Events

Modern text-to-image models achieve strong visual fidelity and prompt alignment, but often at the cost of generative diversity.

In this talk, I will present two complementary approaches to this problem: a simple inference-time method that achieves rich diversity by intervening in the model’s internal representations, and a new formulation of controlled diversity, where users explore structured image galleries through meaningful semantic variations.

Bio:

Omer Dahary is a PhD student in Computer Science at Tel Aviv University, advised by Daniel Cohen-Or. His research focuses on generative models, with an emphasis on improving their ability to align with user control.