This work tackles the information loss bottleneck of vector-quantization (VQ) autoregressive image generation by introducing a novel model architecture called the 2-Dimensional Autoregression (DnD) Transformer. The DnD-Transformer predicts more codes for an image by introducing a new autoregression direction, \textit{model depth}, along with the sequence length direction. Compared to traditional 1D autoregression and previous work utilizing similar 2D image decomposition such as RQ-Transformer, the DnD-Transformer is an end-to-end model that can generate higher quality images with the same backbone model size and sequence length, opening a new optimization perspective for autoregressive image generation. Furthermore, our experiments reveal that the DnD-Transformer's potential extends beyond generating natural images. It can even generate images with rich text and graphical elements in a self-supervised manner, demonstrating an understanding of these combined modalities. This has not been previously demonstrated for popular vision generative models such as diffusion models, showing a spark of vision-language intelligence when trained solely on images. Code, datasets and models are open at https://github.com/chenllliang/DnD-Transformer.

本研究解决了向量量化自回归图像生成中的信息损失瓶颈，通过引入一种新的二维自回归变压器（DnD-Transformer）模型架构。相比传统的一维自回归方法，DnD-Transformer以新的自回归方向和模型深度实现更高质量的图像生成，并在自我监督的方式下有效生成包含文本和图形元素的图像，展示出其视觉语言智能的潜力。

视觉语言智能的火花：用于高效细粒度图像生成的二维自回归变压器