E4C: Enhance Editability for Text-Based Image Editing by Harnessing Efficient CLIP Guidance
Tianrui Huang, Pu Cao, Lu Yang, Chun Liu, Mengjie Hu, Zhiwei Liu, Qing Song · IEEE Transactions on Circuits and Systems for Video Technology · 2025
Diffusion-based image editing involves both preserving the source image content and generating new content or applying modifications. Although current editing approaches have made improvements under text guidance, they have two key drawbacks: overemphasis on retaining original image info, neglecting editability and text alignment, and inability to handle both structure-consistent and non-rigid editing tasks. In this paper, we propose a zero-shot image editing method, named Enhance Editability for text-based image Editing via Efficient CLIP guidance (E4C), which presents an innovative adaptive feature sharing mechanism to enable multi-task editing. Additionally, a novel random gateway mechanism is designed to efficiently introduce CLIP guidance into the multi-step sampling of diffusion, achieving high congruence between editing results and target text. 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.