We propose a novel approach to image generation by decomposing an image into
a structured sequence, where each element in the sequence shares the same
spatial resolution but differs in the number of unique tokens used, capturing
different level of visual granularity. Image generation is carried out through
our newly introduced Next Visual Granularity (NVG) generation framework, which
generates a visual granularity sequence beginning from an empty image and
progressively refines it, from global layout to fine details, in a structured
manner. This iterative process encodes a hierarchical, layered representation
that offers fine-grained control over the generation process across multiple
granularity levels. We train a series of NVG models for class-conditional image
generation on the ImageNet dataset and observe clear scaling behavior. Compared
to the VAR series, NVG consistently outperforms it in terms of FID scores (3.30
-> 3.03, 2.57 ->2.44, 2.09 -> 2.06). We also conduct extensive analysis to
showcase the capability and potential of the NVG framework. Our code and models
will be released.