  There is a prevalent opinion in the recent literature that Diffusion-based
models outperform GAN-based counterparts on the Image Super Resolution (ISR)
problem. However, in most studies, Diffusion-based ISR models were trained
longer and utilized larger networks than the GAN baselines. This raises the
question of whether the superiority of Diffusion models is due to the Diffusion
paradigm being better suited for the ISR task or if it is a consequence of the
increased scale and computational resources used in contemporary studies. In
our work, we compare Diffusion-based and GAN-based Super Resolution under
controlled settings, where both approaches are matched in terms of
architecture, model and dataset size, and computational budget. We show that a
GAN-based model can achieve results comparable to a Diffusion-based model.
Additionally, we explore the impact of design choices such as text conditioning
and augmentation on the performance of ISR models, showcasing their effect on
several downstream tasks. We will release the inference code and weights of our
scaled GAN.
