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.

翻译：摘要：尽管扩散模型展现出生成高质量图像的显著能力，但其对训练数据的“复制”倾向引发了隐私担忧。虽然近期研究表明，这种复制可能源于训练数据描述的泛化不足以及训练图像的重复，但有效的缓解策略仍难以实现。为填补这一空白，本文首先提出一个衡量描述泛化性的通用性分数，并利用大语言模型（LLM）对训练描述进行泛化处理。随后，我们借助泛化后的描述，提出一种新颖的双重融合增强方法来缓解扩散模型的复制问题。实验结果表明，与原始扩散模型相比，所提方法能显著将复制现象减少43.5%，同时保持生成内容的多样性与质量。