Text-to-image models suffer from various safety issues that may limit their suitability for
deployment. Previous methods have separately addressed individual issues of bias …

Text-to-image diffusion models often make implicit assumptions about the world when
generating images. While some assumptions are useful (eg, the sky is blue), they can also …

In recent years, the rapid advancement of machine learning (ML) models, particularly
transformer-based pre-trained models, has revolutionized Natural Language Processing …

Bias amplification is a phenomenon in which models increase imbalances present in the
training data. In this paper, we study bias amplification in the text-to-image domain using …

Recent progress in generative models has resulted in models that produce both realistic as
well as relevant images for most textual inputs. These models are being used to generate …

The field of computer vision is now a multi-billion dollar enterprise, with its use in
surveillance applications driving this large market share. In the last six years, computer …

Text-to-image models, which can generate high-quality images based on textual input, have
recently enabled various content-creation tools. Despite significantly affecting a wide range …

With the advancement of artificial intelligence, big data, and cloud computing, numerous
generative AI applications have surfaced. In contrast to conventional generative design and …

The race to develop image generation models is intensifying, with a rapid increase in the
number of text-to-image models available. This is coupled with growing public awareness of …

With the emergence of various generative AI applications, artificial intelligence-generated
content (AIGC) demonstrates positive potential for design activities. However, few scholars …