title: Continuous Layout Editing of Single Images with Diffusion Models
summary: 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.
paper_authors._id: 6494f40c4de6df100c83bfcd
paper_authors.name: Zhiyuan Zhang
paper_authors._id: 6494f40c4de6df100c83bfce
paper_authors.name: Zhitong Huang
paper_authors._id: 6494f40c4de6df100c83bfcf
paper_authors.name: Jing Liao
paper_title: Continuous Layout Editing of Single Images with Diffusion Models
paper_summary: 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.
paper_ai_summary: A novel framework using masked textual inversion and a training-free optimization method for layout editing of single images preserves visual properties and outperforms existing methods.
paper_ai_keywords: diffusion models layout editing masked textual inversion pre-trained diffusion model training-free optimization layout control visual properties single images