Drift-AR: Single-Step Visual Autoregressive Generation via Anti-Symmetric Drifting

• URL: http://arxiv.org/abs/2603.28049v2
• Date: Wed, 08 Apr 2026 10:41:16 GMT
• Title: Drift-AR: Single-Step Visual Autoregressive Generation via Anti-Symmetric Drifting
• Abstract summary: Autoregressive (AR)-Diffusion hybrid paradigms combine AR's structured semantic modeling with diffusion's high-fidelity synthesis, yet suffer from a dual speed bottleneck.<n>We propose textbfDrift-AR, which leverages entropy signal to accelerate both stages.<n> Experiments on MAR, TransDiff, and NextStep-1 demonstrate 3.8-5.5$times$ speedup with genuine 1-NFE decoding, matching or surpassing original quality.
• Score: 25.589468409950484
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Autoregressive (AR)-Diffusion hybrid paradigms combine AR's structured semantic modeling with diffusion's high-fidelity synthesis, yet suffer from a dual speed bottleneck: the sequential AR stage and the iterative multi-step denoising of the diffusion vision decode stage. Existing methods address each in isolation without a unified principle design. We observe that the per-position \emph{prediction entropy} of continuous-space AR models naturally encodes spatially varying generation uncertainty, which simultaneously governing draft prediction quality in the AR stage and reflecting the corrective effort required by vision decoding stage, which is not fully explored before. Since entropy is inherently tied to both bottlenecks, it serves as a natural unifying signal for joint acceleration. In this work, we propose \textbf{Drift-AR}, which leverages entropy signal to accelerate both stages: 1) for AR acceleration, we introduce Entropy-Informed Speculative Decoding that align draft-target entropy distributions via a causal-normalized entropy loss, resolving the entropy mismatch that causes excessive draft rejection; 2) for visual decoder acceleration, we reinterpret entropy as the \emph{physical variance} of the initial state for an anti-symmetric drifting field -- high-entropy positions activate stronger drift toward the data manifold while low-entropy positions yield vanishing drift -- enabling single-step (1-NFE) decoding without iterative denoising or distillation. Moreover, both stages share the same entropy signal, which is computed once with no extra cost. Experiments on MAR, TransDiff, and NextStep-1 demonstrate 3.8-5.5$\times$ speedup with genuine 1-NFE decoding, matching or surpassing original quality. Code will be available at https://github.com/aSleepyTree/Drift-AR.

Related papers

• FARI: Robust One-Step Inversion for Watermarking in Diffusion Models [89.29541056113442]
  Inversion-based watermarking is a promising approach to authenticate diffusion-generated images.<n>Existing approaches over-optimize internal truncation error.<n>textbfFARI is a one-step inversion framework paired with lightweight adversarial LoRA fine-tuning of the denoiser for watermark extraction.
  arXiv  Detail & Related papers  (2026-07-29T10:13:55Z)
• Deconstructing Off-Policy Ratios: Entropy-Scaled Trust Regions for Asynchronous Reinforcement Learning [6.667218294299622]
  Asynchronous reinforcement learning (RL) accelerates large language model (LLM) post-training by overlapping rollout generation with policy optimization.<n>Existing methods typically retain or discard tokens based solely on the magnitude of their importance ratios.<n>We propose the Entropy-Scaled Trust Region (ESTR), which scales each token's off-policy deviation by its local entropy.
  arXiv  Detail & Related papers  (2026-07-24T10:55:42Z)
• AdaFlash: Adaptive Speculative Decoding via On-Policy Distilled Diffusion Drafters [67.51886052453452]
  Speculative decoding has become a prevalent paradigm for accelerating large language model inference.<n>Recent work such as DFlash further boosts drafting efficiency by leveraging diffusion drafters.<n>We propose AdaFlash framework, comprising two components: (i) an on-policy distillation (OPD) algorithm with reverse-KL divergence tailored for diffusion drafters, and (ii) an adaptive length head that dynamically adjusts the candidate sequence length on the fly.
  arXiv  Detail & Related papers  (2026-07-21T15:52:50Z)
• AAD-1: Asymmetric Adversarial Distillation for One-Step Autoregressive Video Generation [66.55343616132005]
  AAD-1 is an Asymmetric Adversarial Distillation framework for one-step autoregressive image-to-video generation.<n>AAD-1 achieves state-of-the-art performance in one-step autoregressive video generation.
  arXiv  Detail & Related papers  (2026-06-02T17:55:30Z)
• A convolutional autoencoder and neural ODE framework for surrogate modeling of transient counterflow flames [0.12314765641075438]
  A novel convolutional autoencoder neural ODE (CAE-NODE) framework is proposed for a reduced-order model (ROM) of transient 2D counterflow flames.<n>The network can accurately capture the entire transient process, including ignition, flame propagation, and the gradual transition to a non-premixed condition.
  arXiv  Detail & Related papers  (2026-03-16T09:43:49Z)
• OmniForcing: Unleashing Real-time Joint Audio-Visual Generation [51.031726911410594]
  Recent joint audio-visual diffusion models achieve remarkable generation quality but suffer from high latency.<n>We propose OmniForcing, the first framework to distill an offline, dual-stream bidirectional diffusion model into a high-fidelity streaming autore generator.
  arXiv  Detail & Related papers  (2026-03-12T08:17:36Z)
• HiAR: Efficient Autoregressive Long Video Generation via Hierarchical Denoising [52.237486207964245]
  HiAR is a hierarchical denoising framework that reverses the conventional generation order.<n>It performs causal generation across all blocks at every denoising step, so that each block is always conditioned on context at the same noise level.<n>On VBench (20s generation), HiAR achieves the best overall score and the lowest temporal drift among all compared methods.
  arXiv  Detail & Related papers  (2026-03-09T17:58:16Z)
• CaTok: Taming Mean Flows for One-Dimensional Causal Image Tokenization [122.88484422855934]
  We present CaTok, a 1D causal image tokenizer with a MeanFlow decoder.<n>By selecting tokens over time intervals, CaTok learns causal 1D representations that support both fast one-step generation and high-fidelity multi-step sampling.<n> Experiments demonstrate that CaTok achieves state-of-the-art results on ImageNet reconstruction, reaching 0.75 FID, 22.53 PSNR and 0.674 SSIM.
  arXiv  Detail & Related papers  (2026-03-06T16:39:17Z)
• Adaptive Visual Autoregressive Acceleration via Dual-Linkage Entropy Analysis [50.48301331112126]
  We propose NOVA, a training-free token reduction acceleration framework for Visual AutoRegressive modeling.<n>NOVA adaptively determines the acceleration activation scale during inference by online identifying the inflection point of scale entropy growth.<n>Experiments and analyses validate NOVA as a simple yet effective training-free acceleration framework.
  arXiv  Detail & Related papers  (2026-02-01T17:29:42Z)
• Fast-ARDiff: An Entropy-informed Acceleration Framework for Continuous Space Autoregressive Generation [12.384836052394272]
  Autoregressive(AR)-diffusion hybrid paradigms combine AR's structured modeling with diffusion's synthesis.<n>We propose a unified AR-diffusion framework Fast-ARDiff that jointly optimize both components.<n>Fast-ARDiff achieves state-of-the-art acceleration across diverse models.
  arXiv  Detail & Related papers  (2025-12-09T12:35:18Z)
• OmniSAT: Compact Action Token, Faster Auto Regression [70.70037017501357]
  We introduce an Omni Swift Action Tokenizer, which learns a compact, transferable action representation.<n>The resulting discrete tokenization shortens the training sequence by 6.8$times$, and lowers the target entropy.
  arXiv  Detail & Related papers  (2025-10-08T03:55:24Z)
• Continuous Speculative Decoding for Autoregressive Image Generation [27.308442169466975]
  Continuous visual autoregressive (AR) models have demonstrated promising performance in image generation.<n> speculative decoding has effectively accelerated discrete autoregressive inference.<n>This work addresses challenges from low acceptance rate, inconsistent output distribution, and modified distribution without analytic expression.
  arXiv  Detail & Related papers  (2024-11-18T09:19:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.