Diffusion models have become the dominant approach for visual generation. They are trained by denoising a Markovian process that gradually adds noise to the input. We argue that the Markovian property limits the models ability to fully utilize the generation trajectory, leading to inefficiencies during training and inference. In this paper, we propose DART, a transformer-based model that unifies autoregressive (AR) and diffusion within a non-Markovian framework. DART iteratively denoises image patches spatially and spectrally using an AR model with the same architecture as standard language models. DART does not rely on image quantization, enabling more effective image modeling while maintaining flexibility. Furthermore, DART seamlessly trains with both text and image data in a unified model. Our approach demonstrates competitive performance on class-conditioned and text-to-image generation tasks, offering a scalable, efficient alternative to traditional diffusion models. Through this unified framework, DART sets a new benchmark for scalable, high-quality image synthesis.

 本研究解决了传统扩散模型在训练和推理过程中由于马尔可夫特性所带来的效率问题。提出的DART模型通过非马尔可夫框架，将自回归和扩散模型结合，使图像补丁的迭代去噪更加高效，并在处理文本和图像数据上表现出卓越的性能。DART为可扩展的高质量图像合成设定了新的基准。 

DART：用于可扩展文本到图像生成的去噪自回归变压器