Large-scale text-to-image models evolve rapidly in size and architecture. The existing adapters struggle to keep pace with these models, requiring extensive retraining. This paper proposes a novel adapter transfer framework, A4A (Adapter for Adapter), which uses an all-for-all mapping approach to seamlessly transfer attention-based adapters across different model architectures (e.g., U-Net to transformer). The framework consists of Coupling Space Projection and Upgraded Space Mapping. During Coupling Space Projection, all attention features of the pretrained adapter are aggregated to fully capture the coupling relationship before being projected into a unified space. The unified space maintains coupling features in a consistent dimension, effectively and efficiently addressing feature scale discrepancies arising from the base model’s architecture. In the Upgraded Space Mapping Module, randomly initialized learnable features are introduced to connect the unified and upgraded spaces by integrating reference features via the attention mechanism. The learned features are adaptively injected into the upgrade model through the Alignment module, which bridges the discrepancies between the models using the all-for-all mapping. Experimental results on personalized image generation tasks demonstrate that A4A outperforms previous methods in transferring adapters while being the first to achieve adapter transfer across model architectures.

Semi-supervised Domain Adaptation (SSDA) aims to learn a well-performed model using fully labeled source samples and scarcely labeled target samples, along with unlabeled target samples. Due to the dominant presence of labeled samples from the source domain in the training data, both the feature extractor and classifier can display bias towards the source domain. This can result in sub-optimal feature extraction for the challenging target samples that have notable differences from the source domain. Moreover, the source-favored classifier can hinder the classification performance of the target domain. To this end, we propose a novel Joint Contrastive Learning with Sensitivity (JCLS) in this paper, which consists of sensitivity-aware feature contrastive learning (SFCL) and class-wise probabilistic contrastive learning (CPCL). Different from the traditional contrastive learning, SFCL pays more attention to the sensitive samples during optimizing the feature extractor, and consequently the feature discrimination of unlabeled samples can be enhanced. CPCL performs class-wise contrastive learning in the probabilistic space to enforce the cross-domain classifier to match the real distribution of source and target samples. By combining these two components, our JCLS is able to extract domain-invariant and compact features and obtain a well-performed classifier. We conduct the experiments on the DomainNet and Office-Home benchmarks, and the results show that our approach achieves state-of-the-art performance.