Abstract
Generating images in a consistent reference visual style remains a challenging computer vision task. State-of-the-art methods aiming for style-consistent generation struggle to effectively separate semantic content from stylistic elements, leading to content leakage from the image provided as a reference to the targets. To address this challenge, we propose Only-Style: a method designed to mitigate content leakage in a semantically coherent manner while preserving stylistic consistency. Only-Style works by localizing content leakage during inference, allowing the adaptive tuning of a parameter that controls the style alignment process, specifically within the image patches containing the subject in the reference image. This adaptive process best balances stylistic consistency with leakage elimination. Moreover, the localization of content leakage can function as a standalone component, given a reference-target image pair, allowing the adaptive tuning of any method-specific parameter that provides control over the impact of the stylistic reference. In addition, we propose a novel evaluation framework to quantify the success of style-consistent generations in avoiding undesired content leakage. Our approach demonstrates a significant improvement over state-of-the-art methods through extensive evaluation across diverse instances, consistently achieving robust stylistic consistency without undesired content leakage.
Abstract (translated)
在一致的参考视觉风格下生成图像仍然是一个具有挑战性的计算机视觉任务。最先进的旨在实现风格一致性生成的方法难以有效地区分语义内容和风格元素，导致从提供的参考图片中向目标泄露不必要的内容信息。为了解决这一挑战，我们提出了Only-Style：一种方法，旨在以语义连贯的方式减少内容泄漏，同时保持风格的一致性。Only-Style通过在推理过程中定位内容泄漏来工作，并允许自适应调整控制样式对齐过程的参数（特别是在参考图像中包含主体的图块内）。这种自适应处理最能平衡风格一致性与泄漏消除之间的关系。此外，定位内容泄露的功能可以作为独立组件存在，给定一个参考目标图片对，允许任何特定方法的参数进行自适应调节，从而控制样式参照的影响。 我们还提出了一种新的评估框架，以量化风格一致生成在避免不需要的内容泄漏方面的成功程度。通过广泛的测试和各种实例，我们的方法明显优于最先进的方法，在保持强大风格一致性的同时，始终能有效地防止不需要的内容泄露。
URL
https://arxiv.org/abs/2506.09916
