STARFlow: Scaling Latent Normalizing Flows for High-resolution Image Synthesis
Jiatao Gu, Tianrong Chen, David Berthelot, Huangjie Zheng, Yuyang Wang, Ruixiang ZHANG, Laurent Dinh, Miguel Angel Bautista, Joshua Susskind, Shuangfei Zhai
We present STARFlow, a scalable generative model based on autoregressive flows (AFs)—a special class of normalizing flows—that achieves strong performance on high-resolution image synthesis. We first establish the theoretical universality of AFs for modeling continuous distributions. Building on this foundation, we introduce a set of architectural and algorithmic innovations that significantly enhance the scalability of normalizing flows: (1) a deep-shallow design where a deep AF block captures most of the model’s capacity, followed by a few shallow AF blocks that are computationally cheap yet contribute non-negligibly, (2) learning in the latent space of pretrained autoencoders, which proves far more effective than modeling pixels directly, and (3) a novel guidance algorithm that substantially improves sample quality. Crucially, our model remains a single, end-to-end normalizing flow, allowing exact maximum likelihood training in continuous space without discretization. \model{} achieves competitive results in both class- and text-conditional image generation, with sample quality approaching that of state-of-the-art diffusion models. To our knowledge, this is the first successful demonstration of normalizing flows at this scale and resolution.