Switti: Designing Scale-Wise Transformers for Text-to-Image Synthesis

Anton Voronov, Denis Kuznedelev, Mikhail Khoroshikh, Valentin Khrulkov, Dmitry Baranchuk

Switti: Designing Scale-Wise Transformers for Text-to-Image Synthesis: 34 upvotes on Hugging Face Daily Papers, #4 of 29 papers on 2024-12-03. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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