Composite Diffusion | whole >= Σparts
Vikram Jamwal Ramaneswaran S
For an artist or a graphic designer, the spatial layout of a scene is a
critical design choice. However, existing text-to-image diffusion models
provide limited support for incorporating spatial information. This paper
introduces Composite Diffusion as a means for artists to generate high-quality
images by composing from the sub-scenes. The artists can specify the
arrangement of these sub-scenes through a flexible free-form segment layout.
They can describe the content of each sub-scene primarily using natural text
and additionally by utilizing reference images or control inputs such as line
art, scribbles, human pose, canny edges, and more.
  We provide a comprehensive and modular method for Composite Diffusion that
enables alternative ways of generating, composing, and harmonizing sub-scenes.
Further, we wish to evaluate the composite image for effectiveness in both
image quality and achieving the artist's intent. We argue that existing image
quality metrics lack a holistic evaluation of image composites. To address
this, we propose novel quality criteria especially relevant to composite
generation.
  We believe that our approach provides an intuitive method of art creation.
Through extensive user surveys, quantitative and qualitative analysis, we show
how it achieves greater spatial, semantic, and creative control over image
generation. In addition, our methods do not need to retrain or modify the
architecture of the base diffusion models and can work in a plug-and-play
manner with the fine-tuned models.