Diffusion-based models, such as the Stable Diffusion model, have revolutionized text-to-image synthesis with their ability to produce high-quality, high-resolution images. These advancements have prompted significant progress in image generation and editing tasks. However, these models also raise concerns due to their tendency to memorize and potentially replicate exact training samples, posing privacy risks and enabling adversarial attacks. Duplication in training datasets is recognized as a major factor contributing to memorization, and various forms of memorization have been studied so far. This paper focuses on two distinct and underexplored types of duplication that lead to replication during inference in diffusion-based models, particularly in the Stable Diffusion model. We delve into these lesser-studied duplication phenomena and their implications through two case studies, aiming to contribute to the safer and more responsible use of generative models in various applications.

翻译：基于扩散的模型（如Stable Diffusion模型）凭借其生成高质量、高分辨率图像的能力，彻底改变了文本到图像合成领域。这些进展推动了图像生成与编辑任务的重大突破。然而，这类模型因其倾向于记忆并可能复现训练样本的精确内容而引发担忧，由此带来隐私风险并可能助长对抗性攻击。训练数据集的重复被认为是导致记忆化的主要因素，目前已有研究对多种记忆形式进行了探讨。本文聚焦于两种尚未被充分研究的特殊重复类型，它们会导致扩散模型（特别是Stable Diffusion模型）在推理过程中产生复制行为。我们通过两个案例研究深入探究这些鲜少被提及的重复现象及其影响，旨在推动生成模型在各应用场景中更安全、更负责任地使用。