FIA-Edit：用于高效高保真无反转文本引导图像编辑的频率交互注意力
FIA-Edit: Frequency-Interactive Attention for Efficient and High-Fidelity Inversion-Free Text-Guided Image Editing
AI总结：
针对基于流的无反转文本引导图像编辑方法存在的源信息整合不足问题，提出FIA-Edit框架，通过频率交互注意力的两个关键模块提升跨域对齐与结构语义保留，实现低计算成本下的高保真编辑，还首次拓展至临床应用并带来医学数据增强收益。
摘要：
随着扩散模型的兴起，文本引导图像编辑取得了快速进展。虽然基于流的无反转方法通过避免潜在反转实现了高效率，但由于缺乏对源信息的有效整合，它们往往无法有效整合源信息，导致背景保留不佳、空间不一致和过度编辑。在本文中，我们提出FIA-Edit，一种新颖的无反转框架，通过频率交互注意力实现高保真和语义精确的编辑。具体而言，我们设计了两个关键组件：（1）频率表示交互（FRI）模块，通过在自注意力中交换源特征和目标特征之间的频率分量来增强跨域对齐；（2）特征注入（FIJ）模块，将源侧查询、键、值和文本嵌入显式整合到目标分支的交叉注意力中，以保留结构和语义。全面且广泛的实验表明，FIA-Edit以低计算成本（在RTX 4090上每512×512图像约6秒）支持高保真编辑，并且在视觉质量、背景保真度和可控性方面在不同任务中始终优于现有方法。此外，我们首次将文本引导图像编辑扩展到临床应用。通过在手术图像中合成解剖学上连贯的出血变化，FIA-Edit为医学数据增强开辟了新机会，并在下游出血分类中带来显著收益。我们的项目可在https://github.com/kk42yy/FIA-Edit获取。
英文摘要：
Text-guided image editing has advanced rapidly with the rise of diffusion models. While flow-based inversion-free methods offer high efficiency by avoiding latent inversion, they often fail to effectively integrate source information, leading to poor background preservation, spatial inconsistencies, and over-editing due to the lack of effective integration of source information. In this paper, we present FIA-Edit, a novel inversion-free framework that achieves high-fidelity and semantically precise edits through a Frequency-Interactive Attention. Specifically, we design two key components: (1) a Frequency Representation Interaction (FRI) module that enhances cross-domain alignment by exchanging frequency components between source and target features within self-attention, and (2) a Feature Injection (FIJ) module that explicitly incorporates source-side queries, keys, values, and text embeddings into the target branch's cross-attention to preserve structure and semantics. Comprehensive and extensive experiments demonstrate that FIA-Edit supports high-fidelity editing at low computational cost (~6s per 512 * 512 image on an RTX 4090) and consistently outperforms existing methods across diverse tasks in visual quality, background fidelity, and controllability. Furthermore, we are the first to extend text-guided image editing to clinical applications. By synthesizing anatomically coherent hemorrhage variations in surgical images, FIA-Edit opens new opportunities for medical data augmentation and delivers significant gains in downstream bleeding classification. Our project is available at: https://github.com/kk42yy/FIA-Edit.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
