While diffusion models demonstrate a remarkable capability for generating
high-quality images, their tendency to `replicate' training data raises privacy
concerns. Although recent research suggests that this replication may stem from
the insufficient generalization of training data captions and duplication of
training images, effective mitigation strategies remain elusive. To address
this gap, our paper first introduces a generality score that measures the
caption generality and employ large language model (LLM) to generalize training
captions. Subsequently, we leverage generalized captions and propose a novel
dual fusion enhancement approach to mitigate the replication of diffusion
models. Our empirical results demonstrate that our proposed methods can
significantly reduce replication by 43.5% compared to the original diffusion
model while maintaining the diversity and quality of generations.

使用扩展语言模型和双重融合增强方法，本研究提出的方法可以显著减少扩散模型的复制行为，仅相比原始的扩散模型减少了 43.5%，同时保持了生成图像的多样性和质量。