## 1 Introduction
Classifying GAN structural variants, the main representative models include the following: the Deep Convolutional Generative Adversarial Network (DCGAN) [12; 13], which provides more stable training and is suitable for image classification; the Semi-Supervised Learning Generative Adversarial Network (SGAN) [14; 15], which improves sample generation quality and reduces training time; the Conditional Generative Adversarial Network (CGAN) [16; 17], which controls the generation of samples meeting specific labels; the Laplacian Pyramid Generative Adversarial Network (LAPGAN) [18; 19], which achieves high-quality fine image generation; and the Boundary Equilibrium Generative Adversarial Network (BEGAN) [20; 21], which simplifies the model training process. Classifying GAN loss function variants, models encompass the Wasserstein Generative Adversarial Network (WGAN) [22], which improves the stability of training by enhancing probability distribution metrics; the WGAN-GP (Wasserstein GAN with Gradient Penalty) [23; 24], a further refinement of the WGAN; the F-Divergence Generative Adversarial Network (F-Divergence GAN) [25], optimizing the problem of maximizing and minimizing values; and the Least Squares Generative Adversarial Network (LSGAN) [26], which enhances both the quality of generation and training stability. Furthermore, research on utilizing Generative Adversarial Networks for tasks related to automatic layout is on the rise [27]. StackGAN [28], by constructing a stacked network structure, achieved the generation of photo-realistic images corresponding to textual descriptions for the first time, with image sizes reaching 256 \(\times\) 256. [PERSON] et al. [29] introduced the Deep Attention mechanism into the generator of GAN, thus creating the AttnGAN model. This model utilizes attention-driven multi-stage refinement techniques, focusing on relevant descriptive words to synthesize fine-grained details in images, providing a reference for the text-to-image task in complex scenarios. [PERSON] et al. [30] proposed a Transformer-based diffusion model for layout generation. [PERSON] et al. [31] adopted a site-embedded generative adversarial network (ESGAN) for automated building layout generation. [PERSON] et al. [32] constructed an element-conditioned GAN for graphic layout generation conditioned on specified design elements. [PERSON] et al. [33] proposed the Attention and Condition-based Generative Adversarial Network (AC-GAN). The model, based on given text attributes, accomplishes image generation across various categories, including different clothing styles, colors, patterns, and necklines, and has been applied in design and production applications. The aforementioned methods have given rise to various GAN variant models, contributing to a more stable training process and a significant improvement in the quality of generated images. Nevertheless, the present generation of layouts that combine both rationality and aesthetic appeal encounters numerous challenges, including the deficiencies of current methodologies in structural logic, alignment of elements, handling of text-image relationships, and inadequate attention to element details and their mutual interactions within the page. To address these issues, this paper proposes a Transformer-based Generative Adversarial Network for page layout. Transformers [34; 35; 36; 37] exhibit excellent contextual modeling abilities and robust long-term memory capabilities in handling lengthy textual tasks. The experimental outcomes demonstrate that the TGAN surpasses other techniques in both subjective and objective evaluations for generating page layouts. The layouts produced by the TGAN exhibit superior element alignment, effective overlap avoidance, and enhanced overall layout quality and consistency, thereby offering a more efficient approach to automated page layout creation.