  Cutting-edge diffusion models produce images with high quality and
customizability, enabling them to be used for commercial art and graphic design
purposes. But do diffusion models create unique works of art, or are they
replicating content directly from their training sets? In this work, we study
image retrieval frameworks that enable us to compare generated images with
training samples and detect when content has been replicated. Applying our
frameworks to diffusion models trained on multiple datasets including Oxford
flowers, Celeb-A, ImageNet, and LAION, we discuss how factors such as training
set size impact rates of content replication. We also identify cases where
diffusion models, including the popular Stable Diffusion model, blatantly copy
from their training data.
