Scale-Wise VAR is Secretly Discrete Diffusion
Authors: name: Amandeep Kumar, name: Nithin Gopalakrishnan Nair, name: Vishal M. Patel

Autoregressive (AR) transformers have emerged as a powerful paradigm for
visual generation, largely due to their scalability, computational efficiency
and unified architecture with language and vision. Among them, next scale
prediction Visual Autoregressive Generation (VAR) has recently demonstrated
remarkable performance, even surpassing diffusion-based models. In this work,
we revisit VAR and uncover a theoretical insight: when equipped with a
Markovian attention mask, VAR is mathematically equivalent to a discrete
diffusion. We term this reinterpretation as Scalable Visual Refinement with
Discrete Diffusion (SRDD), establishing a principled bridge between AR
transformers and diffusion models. Leveraging this new perspective, we show how
one can directly import the advantages of diffusion such as iterative
refinement and reduce architectural inefficiencies into VAR, yielding faster
convergence, lower inference cost, and improved zero-shot reconstruction.
Across multiple datasets, we show that the diffusion based perspective of VAR
leads to consistent gains in efficiency and generation.