  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.

Cryptography and Security;Computer Vision and Pattern Recognition;Machine Learning