Dynamic Prompt Learning: Addressing Cross-Attention Leakage for Text-Based Image Editing

Kai Wang, Fei Yang, Shiqi Yang, Muhammad Atif Butt, Joost van de Weijer

Keywords: Diffusion Models; Text-guided Image Edit; Textual Inversion; Localization

TL;DR: Existing text-guided image editing methods suffer from cross-attention leakage, we solve this problem by updating new added dynamic tokens for each noun word with the proposed losses. By this way, we achieve enhanced text-guided editing performance.

Abstract: Large-scale text-to-image generative models have been a ground-breaking development in generative AI, with diffusion models showing their astounding ability to synthesize convincing images following an input text prompt. The goal of image editing research is to give users control over the generated images by modifying the text prompt. Current image editing techniques are susceptible to unintended modifications of regions outside the targeted area, such as on the background or on distractor objects which have some semantic or visual relationship with the targeted object. According to our experimental findings, inaccurate cross-attention maps are at the root of this problem. Based on this observation, we propose $\textit{Dynamic Prompt Learning}$ ($DPL$) to force cross-attention maps to focus on correct $\textit{noun}$ words in the text prompt. By updating the dynamic tokens for nouns in the textual input with the proposed leakage repairment losses, we achieve fine-grained image editing over particular objects while preventing undesired changes to other image regions. Our method $DPL$, based on the publicly available $\textit{Stable Diffusion}$, is extensively evaluated on a wide range of images, and consistently obtains superior results both quantitatively (CLIP score, Structure-Dist) and qualitatively (on user-evaluation). We show improved prompt editing results for Word-Swap, Prompt Refinement, and Attention Re-weighting, especially for complex multi-object scenes.

Submission Number: 61