  Autoregressive and diffusion models drive the recent breakthroughs on
text-to-image generation. Despite their huge success of generating
high-realistic images, a common shortcoming of these models is their high
inference latency - autoregressive models run more than a thousand times
successively to produce image tokens and diffusion models convert Gaussian
noise into images with many hundreds of denoising steps. In this work, we
explore non-autoregressive text-to-image models that efficiently generate
hundreds of image tokens in parallel. We develop many model variations with
different learning and inference strategies, initialized text encoders, etc.
Compared with autoregressive baselines that needs to run one thousand times,
our model only runs 16 times to generate images of competitive quality with an
order of magnitude lower inference latency. Our non-autoregressive model with
346M parameters generates an image of 256$\times$256 with about one second on
one V100 GPU.
