This work presents Switti, a scale-wise transformer for text-to-image generation. We start by adapting an existing next-scale prediction autoregressive (AR) architecture to T2I generation, investigating and mitigating training stability issues in the process. Next, we argue that scale-wise transformers do not require causality and propose a non-causal counterpart facilitating ~21% 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. By disabling guidance at these scales, we achieve an additional sampling acceleration of ~32% 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 7x faster.
View on arXiv@article{voronov2025_2412.01819,
title={ Switti: Designing Scale-Wise Transformers for Text-to-Image Synthesis },
author={ Anton Voronov and Denis Kuznedelev and Mikhail Khoroshikh and Valentin Khrulkov and Dmitry Baranchuk },
journal={arXiv preprint arXiv:2412.01819},
year={ 2025 }
}