Despite significant advancements in image customization with diffusion
models, current methods still have several limitations: 1) unintended changes
in non-target areas when regenerating the entire image; 2) guidance solely by a
reference image or text descriptions; and 3) time-consuming fine-tuning, which
limits their practical application. In response, we introduce a tuning-free
framework for simultaneous text-image-guided image customization, enabling
precise editing of specific image regions within seconds. Our approach
preserves the semantic features of the reference image subject while allowing
modification of detailed attributes based on text descriptions. To achieve
this, we propose an innovative attention blending strategy that blends
self-attention features in the UNet decoder during the denoising process. To
our knowledge, this is the first tuning-free method that concurrently utilizes
text and image guidance for image customization in specific regions. Our
approach outperforms previous methods in both human and quantitative
evaluations, providing an efficient solution for various practical
applications, such as image synthesis, design, and creative photography.

通过无需调整的方法，本文提出了一种同时利用文本和图像指导的图像定制方法，允许在几秒钟内精确编辑特定图像区域，通过创新的注意力混合策略，本方法在图像定制中表现优异，是一种高效的解决方案。