面向细粒度图像生成的按检测头自适应旋转位置编码
Head-wise Adaptive Rotary Positional Encoding for Fine-Grained Image Generation
把标准多维 RoPE 换成按检测头自适应、经 SVD 参数化的可学习旋转映射，可直接作为现有 DiT/Flux 类图像生成主干的位置编码替换件，改善细粒度空间关系与计数能力，且不破坏相对位置性质。
原文依据 We propose HARoPE, a head-wise adaptive extension that inserts a learnable linear transformation parameterized via singular value decomposition (SVD) before the rotary mapping.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论Transformer 依赖显式位置编码来建模数据中的结构。旋转位置嵌入（RoPE）在一维领域表现优异，但将其应用于图像生成时，暴露出在细粒度空间关系建模、颜色线索和物体计数等方面的显著局限。本文指出，标准多维 RoPE 在捕捉细粒度图像生成所需的复杂结构偏置方面存在关键局限：频率分配僵化、各轴相互独立以及对所有检测头一视同仁。我们提出 HARoPE，一种按检测头自适应的扩展，在旋转映射之前插入一个通过奇异值分解（SVD）参数化的可学习线性变换。这一轻量修改实现了动态频率重分配、旋转平面的语义对齐以及检测头特定的位置感受野，同时严格保持 RoPE 的相对位置性质。在类别条件 ImageNet 生成以及文本到图像生成（Flux 与 MMDiT）上的大量实验表明，HARoPE 相对强 RoPE 基线和其他扩展方法均能稳定提升性能。该方法可作为有效的即插即用替代方案，为增强基于 Transformer 的图像生成模型的位置感知能力提供了一种有原则且适应性强的解决方案。
英文原文摘要 1295 字符
Transformers rely on explicit positional encoding to model structure in data. WhileRotary Position Embedding (RoPE) excels in 1D domains, its application to image generation reveals significant limitations such as fine-grained spatial relationmodeling, color cues, and object counting. This paper identifies key limitationsof standard multi-dimensional RoPE--rigid frequency allocation, axis-wise independence, and uniform head treatment--in capturing the complex structural biasesrequired for fine-grained image generation. We propose HARoPE, a head-wiseadaptive extension that inserts a learnable linear transformation parameterized viasingular value decomposition (SVD) before the rotary mapping. This lightweightmodification enables dynamic frequency reallocation, semantic alignment of rotaryplanes, and head-specific positional receptive fields while rigorously preservingRoPE's relative-position property. Extensive experiments on class-conditional ImageNet and text-to-image generation (Flux and MMDiT) demonstrate that HARoPEconsistently improves performance over strong RoPE baselines and other extensions. The method serves as an effective drop-in replacement, offering a principledand adaptable solution for enhancing positional awareness in transformer-basedimage generative models.