  Generating background scenes for salient objects plays a crucial role across
various domains including creative design and e-commerce, as it enhances the
presentation and context of subjects by integrating them into tailored
environments. Background generation can be framed as a task of text-conditioned
outpainting, where the goal is to extend image content beyond a salient
object's boundaries on a blank background. Although popular diffusion models
for text-guided inpainting can also be used for outpainting by mask inversion,
they are trained to fill in missing parts of an image rather than to place an
object into a scene. Consequently, when used for background creation,
inpainting models frequently extend the salient object's boundaries and thereby
change the object's identity, which is a phenomenon we call "object expansion."
This paper introduces a model for adapting inpainting diffusion models to the
salient object outpainting task using Stable Diffusion and ControlNet
architectures. We present a series of qualitative and quantitative results
across models and datasets, including a newly proposed metric to measure object
expansion that does not require any human labeling. Compared to Stable
Diffusion 2.0 Inpainting, our proposed approach reduces object expansion by
3.6x on average with no degradation in standard visual metrics across multiple
datasets.
