通过引导与调度改进基于扩散模型的图像编辑保真度
Improving Diffusion-Based Image Editing Faithfulness via Guidance and Scheduling
AI总结：
针对图像编辑中可编辑性与保真度的权权衡问题，提出保真度引导与调度（FGS）方法，通过加强输入信息保留和引入调度策略解决错位，在维持可编辑性的同时显著提升保真度，且兼容多种编辑方法。
摘要：
文本引导的扩散模型已成为高质量图像合成的基础，实现了动态图像编辑。在图像编辑中，两个关键方面是可编辑性（决定修改程度）和保真度（反映未更改元素的保留程度）。然而，由于可编辑性和保真度之间固有权衡，实现最佳结果具有挑战性。为此，我们提出保真度引导与调度（FGS），以在最小化对可编辑性影响的前提下增强保真度。FGS 结合保真度引导来加强对输入图像信息的保留，并引入调度策略以解决可编辑性与保真度之间的错位。实验结果表明，FGS 在保持可编辑性的同时实现了更优的保真度。此外，它与多种编辑方法的兼容性使其能够跨多样任务实现精确、高质量的图像编辑。
英文摘要：
Text-guided diffusion models have become essential for high-quality image synthesis, enabling dynamic image editing. In image editing, two crucial aspects are editability, which determines the extent of modification, and faithfulness, which reflects how well unaltered elements are preserved. However, achieving optimal results is challenging because of the inherent trade-off between editability and faithfulness. To address this, we propose Faithfulness Guidance and Scheduling (FGS), which enhances faithfulness with minimal impact on editability. FGS incorporates faithfulness guidance to strengthen the preservation of input image information and introduces a scheduling strategy to resolve misalignment between editability and faithfulness. Experimental results demonstrate that FGS achieves superior faithfulness while maintaining editability. Moreover, its compatibility with various editing methods enables precise, high-quality image edits 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。
