Paper ID: 2410.02055

Using Style Ambiguity Loss to Improve Aesthetics of Diffusion Models

James Baker

Teaching text-to-image models to be creative involves using style ambiguity loss. In this work, we explore using the style ambiguity training objective, used to approximate creativity, on a diffusion model. We then experiment with forms of style ambiguity loss that do not require training a classifier or a labeled dataset, and find that the models trained with style ambiguity loss can generate better images than the baseline diffusion models and GANs. Code is available at this https URL.

Submitted: Oct 2, 2024