E4C：通过利用高效CLIP引导增强基于文本的图像编辑的可编辑性
E4C: Enhance Editability for Text-Based Image Editing by Harnessing Efficient CLIP Guidance
AI总结：
本文提出E4C零样本图像编辑方法，通过双分支特征共享和随机门控CLIP引导优化，在保留源图像内容的同时增强文本对齐，有效解决现有方法中的对齐问题。
摘要：
基于扩散的图像编辑是一个复合过程，既需要保留源图像内容，又需要生成新内容或应用修改。尽管当前的编辑方法在文本引导下已有改进，但大多数方法仅专注于保留输入图像的信息，忽视了可编辑性以及与目标提示对齐的重要性。在本文中，我们优先考虑可编辑性，提出了一种零样本图像编辑方法，名为通过高效CLIP引导增强基于文本的图像编辑的可编辑性（E4C），该方法仅需推理阶段的优化即可显式增强可编辑性和文本对齐。具体而言，我们开发了一个统一的双分支特征共享流水线，能够在保留源图像结构或纹理的同时，允许另一分支根据编辑任务进行调整。我们进一步将CLIP引导集成到我们的流水线中，利用我们新颖的随机门控优化机制，高效增强与目标提示的语义对齐。全面的定量和定性实验表明，我们的方法有效解决了现有方法中普遍存在的文本对齐问题，同时保持了对源图像的保真度，并在广泛的编辑任务中表现良好。
英文摘要：
Diffusion-based image editing is a composite process of preserving the source image content and generating new content or applying modifications. While current editing approaches have made improvements under text guidance, most of them have only focused on preserving the information of the input image, disregarding the importance of editability and alignment to the target prompt. In this paper, we prioritize the editability by proposing a zero-shot image editing method, named \textbf{E}nhance \textbf{E}ditability for text-based image \textbf{E}diting via \textbf{E}fficient \textbf{C}LIP guidance (\textbf{E4C}), which only requires inference-stage optimization to explicitly enhance the edibility and text alignment. Specifically, we develop a unified dual-branch feature-sharing pipeline that enables the preservation of the structure or texture of the source image while allowing the other to be adapted based on the editing task. We further integrate CLIP guidance into our pipeline by utilizing our novel random-gateway optimization mechanism to efficiently enhance the semantic alignment with the target prompt. Comprehensive quantitative and qualitative experiments demonstrate that our method effectively resolves the text alignment issues prevalent in existing methods while maintaining the fidelity to the source image, and performs well across a wide range of editing 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。
