Few-shot image generative adaptation, which finetunes well-trained generative models on limited examples, is of practical importance. The main challenge is that the few-shot model easily becomes overfitting. It can be attributed to two aspects: the lack of sample diversity for the generator and the failure of fidelity discrimination for the discriminator. In this paper, we introduce two novel methods to solve the diversity and fidelity respectively. Concretely, we propose dynamic weighted semantic correspondence to keep the diversity for the generator, which benefits from the richness of samples generated by source models. To prevent discriminator overfitting, we propose coupled training paradigm across the source and target domains to keep the feature extraction capability of the discriminator backbone. Extensive experiments show that our method outperforms previous methods both on image quality and diversity significantly.