  We present STAR, a text-to-image model that employs scale-wise
auto-regressive paradigm. Unlike VAR, which is limited to class-conditioned
synthesis within a fixed set of predetermined categories, our STAR enables
text-driven open-set generation through three key designs: To boost diversity
and generalizability with unseen combinations of objects and concepts, we
introduce a pre-trained text encoder to extract representations for textual
constraints, which we then use as guidance. To improve the interactions between
generated images and fine-grained textual guidance, making results more
controllable, additional cross-attention layers are incorporated at each scale.
Given the natural structure correlation across different scales, we leverage 2D
Rotary Positional Encoding (RoPE) and tweak it into a normalized version. This
ensures consistent interpretation of relative positions across token maps at
different scales and stabilizes the training process. Extensive experiments
demonstrate that STAR surpasses existing benchmarks in terms of fidelity,image
text consistency, and aesthetic quality. Our findings emphasize the potential
of auto-regressive methods in the field of high-quality image synthesis,
offering promising new directions for the T2I field currently dominated by
diffusion methods.
