Few-shot generative model adaption is a challenging task that aims to adapt a generative model pre-trained on a large-scale source domain dataset to the target domain with limited training samples. Current methods do their best to transfer source domain knowledge to the target generator in different ways. However, the over-fitting problem has always been a thorny problem in model adaption and is hard to solve due to the limited training data. To overcome such issues, we revisit the training process of model adaption and devise hypothetical experiments. Our research has found that current adaption methods fail to fully use the learned knowledge in the source generator. Meanwhile, in the all-parameter training mode, parameters independent of the source domain are also fine-tuned when fitting the few-shot target samples. In order to circumvent such issues, we propose the optimal kernel modulation method for effective few-shot generative model adaption. The idea of optimal transport theory is leveraged to measure the importance of model parameters for knowledge preservation and transfer. Meanwhile, to realize the control of parameter optimization, we adopt the parameter-efficient kernel modulation method according to its importance. Extensive quantitative and qualitative experiments prove the effectiveness and superiority of our method.

Few-shot generative model adaptation aims to obtain an excellent model that generates high-quality and high-diversity images with a few training data. However, a small number of training samples often leads to overfitting of the model, which leads the generated images to lose generative diversity. Existing methods either fail to preserve structural information, leading to overfitting phenomena, or maintain too much structure in the source domain, failing to transfer styles well. To solve these problems, we propose an effective generative model adaptation method with style-guided prompt to balance generative diversity and style transformation. Firstly, by freezing the structure-related parameters of the pre-trained model, we preserve the robustness and diversity of the source domain's generative model, which helps to mitigate overfitting and maintain diversity in the generated images. Secondly, the proposed style-guided prompt method allows us to capture the target domain's style more naturally, facilitating more accurate and efficient style transfer in the generated images. Thirdly, the multi-layer deep contrastive loss is designed to further enhance the generated images' diversity and quality by preserving the generative diversity in the source domain without using extra target domain data. Extensive quantitative and qualitative experiments prove the effectiveness and superiority of our method. Project Page:https://github.com/Patricia886/SGP/.