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Editing real images authentically while also achieving cross-domain editingremains a challenge. Recent studies have focused on converting real images intolatent codes and accomplishing image editing by manipulating these codes.However, merely manipulating the latent codes would constrain the edited imagesto the generator's image domain, hindering the attainment of diverse editinggoals. In response, we propose an innovative image editing method calledHyperEditor, which utilizes weight factors generated by hypernetworks toreassign the weights of the pre-trained StyleGAN2's generator. Guided by CLIP'scross-modal image-text semantic alignment, this innovative approach enables usto simultaneously accomplish authentic attribute editing and cross-domain styletransfer, a capability not realized in previous methods. Additionally, weascertain that modifying only the weights of specific layers in the generatorcan yield an equivalent editing result. Therefore, we introduce an adaptivelayer selector, enabling our hypernetworks to autonomously identify the layersrequiring output weight factors, which can further improve our hypernetworks'efficiency. Extensive experiments on abundant challenging datasets demonstratethe effectiveness of our method.

HyperEditor: Achieving Both Authenticity and Cross-Domain Capability in Image Editing via Hypernetworks