Scale-Wise VAR is Secretly Discrete Diffusion
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.
68da7a37cfa03a5158a1edbf
Amandeep Kumar
claimed_verified
2025-09-29T13:05:09.208Z
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/avatars/451361495c67be90a07698c3ad358dc8.svg
Amandeep
user
Aman015
68da7a37cfa03a5158a1edc0
Nithin Gopalakrishnan Nair
68da7a37cfa03a5158a1edc1
Vishal M. Patel
Scale-Wise VAR is Secretly Discrete Diffusion
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.
VAR, an autoregressive transformer for visual generation, is shown to be equivalent to a discrete diffusion when using a Markovian attention mask, leading to improved efficiency and generation quality.
autoregressive transformers visual generation next scale prediction VAR diffusion-based models Markovian attention mask discrete diffusion Scalable Visual Refinement with Discrete Diffusion SRDD iterative refinement zero-shot reconstruction