  We present a novel U-Attention vision Transformer for universal texture
synthesis. We exploit the natural long-range dependencies enabled by the
attention mechanism to allow our approach to synthesize diverse textures while
preserving their structures in a single inference. We propose a hierarchical
hourglass backbone that attends to the global structure and performs patch
mapping at varying scales in a coarse-to-fine-to-coarse stream. Completed by
skip connection and convolution designs that propagate and fuse information at
different scales, our hierarchical U-Attention architecture unifies attention
to features from macro structures to micro details, and progressively refines
synthesis results at successive stages. Our method achieves stronger 2$\times$
synthesis than previous work on both stochastic and structured textures while
generalizing to unseen textures without fine-tuning. Ablation studies
demonstrate the effectiveness of each component of our architecture.
