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

• URL: http://arxiv.org/abs/2312.13537v1
• Date: Thu, 21 Dec 2023 02:39:53 GMT
• Title: HyperEditor: Achieving Both Authenticity and Cross-Domain Capability in Image Editing via Hypernetworks
• Authors: Hai Zhang, Chunwei Wu, Guitao Cao, Hailing Wang, Wenming Cao
• Abstract summary: 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.
• Score: 5.9189325968909365
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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