h the autoregressive inference
mechanism, providing a more natural and efficient way for the AR model to
generate complex visual content. Our compact 1D AR model achieves high-quality
image synthesis with significantly fewer tokens than previous approaches, i.e.
VAR/VQGAN. We further propose a parallel inference mechanism with
self-correction that accelerates generation speed by approximately 8x while
reducing accumulation sampling error inherent in teacher-forcing supervision.
On the ImageNet 256x256 bench