A key challenge in autoregressive image generation is to efficiently sample independent locations in parallel, while still modeling mutual dependencies with serial conditioning. Some recent works have addressed this by conditioning between scales in a multiscale pyramid. Others have looked at parallelizing samples in a single image using regular partitions or randomized orders. In this work we examine a flexible, fixed ordering based on progressive checkerboards for multiscale autoregressive image generation. Our ordering draws samples in parallel from evenly spaced regions at each scale, maintaining full balance in all levels of a quadtree subdivision at each step. This enables effective conditioning both between and within scales. Intriguingly, we find evidence that in our balanced setting, a wide range of scale-up factors lead to similar results, so long as the total number of serial steps is constant. On class-conditional ImageNet, our method achieves competitive performance compared to recent state-of-the-art autoregressive systems with like model capacity, using fewer sampling steps.

翻译：自回归图像生成的一个关键挑战在于如何高效并行采样独立位置，同时通过序列条件化建模相互依赖关系。近期研究通过多尺度金字塔中的跨尺度条件化解决了部分问题，另一些研究则采用规则分区或随机顺序对单张图像进行并行采样。本文研究了一种基于渐进棋盘法的灵活固定排序策略，用于多尺度自回归图像生成。该方法在每一尺度上从均匀分布的区域并行采样，在四叉树细分的每个层级保持完全平衡，从而实现了尺度间与尺度内的有效条件化。有趣的是，我们发现：在平衡设置下，只要总序列步数恒定，多种尺度放大因子都能产生相似结果。在类别条件化ImageNet数据集上，本方法在模型容量相近的情况下，以更少的采样步骤达到了与近期先进自回归系统相当的性能水平。