## 1 Introduction
Recently, text-to-image transformations have been successfully implemented in the field of computer science using various conditional GANs (cGANs) [19]. GAN-INT-CLS [20] first utilized cGANs to generate 64 \(\times\) 64 images based on textual descriptions. Furthermore, for enhanced image resolution and the efficient utilization of computing resources, StackGAN [21] and StackGAN++ [22] proposed a multi-stage approach to generate 256 \(\times\) 256 images by combining multiple generator and discriminator pairs. However, the problem of inconsistency in the details of the generated images remains. To address this limitation, [PERSON] et al. [23] further enhanced the multi-stage generation process by incorporating a text-to-image-to-text framework to align the images with the corresponding descriptions. However, the complexity and entanglements introduced by the multi-stage framework render effective training challenging. To overcome these problems, [PERSON] et al. [24] proposed DF-GAN, which adopts a single-stage framework (One-Way Output) for direct high-resolution image generation, enabling end-to-end training. Moreover, within the end-to-end training framework, various fusion methods have been introduced to facilitate the generation of refined visual content. [PERSON] et al. [25] proposed semantic condition batch normalization by adapting a text fusion method to align individual image regions with words in the description. In contrast, DiverGAN [26] employs the attention mechanism to fuse text and image features for semantically consistent image generation. However, these approaches are not directly applicable to generating conceptual sketches from text semantics due to the inherent sparsity of sketches compared to color images. Consequently, generating high-quality architectural sketches from text semantics is difficult owing to the sparsity of sketches. As mentioned earlier, existing AI-assisted architectural conceptual designs require a more refined method for conveying design concepts or user preferences, and text-to-image transformation is a promising solution to this limitation. It is crucial to develop an effective text fusion method to enable the generation of high-quality sketches. Therefore, two interconnected sub-modules, the design concept extraction (DCE) and architectural appearance generation (AAG) modules, are proposed in this paper for the intelligent design of high-rise building concepts from textual descriptions. The DCE module employs a contrastive learning framework to extract abstract design concepts from textual descriptions, thus facilitating the matching of corresponding image-text pairs. Thereafter, the AAG module translates the extracted text semantics into a spectrum of realistic and diverse high-rise building concepts. To generate semantically coherent and visually realistic high-rise conceptual sketches, the AAG module employs a novel deep sparse text fusion GAN (DSTF-GAN) to generate semantically coherent and visually realistic high-rise conceptual sketches by integrating the inherent sparsity of sketches and extracted semantic information with image features across both channel and spatial dimensions. Additionally, the AAG module incorporates a pre-trained latent stable diffusion model [27] into a pre-trained rendering (PR) module to render the generated sketches into finalized images. The novelty of this study is summarized as follows: (1) A pioneering AI-assisted approach for autonomously designing high-rise building concepts from text descriptions is proposed; (2) a generic model is introduced to obtain a pre-trained text encoder through contrastive learning, facilitating the effective matching of sketches with their descriptions; and (3) a novel generative adversarial network that integrates sketch sparsity and text semantics to generate high-rise conceptual sketches is proposed. The remainder of this article is organized as follows. Section 2 elaborates on the proposed framework for intelligent design based on textual descriptions.