  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 Dynamic Prompt Learning (DPL) to force
cross-attention maps to focus on correct 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 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.
