MC-SJD : Maximal Coupling Speculative Jacobi Decoding for Autoregressive
  Visual Generation Acceleration
While autoregressive (AR) modeling has recently emerged as a new paradigm in
visual generation, its practical adoption is severely constrained by the slow
inference speed of per-token generation, which often requires thousands of
steps to produce a single sample. To address this challenge, we propose MC-SJD,
a training-free, lossless parallel decoding framework designed to accelerate AR
visual generation by extending the recently introduced Speculative Jacobi
Decoding (SJD). Although SJD shows strong potential for accelerating AR
generation, we demonstrate that token instability across iterations
significantly reduces the acceptance rate, a limitation that primarily arises
from the independent sampling process used during draft token generation. To
overcome this, we introduce MC-SJD, an information-theoretic approach based on
coupling, which substantially accelerates standard SJD by maximizing the
probability of sampling identical draft tokens across consecutive iterations,
all while preserving its lossless property. Remarkably, this method requires
only a single-line modification to the existing algorithm, yet achieves
substantial performance gains, delivering up to a ~4.2x acceleration in image
generation and ~13.3x acceleration in video generation compared to standard AR
decoding, without any degradation in output quality.
69021742646208eac0d1f681
Junhyuk So
claimed_verified
2025-10-30T14:41:26.034Z
6253d06427a8414d3bbea461
https://cdn-avatars.huggingface.co/v1/production/uploads/1649659952956-noauth.jpeg
Junhyuk So
goqhadl9298
user
goqhadl9298
69021742646208eac0d1f682
Hyunho Kook
69021742646208eac0d1f683
Chaeyeon Jang
69021742646208eac0d1f684
Eunhyeok Park
MC-SJD : Maximal Coupling Speculative Jacobi Decoding for Autoregressive
  Visual Generation Acceleration
While autoregressive (AR) modeling has recently emerged as a new paradigm in
visual generation, its practical adoption is severely constrained by the slow
inference speed of per-token generation, which often requires thousands of
steps to produce a single sample. To address this challenge, we propose MC-SJD,
a training-free, lossless parallel decoding framework designed to accelerate AR
visual generation by extending the recently introduced Speculative Jacobi
Decoding (SJD). Although SJD shows strong potential for accelerating AR
generation, we demonstrate that token instability across iterations
significantly reduces the acceptance rate, a limitation that primarily arises
from the independent sampling process used during draft token generation. To
overcome this, we introduce MC-SJD, an information-theoretic approach based on
coupling, which substantially accelerates standard SJD by maximizing the
probability of sampling identical draft tokens across consecutive iterations,
all while preserving its lossless property. Remarkably, this method requires
only a single-line modification to the existing algorithm, yet achieves
substantial performance gains, delivering up to a ~4.2x acceleration in image
generation and ~13.3x acceleration in video generation compared to standard AR
decoding, without any degradation in output quality.
A new parallel decoding framework, MC-SJD, accelerates autoregressive visual generation by improving token stability and acceptance rate, achieving significant speedups without quality loss.
autoregressive modeling parallel decoding Speculative Jacobi Decoding token instability information-theoretic approach coupling image generation video generation