Conceptual Model and NLP-System "Text to Image"

Introduction. The development of theoretical tools and instrumental means of transforming text information into images is an urgent problem for various fields of human activity and organizational systems of various purposes. The article proposes a conceptual model and NLP system "Text to image" based on the methodology of system engineering of Data Science systems, architecture, and software of the image generation system based on the latent diffusion model. It is proposed to improve the basic architecture of the latent diffusion model by using a diffusion transformer. It is found that unlike approaches based on U-Net architecture, DiTs work with latent patches, providing better scalability and increased performance. The purpose of the work is to develop a scientifically based conceptual model and system for transforming text descriptions into images, based on the methodology of system engineering, modern methods of deep learning and business profile of Erikson – Penker. Results. Estimation problems, the properties of which are regulated by a parameter, have been constructed for the problem of placing objects in Euclidean space. The properties of the evaluation problem depending on the value of the parameter are studied and the limits of the value of the parameter are shown, the observance of which allows obtaining estimates adequate to the initial problem. Verification and validation of the developed NLP system "Text to image" for converting text data into images was carried out. The generation results demonstrate the exact reproduction of key elements, which indicates the high quality of the correspondence between the image and the text description. As a result of a comparative analysis of the performance of the models, it was determined that the TransformerLD system, although inferior to the Stable Diffusion and DALL-E 2 models in terms of FID and IS, still remains competitive. Conclusions. The construction of a dynamic branching tree and nonlinear estimations allows speeding up the process of finding the optimal solution, but it depends significantly on the initial problem, which complicates the development of a general algorithm. The development of the conceptual model and the NLP system "Text to image" allows implementing the effective transformation of text data into images, which is a topical issue in the field of data visualization. Keywords: system engineering, Data Science, NLP-systems “Text to image.