 the distribution over
multi-scale representations, significantly reducing generation latency.
However, these methods condition each scale on all previous scales and require
each token to consider all preceding tokens, exhibiting scale and spatial
redundancy. To better model the distribution by mitigating redundancy, we
propose Markovian Visual AutoRegressive modeling (MVAR), a novel autoregressive
framework that introduces scale and spatial Markov assumptions to reduce the
complexity of conditional probabi