  Although autoregressive models have achieved promising results on image
generation, their unidirectional generation process prevents the resultant
images from fully reflecting global contexts. To address the issue, we propose
an effective image generation framework of Draft-and-Revise with Contextual
RQ-transformer to consider global contexts during the generation process. As a
generalized VQ-VAE, RQ-VAE first represents a high-resolution image as a
sequence of discrete code stacks. After code stacks in the sequence are
randomly masked, Contextual RQ-Transformer is trained to infill the masked code
stacks based on the unmasked contexts of the image. Then, Contextual
RQ-Transformer uses our two-phase decoding, Draft-and-Revise, and generates an
image, while exploiting the global contexts of the image during the generation
process. Specifically. in the draft phase, our model first focuses on
generating diverse images despite rather low quality. Then, in the revise
phase, the model iteratively improves the quality of images, while preserving
the global contexts of generated images. In experiments, our method achieves
state-of-the-art results on conditional image generation. We also validate that
the Draft-and-Revise decoding can achieve high performance by effectively
controlling the quality-diversity trade-off in image generation.
