  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.
