面向高保真基于文本编辑的任务导向扩散反演
Task-Oriented Diffusion Inversion for High-Fidelity Text-based Editing
AI总结：
本文提出TODInv框架，通过在扩展P*空间内优化提示词嵌入实现真实图像反演与编辑，分层机制确保高保真度与精确可编辑性，实验证明其性能优于现有方法。
摘要：
近期文本引导扩散模型的进展解锁了强大的图像操控能力，然而平衡真实图像的重建保真度与可编辑性仍是一项重大挑战。在本研究中，我们提出了任务导向扩散反演（TODInv），这是一种新颖的框架，通过在扩展的P*空间内优化提示词嵌入，对针对特定编辑任务定制的真实图像进行反演和编辑。通过在不同的U-Net层和时间步利用不同的嵌入，TODInv通过交互优化将反演和编辑无缝集成，确保了高保真度和精确的可编辑性。这种分层编辑机制将任务分为结构、外观和全局编辑，仅优化不受当前编辑任务影响的嵌入。在基准数据集上的大量实验表明，TODInv的性能优于现有方法，在提供定量和定性增强的同时，展示了其在少步扩散模型上的多功能性。
英文摘要：
Recent advancements in text-guided diffusion models have unlocked powerful image manipulation capabilities, yet balancing reconstruction fidelity and editability for real images remains a significant challenge. In this work, we introduce \textbf{T}ask-\textbf{O}riented \textbf{D}iffusion \textbf{I}nversion (\textbf{TODInv}), a novel framework that inverts and edits real images tailored to specific editing tasks by optimizing prompt embeddings within the extended \(\mathcal{P}^*\) space. By leveraging distinct embeddings across different U-Net layers and time steps, TODInv seamlessly integrates inversion and editing through reciprocal optimization, ensuring both high fidelity and precise editability. This hierarchical editing mechanism categorizes tasks into structure, appearance, and global edits, optimizing only those embeddings unaffected by the current editing task. Extensive experiments on benchmark dataset reveal TODInv's superior performance over existing methods, delivering both quantitative and qualitative enhancements while showcasing its versatility with few-step diffusion model.
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