In this paper, we show that a binary latent space can be explored for compact
yet expressive image representations. We model the bi-directional mappings
between an image and the corresponding latent binary representation by training
an auto-encoder with a Bernoulli encoding distribution. On the one hand, the
binary latent space provides a compact discrete image representation of which
the distribution can be modeled more efficiently than pixels or continuous
latent representations. On the other hand, we now represent each image patch as
a binary vector instead of an index of a learned cookbook as in discrete image
representations with vector quantization. In this way, we obtain binary latent
representations that allow for better image quality and high-resolution image
representations without any multi-stage hierarchy in the latent space. In this
binary latent space, images can now be generated effectively using a binary
latent diffusion model tailored specifically for modeling the prior over the
binary image representations. We present both conditional and unconditional
image generation experiments with multiple datasets, and show that the proposed
method performs comparably to state-of-the-art methods while dramatically
improving the sampling efficiency to as few as 16 steps without using any
test-time acceleration. The proposed framework can also be seamlessly scaled to
$1024 \times 1024$ high-resolution image generation without resorting to latent
hierarchy or multi-stage refinements.

本文基于自动编码器训练了一个伯努利编码分布的二元潜空间模型，用于图像压缩和高分辨率图像生成。结果表明该方法无需使用多级层次结构即可大幅提高采样效率，生成的图像质量好且效果与现有先进方法相当。