  In this paper, we take a new approach to autoregressive image generation that
is based on two main ingredients. The first is wavelet image coding, which
allows to tokenize the visual details of an image from coarse to fine details
by ordering the information starting with the most significant bits of the most
significant wavelet coefficients. The second is a variant of a language
transformer whose architecture is re-designed and optimized for token sequences
in this 'wavelet language'. The transformer learns the significant statistical
correlations within a token sequence, which are the manifestations of
well-known correlations between the wavelet subbands at various resolutions. We
show experimental results with conditioning on the generation process.
