally, we
ascertain that modifying only the weights of specific layers in the generator
can yield an equivalent editing result. Therefore, we introduce an adaptive
layer selector, enabling our hypernetworks to autonomously identify the layers
requiring output weight factors, which can further improve our hypernetworks'
efficiency. Extensive experiments on abundant challenging datasets demonstrate
the effectiveness of our method.
