Switti: Designing Scale-Wise Transformers for Text-to-Image Synthesis
Authors: name: Anton Voronov, name: Denis Kuznedelev, name: Mikhail Khoroshikh, name: Valentin Khrulkov, name: Dmitry Baranchuk

This work presents Switti, a scale-wise transformer for text-to-image
generation. Starting from existing next-scale prediction AR models, we first
explore them for T2I generation and propose architectural modifications to
improve their convergence and overall performance. We then observe that
self-attention maps of our pretrained scale-wise AR model exhibit weak
dependence on preceding scales. Based on this insight, we propose a non-AR
counterpart facilitating {sim}11% faster sampling and lower memory usage
while also achieving slightly better generation quality.Furthermore, we reveal
that classifier-free guidance at high-resolution scales is often unnecessary
and can even degrade performance. %may be not only unnecessary but potentially
detrimental. By disabling guidance at these scales, we achieve an additional
sampling acceleration of {sim}20% and improve the generation of
fine-grained details. Extensive human preference studies and automated
evaluations show that Switti outperforms existing T2I AR models and competes
with state-of-the-art T2I diffusion models while being up to 7{times}
faster.