发表机构
University of Maryland; Netflix（马里兰大学; 网飞公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对流匹配模型源分布缺乏空间结构的问题，提出StructFlow将空间局部性编码到源分布，可提升图像生成质量与局部可控重合成性能，且能集成到大型预训练模型中。
AI 中文摘要
当前流匹配模型学习将独立同分布（i.i.d.）高斯噪声的源分布转换为自然图像的目标分布，但该源分布不包含任何空间结构概念。然而，图像本质上具有局部性，因为邻近像素之间存在强相关性。我们假设，由于噪声是独立采样的，模型在训练过程中会被隐含地鼓励利用噪声较小的邻域作为上下文，从而部分绕过了正确学习图像真实局部结构的需求。换句话说，该源分布与图像领域的归纳偏置相悖。为缓解这种设计差异，我们提出了StructFlow，它通过让小区域内的像素共享一个公共噪声分量，直接将空间局部性编码到源分布中。这种结构化源产生的传输路径与图像区域几何对齐，具备通用流匹配难以实现的特性：自然尊重边界的细粒度局部编辑、鲁棒的结构保留以及图像间平滑的语义插值。我们证明这些优势也可扩展到大型预训练模型，表明StructFlow甚至可以通过轻量级的训练后阶段进行集成。在多个数据集上针对无条件、类别条件和文本条件的不同扩散Transformer架构开展的全面实验证实，StructFlow不仅提供了具有竞争力的图像生成质量，还显著提升了局部可控重合成的性能。
英文摘要
Current flow matching models learn to transport the source i.i.d. Gaussian noise into the target distribution of natural images, yet this source distribution carries no notion of spatial structure. Images however are fundamentally local since nearby pixels are strongly correlated. By sampling the noise independently, we hypothesize that models are implicitly encouraged to exploit less noisy neighbors as context during training, partially bypassing the need to properly learn the true local structure of images. The source distribution, in other words, works against the inductive bias of the image domain. To ameliorate this design discrepancy, we propose StructFlow which encodes spatial locality directly into the source by having the pixels within a small region share a common noise component. This structured source produces transport paths that are geometrically aligned with image regions - enabling properties that generic flow matching struggles to provide: fine-grained local editing that naturally respects boundaries, robust structure preservation, and smooth semantic interpolation between images. We show that these benefits also extend to large pre-trained models, demonstrating that StructFlow can even be incorporated through a lightweight post-training phase. Comprehensive experiments on multiple datasets, in unconditional, class and text-conditioned regimes, using different diffusion transformer architectures confirm that StructFlow not only offers competitive image generation quality, but also significantly improves localized controllable re-synthesis.
