  We propose Stratified Image Transformer(StraIT), a pure
non-autoregressive(NAR) generative model that demonstrates superiority in
high-quality image synthesis over existing autoregressive(AR) and diffusion
models(DMs). In contrast to the under-exploitation of visual characteristics in
existing vision tokenizer, we leverage the hierarchical nature of images to
encode visual tokens into stratified levels with emergent properties. Through
the proposed image stratification that obtains an interlinked token pair, we
alleviate the modeling difficulty and lift the generative power of NAR models.
Our experiments demonstrate that StraIT significantly improves NAR generation
and out-performs existing DMs and AR methods while being order-of-magnitude
faster, achieving FID scores of 3.96 at 256*256 resolution on ImageNet without
leveraging any guidance in sampling or auxiliary image classifiers. When
equipped with classifier-free guidance, our method achieves an FID of 3.36 and
IS of 259.3. In addition, we illustrate the decoupled modeling process of
StraIT generation, showing its compelling properties on applications including
domain transfer.
