  An ongoing trend in generative modelling research has been to push sample
resolutions higher whilst simultaneously reducing computational requirements
for training and sampling. We aim to push this trend further via the
combination of techniques - each component representing the current pinnacle of
efficiency in their respective areas. These include vector-quantized GAN
(VQ-GAN), a vector-quantization (VQ) model capable of high levels of lossy -
but perceptually insignificant - compression; hourglass transformers, a highly
scaleable self-attention model; and step-unrolled denoising autoencoders
(SUNDAE), a non-autoregressive (NAR) text generative model. Unexpectedly, our
method highlights weaknesses in the original formulation of hourglass
transformers when applied to multidimensional data. In light of this, we
propose modifications to the resampling mechanism, applicable in any task
applying hierarchical transformers to multidimensional data. Additionally, we
demonstrate the scalability of SUNDAE to long sequence lengths - four times
longer than prior work. Our proposed framework scales to high-resolutions
($1024 \times 1024$) and trains quickly (2-4 days). Crucially, the trained
model produces diverse and realistic megapixel samples in approximately 2
seconds on a consumer-grade GPU (GTX 1080Ti). In general, the framework is
flexible: supporting an arbitrary number of sampling steps, sample-wise
self-stopping, self-correction capabilities, conditional generation, and a NAR
formulation that allows for arbitrary inpainting masks. We obtain FID scores of
10.56 on FFHQ256 - close to the original VQ-GAN in less than half the sampling
steps - and 21.85 on FFHQ1024 in only 100 sampling steps.
