FlowSlider: 无训练的连续图像编辑通过保真度引导分解
FlowSlider: Training-Free Continuous Image Editing via Fidelity-Steering Decomposition
AI总结：
FlowSlider通过保真度引导分解实现无训练连续图像编辑，通过几何分析和实证测量证明其稳定性，提升编辑质量。
摘要：
连续图像编辑旨在提供滑块式控制编辑强度的同时保持源图像保真度并保持一致的编辑方向。现有基于学习的滑块方法通常依赖于通过合成或代理监督训练的辅助模块，这引入了额外的训练开销，并将滑块行为与训练分布耦合，这在编辑或领域分布变化时会降低可靠性。我们提出了FlowSlider，一种无需训练的Rectified Flow中的连续编辑方法。FlowSlider将FlowEdit的更新分解为（i）保真度项，作为源条件稳定器，保持身份和结构，以及（ii）引导项，驱动语义向目标编辑过渡。几何分析和实证测量表明，这些项近似正交，通过仅缩放引导项而保持保真度项不变，从而实现稳定的强度控制。因此，FlowSlider在无需训练的情况下提供平滑且可靠的控制，提升了跨多样任务的连续编辑质量。
英文摘要：
Continuous image editing aims to provide slider-style control of edit strength while preserving source-image fidelity and maintaining a consistent edit direction. Existing learning-based slider methods typically rely on auxiliary modules trained with synthetic or proxy supervision. This introduces additional training overhead and couples slider behavior to the training distribution, which can reduce reliability under distribution shifts in edits or domains. We propose \textit{FlowSlider}, a training-free method for continuous editing in Rectified Flow that requires no post-training. \textit{FlowSlider} decomposes FlowEdit's update into (i) a fidelity term, which acts as a source-conditioned stabilizer that preserves identity and structure, and (ii) a steering term that drives semantic transition toward the target edit. Geometric analysis and empirical measurements show that these terms are approximately orthogonal, enabling stable strength control by scaling only the steering term while keeping the fidelity term unchanged. As a result, \textit{FlowSlider} provides smooth and reliable control without post-training, improving continuous editing quality across diverse tasks.
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。
