arXiv 2506.025602025-06-03cs.CV

DCI：用于增强基于扩散的图像编辑的双条件反演

DCI: Dual-Conditional Inversion for Boosting Diffusion-Based Image Editing

Institute of Information Science, Beijing Jiaotong University（信息科学学院，北京交通大学）；Visual Intelligence +X International Cooperation Joint Laboratory of MOE（视觉智能+X国际合作联合实验室）

Zixiang Li, Haoyu Wang, Wei Wang, Chuangchuang Tan, Yunchao Wei, Yao Zhao

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AI总结：

本文提出双条件反演（DCI）框架，通过联合源提示和参考图像将反演建模为双条件不动点优化，同时提升扩散模型图像编辑的重建精度与编辑灵活性，并在多个任务中取得最先进性能。

摘要：

扩散模型在图像生成和编辑任务中取得了显著成功。这些模型中的反演旨在恢复真实或生成图像的潜在噪声表示，从而实现重建、编辑和其他下游任务。然而，迄今为止，大多数反演方法在重建精度和编辑灵活性之间存在固有的权衡。这一局限性源于在反演过程中难以同时保持语义对齐和结构一致性。在这项工作中，我们提出了双条件反演（DCI），这是一个新颖的框架，它联合以源提示和参考图像为条件来引导反演过程。具体来说，DCI将反演过程表述为一个双条件不动点优化问题，在联合引导下同时最小化潜在噪声差距和重建误差。这种设计将反演轨迹锚定在语义空间和视觉空间中，从而产生更准确且更可编辑的潜在表示。我们的新颖设置带来了对反演过程的新理解。大量实验表明，DCI在多个编辑任务中实现了最先进的性能，显著提高了重建质量和编辑精度。此外，我们还证明了我们的方法在重建任务中取得了强劲的结果，这意味着其鲁棒性和泛化能力接近反演过程的最终目标。

英文摘要：

Diffusion models have achieved remarkable success in image generation and editing tasks. Inversion within these models aims to recover the latent noise representation for a real or generated image, enabling reconstruction, editing, and other downstream tasks. However, to date, most inversion approaches suffer from an intrinsic trade-off between reconstruction accuracy and editing flexibility. This limitation arises from the difficulty of maintaining both semantic alignment and structural consistency during the inversion process. In this work, we introduce Dual-Conditional Inversion (DCI), a novel framework that jointly conditions on the source prompt and reference image to guide the inversion process. Specifically, DCI formulates the inversion process as a dual-condition fixed-point optimization problem, minimizing both the latent noise gap and the reconstruction error under the joint guidance. This design anchors the inversion trajectory in both semantic and visual space, leading to more accurate and editable latent representations. Our novel setup brings new understanding to the inversion process. Extensive experiments demonstrate that DCI achieves state-of-the-art performance across multiple editing tasks, significantly improving both reconstruction quality and editing precision. Furthermore, we also demonstrate that our method achieves strong results in reconstruction tasks, implying a degree of robustness and generalizability approaching the ultimate goal of the inversion process.

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