English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Diffusion-based models, such as the Stable Diffusion model, haverevolutionized text-to-image synthesis with their ability to producehigh-quality, high-resolution images. These advancements have promptedsignificant progress in image generation and editing tasks. However, thesemodels also raise concerns due to their tendency to memorize and potentiallyreplicate exact training samples, posing privacy risks and enabling adversarialattacks. Duplication in training datasets is recognized as a major factorcontributing to memorization, and various forms of memorization have beenstudied so far. This paper focuses on two distinct and underexplored types ofduplication that lead to replication during inference in diffusion-basedmodels, particularly in the Stable Diffusion model. We delve into theselesser-studied duplication phenomena and their implications through two casestudies, aiming to contribute to the safer and more responsible use ofgenerative models in various applications.

Memory Triggers: Unveiling Memorization in Text-To-Image Generative Models through Word-Level Duplication