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Recently, generative domain adaptation has achieved remarkable progress,enabling us to adapt a pre-trained generator to a new target domain. However,existing methods simply adapt the generator to a single target domain and arelimited to a single modality, either text-driven or image-driven. Moreover,they cannot maintain well consistency with the source domain, which impedes theinheritance of the diversity. In this paper, we propose UniHDA, a\textbf{unified} and \textbf{versatile} framework for generative hybrid domainadaptation with multi-modal references from multiple domains. We use CLIPencoder to project multi-modal references into a unified embedding space andthen linearly interpolate the direction vectors from multiple target domains toachieve hybrid domain adaptation. To ensure \textbf{consistency} with thesource domain, we propose a novel cross-domain spatial structure (CSS) lossthat maintains detailed spatial structure information between source and targetgenerator. Experiments show that the adapted generator can synthesise realisticimages with various attribute compositions. Additionally, our framework isgenerator-agnostic and versatile to multiple generators, e.g., StyleGAN, EG3D,and Diffusion Models.

UniHDA: A Unified and Versatile Framework for Multi-Modal Hybrid Domain Adaptation