VAR is a novel visual autoregressive modeling method that surpasses diffusion models, achieving more efficient image generation. It establishes the power-law scaling laws of visual generation and possesses zero-shot generalization capabilities. VAR provides a range of pre-trained models of different sizes for users to explore and utilize.