DiSA: Diffusion Step Annealing in Autoregressive Image Generation
Authors: name: Qinyu Zhao, name: Jaskirat Singh, name: Ming Xu, name: Akshay Asthana, name: Stephen Gould, name: Liang Zheng

An increasing number of autoregressive models, such as MAR, FlowAR, xAR, and
Harmon adopt diffusion sampling to improve the quality of image generation.
However, this strategy leads to low inference efficiency, because it usually
takes 50 to 100 steps for diffusion to sample a token. This paper explores how
to effectively address this issue. Our key motivation is that as more tokens
are generated during the autoregressive process, subsequent tokens follow more
constrained distributions and are easier to sample. To intuitively explain, if
a model has generated part of a dog, the remaining tokens must complete the dog
and thus are more constrained. Empirical evidence supports our motivation: at
later generation stages, the next tokens can be well predicted by a multilayer
perceptron, exhibit low variance, and follow closer-to-straight-line denoising
paths from noise to tokens. Based on our finding, we introduce diffusion step
annealing (DiSA), a training-free method which gradually uses fewer diffusion
steps as more tokens are generated, e.g., using 50 steps at the beginning and
gradually decreasing to 5 steps at later stages. Because DiSA is derived from
our finding specific to diffusion in autoregressive models, it is complementary
to existing acceleration methods designed for diffusion alone. DiSA can be
implemented in only a few lines of code on existing models, and albeit simple,
achieves 5-10times faster inference for MAR and Harmon and 1.4-2.5times
for FlowAR and xAR, while maintaining the generation quality.