DuET: 双专家轨迹用于扩散图像编辑
DuET: Dual Expert Trajectories for Diffusion Image Editing
AI总结：
提出训练自由的DuET方法，通过临时切换到文本到图像阶段再返回编辑模式，缓解源图像条件限制，提升编辑指令相关性、语义保真度和感知质量。
摘要：
最近的扩散编辑器在每一步去噪过程中以源图像为条件执行多样化的基于指令的编辑。然而，持续的源图像条件限制可能会限制编辑的完全执行程度和结果的自然性，尤其是当目标场景与输入差异较大时。我们提出了DuET（双专家轨迹），一种无需训练的推理方法，通过过渡到文本到图像阶段再返回编辑模式，暂时放松源图像条件，使得去噪轨迹能够向目标分布移动，同时保留图像条件编辑的结构优势。在不修改模型权重或增加采样成本的情况下，DuET在多种模型和基准上持续改善了指令相关性、语义保真度和感知质量。在某些情况下，这些改进伴随着源图像保留的适度降低，揭示了源保留与编辑保真度之间可预测的权衡。
英文摘要：
Recent diffusion editors perform diverse instruction-based edits while conditioning on the source image at every denoising step. Yet persistent source-image conditioning can limit how fully an edit is executed and how natural the result appears, especially when the target scene diverges substantially from the input. We introduce DuET (Dual Expert Trajectories), a training-free inference method that temporarily relaxes source-image conditioning by transitioning through a text-to-image phase before returning to edit mode ($\mathrm{E}\to\mathrm{T2I}\to\mathrm{E}$), allowing the denoising trajectory to move toward the target distribution while retaining the structural benefits of image-conditioned editing. Without modifying model weights or increasing sampling cost, DuET consistently improves instruction relevance, semantic fidelity, and perceptual quality across diverse models and benchmarks. Fixed switching schedules obtain these gains at a modest, predictable cost in source-image preservation; we show this cost is not fundamental. A per-edit variant, Selective DuET, routes on lightweight attention-probe signals read from the edit trajectory and improves fidelity, naturalness, and artifact scores while keeping source preservation perceptually indistinguishable from the baseline.
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。
