Continuous Layout Editing of Single Images with Diffusion Models
Zhiyuan Zhang Zhitong Huang Jing Liao
Recent advancements in large-scale text-to-image diffusion models have
enabled many applications in image editing. However, none of these methods have
been able to edit the layout of single existing images. To address this gap, we
propose the first framework for layout editing of a single image while
preserving its visual properties, thus allowing for continuous editing on a
single image. Our approach is achieved through two key modules. First, to
preserve the characteristics of multiple objects within an image, we
disentangle the concepts of different objects and embed them into separate
textual tokens using a novel method called masked textual inversion. Next, we
propose a training-free optimization method to perform layout control for a
pre-trained diffusion model, which allows us to regenerate images with learned
concepts and align them with user-specified layouts. As the first framework to
edit the layout of existing images, we demonstrate that our method is effective
and outperforms other baselines that were modified to support this task. Our
code will be freely available for public use upon acceptance.