  Diffusion models have achieved remarkable advancements in text-to-image
generation. However, existing models still have many difficulties when faced
with multiple-object compositional generation. In this paper, we propose
RealCompo, a new training-free and transferred-friendly text-to-image
generation framework, which aims to leverage the respective advantages of
text-to-image models and spatial-aware image diffusion models (e.g., layout,
keypoints and segmentation maps) to enhance both realism and compositionality
of the generated images. An intuitive and novel balancer is proposed to
dynamically balance the strengths of the two models in denoising process,
allowing plug-and-play use of any model without extra training. Extensive
experiments show that our RealCompo consistently outperforms state-of-the-art
text-to-image models and spatial-aware image diffusion models in
multiple-object compositional generation while keeping satisfactory realism and
compositionality of the generated images. Notably, our RealCompo can be
seamlessly extended with a wide range of spatial-aware image diffusion models
and stylized diffusion models. Our code is available at:
https://github.com/YangLing0818/RealCompo
