  Image composition in image editing involves merging a foreground image with a
background image to create a composite. Inconsistent lighting conditions
between the foreground and background often result in unrealistic composites.
Image harmonization addresses this challenge by adjusting illumination and
color to achieve visually appealing and consistent outputs. In this paper, we
present a novel approach for image harmonization by leveraging diffusion
models. We conduct a comparative analysis of two conditional diffusion models,
namely Classifier-Guidance and Classifier-Free. Our focus is on addressing the
challenge of adjusting illumination and color in foreground images to create
visually appealing outputs that seamlessly blend with the background. Through
this research, we establish a solid groundwork for future investigations in the
realm of diffusion model-based image harmonization.
