Consistency-diversity-realism Pareto fronts of conditional image generative models

Understand

Building world models that accurately and comprehensively represent the real world is the utmost aspiration for conditional image generative models as it would enable their use as world simulators.

• For these models to be successful world models, they should not only excel at image quality and prompt-image consistency but also ensure high representation diversity.
• However, current research in generative models mostly focuses on creative applications that are predominantly concerned with human preferences of image quality and aesthetics.
• We note that generative models have inference time mechanisms - or knobs - that allow the control of generation consistency, quality, and diversity.