Recent studies have explored autoregressive models for image generation, with promising results, and have combined diffusion models with autoregressive frameworks to optimize image generation via diffusion losses. In this study, we present a theoretical analysis of diffusion and autoregressive models with diffusion loss, highlighting the latter's advantages. We present a theoretical comparison of conditional diffusion and autoregressive diffusion with diffusion loss, demonstrating that patch denoising optimization in autoregressive models effectively mitigates condition errors and leads to a stable condition distribution. Our analysis also reveals that autoregressive condition generation refines the condition, causing the condition error influence to decay exponentially. In addition, we introduce a novel condition refinement approach based on Optimal Transport (OT) theory to address ``condition inconsistency''. We theoretically demonstrate that formulating condition refinement as a Wasserstein Gradient Flow ensures convergence toward the ideal condition distribution, effectively mitigating condition inconsistency. Experiments demonstrate the superiority of our method over diffusion and autoregressive models with diffusion loss methods.
ScaMo: Exploring the Scaling Law in Autoregressive Motion Generation Model
Shanghai AI Laboratory（上海人工智能实验室）；Shanghai Jiao Tong University（上海交通大学）；Tsinghua University（清华大学）；The University of Hong Kong（香港大学）；Sun Yat-sen University（中山大学）；The Chinese University of Hongkong, Shenzhen（香港中文大学（深圳））
Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis
School of Artificial Intelligence, Jilin University（吉林大学人工智能学院）；Shanghai Innovation Institute（上海创新研究院）；Institute of Trustworthy Embodied AI, Fudan University（复旦大学可信具身人工智能研究院）；Department of Computer Science, The University of Hong Kong（香港大学计算机科学系）
FlexVAR: Flexible Visual Autoregressive Modeling without Residual Prediction
Institute of Information Science, Beijing Jiaotong University（北京交通大学信息科学学院）；University of Technology Sydney（悉尼科技大学）；Meituan（美团）；Georgia Institute of Technology（佐治亚理工学院）
Closing the Safety Gap: Surgical Concept Erasure in Visual Autoregressive Models
Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Jilin University（吉林大学）；Peng Cheng Laboratory（鹏城实验室）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）
Talker-T2AV: Joint Talking Audio-Video Generation with Autoregressive Diffusion Modeling
Hong Kong University of Science and Technology（香港科学与技术大学）；Zhejiang University（浙江大学）；National University of Singapore（新加坡国立大学）；The Chinese University of Hong Kong（香港中文大学）；Peking University（北京大学）；Independent Researcher（独立研究者）
PixelPyramids: Exact Inference Models from Lossless Image Pyramids
TU Darmstadt（达姆施塔特工业大学）
自回归模型是一类具有高度灵活的函数形式的精确推理方法，可以为自然图像提供最先进的密度估计。然而，在维度上的顺序排序使得这些模型在计算上昂贵，并且限制了它们对低分辨率图像的适用性。在这项工作中，我们提出了像素金字塔，块自回归方法采用无损金字塔分解与特定尺度的表示编码的图像像素的联合分布。至关重要的是，与完全自回归方法相比，它提供了更稀疏的依赖结构。我们的PixelPyramid可以在各种图像数据集上获得最先进的密度估计结果，特别是对于高分辨率数据。对于CelebA-HQ 1024 x 1024，我们观察到密度估计（以位/暗为单位）提高到基线的约44%，尽管采样速度甚至优于易于并行化的基于流的模型。
