A Spark of Vision-Language Intelligence: 2-Dimensional Autoregressive Transformer for Efficient Finegrained Image Generation

Figure 1: Generations from DnD-Transformers trained on class-conditional ImageNet256 256 (a.top) and unconditional arXiv images (a.bottom). Unconditional rich-text image generations by trained diffusion (b.1) and autoregressive model (b.2), 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, 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. The field of autoregressive (AR) image generation is experiencing a resurgence of interest, largely driven by groundbreaking advancements in large language models (LLMs), exemplified by the release of ChatGPT (OpenAI, 2022). Because typical AR image generation methods also predict output in a next-token prediction manner, this resemblance has sparked significant efforts in two main areas: 1) transferring advanced, large-scale training techniques and expertise from LLMs to AR image generation models (Bai et al., 2023; Tian et al., 2024; Sun et al., 2024), and 2) developing truly multimodal foundation models capable of both understanding and generating multimodal information within a unified training framework (Lu et al., 2022; 2023; Team, 2024).