Distilling semantically aware orders for autoregressive image generation
Authors: name: Rishav Pramanik, name: Antoine Poupon, name: Juan A. Rodriguez, name: Masih Aminbeidokhti, name: David Vazquez, name: Christopher Pal, name: Zhaozheng Yin, name: Marco Pedersoli

Autoregressive patch-based image generation has recently shown competitive
results in terms of image quality and scalability. It can also be easily
integrated and scaled within Vision-Language models. Nevertheless,
autoregressive models require a defined order for patch generation. While a
natural order based on the dictation of the words makes sense for text
generation, there is no inherent generation order that exists for image
generation. Traditionally, a raster-scan order (from top-left to bottom-right)
guides autoregressive image generation models. In this paper, we argue that
this order is suboptimal, as it fails to respect the causality of the image
content: for instance, when conditioned on a visual description of a sunset, an
autoregressive model may generate clouds before the sun, even though the color
of clouds should depend on the color of the sun and not the inverse. In this
work, we show that first by training a model to generate patches in
any-given-order, we can infer both the content and the location (order) of each
patch during generation. Secondly, we use these extracted orders to finetune
the any-given-order model to produce better-quality images. Through our
experiments, we show on two datasets that this new generation method produces
better images than the traditional raster-scan approach, with similar training
costs and no extra annotations.