Learning to Generate Semantic Layouts for Higher Text-Image Correspondence in Text-to-Image Synthesis

TL;DR

This paper introduces a novel diffusion-based method that generates semantic layouts alongside images to improve text-image correspondence, especially in domain-specific datasets with limited paired data.

Contribution

It proposes a Gaussian-categorical diffusion process for joint image and layout generation, enhancing semantic understanding in text-to-image synthesis without large-scale paired datasets.

Findings

Improved text-image correspondence in experiments

Effective in domain-specific datasets with scarce pairs

Guides models to generate semantically aware images

Abstract

Existing text-to-image generation approaches have set high standards for photorealism and text-image correspondence, largely benefiting from web-scale text-image datasets, which can include up to 5~billion pairs. However, text-to-image generation models trained on domain-specific datasets, such as urban scenes, medical images, and faces, still suffer from low text-image correspondence due to the lack of text-image pairs. Additionally, collecting billions of text-image pairs for a specific domain can be time-consuming and costly. Thus, ensuring high text-image correspondence without relying on web-scale text-image datasets remains a challenging task. In this paper, we present a novel approach for enhancing text-image correspondence by leveraging available semantic layouts. Specifically, we propose a Gaussian-categorical diffusion process that simultaneously generates both images and corresponding layout pairs. Our experiments reveal that we can guide text-to-image generation models to be aware of the semantics of different image regions, by training the model to generate semantic labels for each pixel. We demonstrate that our approach achieves higher text-image correspondence compared to existing text-to-image generation approaches in the Multi-Modal CelebA-HQ and the Cityscapes dataset, where text-image pairs are scarce. Codes are available in this https://pmh9960.github.io/research/GCDP