  Text-to-image generation is conducted through Generative Adversarial Networks
(GANs) or transformer models. However, the current challenge lies in accurately
generating images based on textual descriptions, especially in scenarios where
the content and theme of the target image are ambiguous. In this paper, we
propose a method that utilizes artificial intelligence models for thematic
creativity, followed by a classification modeling of the actual painting
process. The method involves converting all visual elements into quantifiable
data structures before creating images. We evaluate the effectiveness of this
approach in terms of semantic accuracy, image reproducibility, and
computational efficiency, in comparison with existing image generation
algorithms.
