HOFAR: High-Order Augmentation of Flow Autoregressive Transformers

• URL: http://arxiv.org/abs/2503.08032v1
• Date: Tue, 11 Mar 2025 04:29:22 GMT
• Title: HOFAR: High-Order Augmentation of Flow Autoregressive Transformers
• Authors: Yingyu Liang, Zhizhou Sha, Zhenmei Shi, Zhao Song, Mingda Wan,
• Abstract summary: This paper introduces a novel framework that systematically enhances flow autoregressive transformers through high-order supervision.<n>We provide theoretical analysis and empirical evaluation showing that our High-Order FlowAR (HOFAR) demonstrates measurable improvements in generation quality compared to baseline models.
• Score: 17.002793355495136
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Flow Matching and Transformer architectures have demonstrated remarkable performance in image generation tasks, with recent work FlowAR [Ren et al., 2024] synergistically integrating both paradigms to advance synthesis fidelity. However, current FlowAR implementations remain constrained by first-order trajectory modeling during the generation process. This paper introduces a novel framework that systematically enhances flow autoregressive transformers through high-order supervision. We provide theoretical analysis and empirical evaluation showing that our High-Order FlowAR (HOFAR) demonstrates measurable improvements in generation quality compared to baseline models. The proposed approach advances the understanding of flow-based autoregressive modeling by introducing a systematic framework for analyzing trajectory dynamics through high-order expansion.

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