Core Viewpoint - The article discusses the challenges and solutions in optimizing attention mechanisms for visual generation models, focusing on the need for efficient algorithms that can handle increasing input sequence lengths and the unique data distribution of visual attention patterns [3][11][15]. Group 1: Analysis Framework - A systematic analysis framework is proposed to identify key challenges in attention optimization for visual generation tasks, particularly the "diverse and dispersed" attention patterns [3][6]. - The article emphasizes that these diverse attention patterns can be unified into a "local aggregation" block pattern, which simplifies the design of sparse attention mechanisms [3][15]. Group 2: Sparse Attention and Low-Bit Quantization - Existing sparse attention methods face challenges in adapting to diverse attention patterns, leading to difficulties in designing effective sparse masks [7][11]. - The article introduces a novel approach of "reorganizing attention patterns" to unify complex attention modes into hardware-friendly block patterns, enhancing the effectiveness of sparse designs [7][19]. - For low-bit quantization, the article analyzes the key issues related to quantization loss and proposes solutions to minimize these losses through better data distribution management [8][12]. Group 3: Proposed Solution - The proposed "Token Reordering" scheme aims to transform attention maps into a unified block pattern, facilitating both sparsity and quantization [14][19]. - The article highlights that each attention head exhibits consistent local aggregation in specific dimensions, allowing for tailored token reordering strategies [19][24]. Group 4: Performance and Efficiency - Experimental results indicate that the proposed PAROAttention method maintains superior algorithm performance while achieving significant hardware efficiency improvements, outperforming existing sparse attention methods [45][55]. - The method demonstrates a notable reduction in overhead, with the additional costs kept below 1%, showcasing its hardware-friendly nature [57][58]. Group 5: Broader Implications - The insights gained from the analysis of visual attention patterns can inform the design of training methods and parameterization strategies for visual models, potentially leading to the development of more effective foundational models in the field [58].