RealCompo: Balancing Realism and Compositionality Improves Text-to-Image Diffusion Models

Xinchen Zhang, Ling Yang, YaQi Cai, Zhaochen Yu, Kai-Ni Wang, xie jiake, Ye Tian, Minkai Xu, Yong Tang, Yujiu Yang, Bin CUI

Keywords: Text-to-Image Diffusion, Layout-guided Image Diffusion

TL;DR: We propose a new training-free and transferred-friendly text-to-image generation framework to enhance the realism and compositionality of the generated images.

Abstract: 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. Code is available at: https://github.com/YangLing0818/RealCompo

Primary Area: Diffusion based models

Submission Number: 5087