多历史步随机微分方程反演用于具有优越区域感知的图像编辑
Multi-History-Step SDE Inversion for Image Editing with Superior Regional Awareness
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中文总结 AI 辅助
提出MIEdit，一种基于SDE反演的免训练图像编辑框架，通过多历史步方案和自动语义角度掩码实现高效、高质量编辑，并在EditEval++基准上超越现有方法。
中文摘要 AI 辅助
近年来，扩散随机微分方程（SDE）反演和无反演方法在免训练图像编辑中变得普遍，因为它们无需调参即可实现忠实重建。然而，现有方法效率低下、可塑性有限，且难以准确保留未编辑区域。为解决这些问题，我们提出了MIEdit，一种基于SDE反演的免训练编辑框架。MIEdit引入了预测-校正多历史步方案，以更少的步骤实现优越的编辑质量。我们进一步缓解了采样过程中多条件噪声残差与梯度项之间的异质性和冲突，提高了大幅编辑下的稳定性和编辑可塑性。MIEdit还包含反演时自动语义角度掩码（IASM）；它利用无分类器引导在反演过程中自动生成语义角度掩码，并在整个采样过程中应用这些掩码进行区域约束，无需额外用户输入。我们还构建了EditEval++（30个细粒度任务，1000多个图像-文本-掩码三元组）进行全面评估；实验表明MIEdit优于最先进的技术。项目页面：此https URL。
英文摘要
In recent years, diffusion stochastic differential equation (SDE) inversion and inversion-free methods have become prevalent for training-free image editing, as they can achieve faithful reconstruction without tuning. However, existing approaches remain inefficient, exhibit limited plasticity, and struggle to accurately preserve unedited regions. To address these issues, we propose MIEdit, a training-free editing framework based on SDE inversion. MIEdit introduces a predictor-corrector multi-history-step scheme to achieve superior editing quality with fewer steps. We further mitigate heterogeneity and conflict between the multi-conditioned noise residuals and gradient terms during sampling, improving stability and editing plasticity under large edits. MIEdit also includes Inversion-Time Automatic Semantic Angle Masking (IASM); it leverages classifier-free guidance to automatically generate semantic angle masks during inversion and applies them throughout the sampling process for regional constraints, without extra user inputs. We additionally construct EditEval++ (30 fine-grained tasks, 1,000+ image-text-mask triplets) for comprehensive evaluation; experiments show that MIEdit outperforms state-of-the-art techniques. Project page: https://whywwwzzzg.github.io/MIEdit/.
发表机构
- Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
- University of Chinese Academy of Sciences（中国科学院大学）
机构由 AI 辅助整理，请以论文原文为准。
