Marrying Autoregressive Transformer and Diffusion with Multi-Reference
  Autoregression
Authors: name: Dingcheng Zhen, name: Qian Qiao, name: Tan Yu, name: Kangxi Wu, name: Ziwei Zhang, name: Siyuan Liu, name: Shunshun Yin, name: Ming Tao

We introduce TransDiff, the first image generation model that marries
Autoregressive (AR) Transformer with diffusion models. In this joint modeling
framework, TransDiff encodes labels and images into high-level semantic
features and employs a diffusion model to estimate the distribution of image
samples. On the ImageNet 256x256 benchmark, TransDiff significantly outperforms
other image generation models based on standalone AR Transformer or diffusion
models. Specifically, TransDiff achieves a Fr\'echet Inception Distance (FID)
of 1.61 and an Inception Score (IS) of 293.4, and further provides x2 faster
inference latency compared to state-of-the-art methods based on AR Transformer
and x112 faster inference compared to diffusion-only models. Furthermore,
building on the TransDiff model, we introduce a novel image generation paradigm
called Multi-Reference Autoregression (MRAR), which performs autoregressive
generation by predicting the next image. MRAR enables the model to reference
multiple previously generated images, thereby facilitating the learning of more
diverse representations and improving the quality of generated images in
subsequent iterations. By applying MRAR, the performance of TransDiff is
improved, with the FID reduced from 1.61 to 1.42. We expect TransDiff to open
up a new frontier in the field of image generation.