In the past, normalizing generative flows have emerged as a promising class of generative models for natural images. This type of model has many modeling advantages: the ability to efficiently compute log-likelihood of the input data, fast generation and simple overall structure. Normalizing flows remained a topic of active research but later fell out of favor, as visual quality of the samples was not competitive with other model classes, such as GANs, VQ-VAE-based approaches or diffusion models. In this paper we revisit the design of the coupling-based normalizing flow models by carefully ablating prior design choices and using computational blocks based on the Vision Transformer architecture, not convolutional neural networks. As a result, we achieve state-of-the-art quantitative and qualitative performance with a much simpler architecture. While the overall visual quality is still behind the current state-of-the-art models, we argue that strong normalizing flow models can help advancing research frontier by serving as building components of more powerful generative models.

本研究解决了归一化生成流模型在自然图像生成中的局限性，特别是与其他生成模型（如GAN和扩散模型）相比的视觉质量问题。通过使用基于视觉变压器结构的计算模块，论文提出了一种重新设计的耦合归一化流模型，虽然整体视觉质量仍落后，但在数量和质量性能上达到了最先进的水平，显示出强大的归一化流模型在更加复杂生成模型构建中的潜力。

喷流：现代变压器基础的归一化流