With the great success of text-conditioned diffusion models in creative
text-to-image generation, various text-driven image editing approaches have
attracted the attentions of many researchers. However, previous works mainly
focus on discreteness-sensitive instructions such as adding, removing or
replacing specific objects, background elements or global styles (i.e., hard
editing), while generally ignoring subject-binding but semantically
fine-changing continuity-sensitive instructions such as actions, poses or
adjectives, and so on (i.e., soft editing), which hampers generative AI from
generating user-customized visual contents. To mitigate this predicament, we
propose a spatio-temporal guided adaptive editing algorithm AdapEdit, which
realizes adaptive image editing by introducing a soft-attention strategy to
dynamically vary the guiding degree from the editing conditions to visual
pixels from both temporal and spatial perspectives. Note our approach has a
significant advantage in preserving model priors and does not require model
training, fine-tuning, extra data, or optimization. We present our results over
a wide variety of raw images and editing instructions, demonstrating
competitive performance and showing it significantly outperforms the previous
approaches.

为了生成用户定制的视觉内容，本文提出了一种时空引导自适应编辑算法 AdapEdit，通过引入软注意力策略从时空角度动态变化编辑条件对视觉像素的引导程度，成功实现了自适应图像编辑，并在各种原始图像和编辑指令上展示了竞争性能并显著超越了以前的方法。