Co-GAN: A Text-to-Image Synthesis Model with Local and Integral Features

Text-to-Image synthesis is a promising technology that generates realistic images from textual descriptions by deep learning model. However, the state-of-the-art text-to-image synthesis models often struggle to balance the overall integrity and local diversity of objects with rich details, leading to unsatisfactory generation results of some domain-specific images, such as industrial applications. To address this issue, we propose Co-GAN, a text-to-image synthesis model that introduces two modules to enhance local diversity and maintain overall structural integrity respectively. Local Feature Enhancement (LFE) module improves the local diversity of generated images, while Integral Structural Maintenance (ISM) module ensures that the integral information is preserved. Furthermore, a cascaded central loss is proposed to address the instability during the generative training. To tackle the problem of incomplete image types in existing datasets, we create a new text-to-image synthesis dataset containing seven types of industrial components, and test the effects of various existing methods based on the dataset. The results of comparative and ablation experiments show that, compared with other current methods, the images generated by Co-GAN incorporate more details and maintain the integrity.