By simulating the human brain, artificial intelligence-generated image models have the ability to learn various features from text memory, such as color, shape, and corresponding semantics. However, the quality of the generated images can be inconsistent due to various influencing factors. Therefore, the objective of this study is to evaluate the color quality of generated images depicting natural objects, such as sky, grass, fallen leaves, and rocks, based on memory colors. The memory color of the object in the reference image, obtained through the Baidu crawler, serves as the benchmark for comparison with the generated image. Alternatively, the color quality assessment of the generated images was performed by constructing H-S polar scatterplots, analyzing lab color distribution, and calculating average color differences. The GauGAN and ERNIE-ViLG models demonstrate higher accuracy in reproducing the memory color of the four objects, with the exception of the “fallen leaves” model, which exhibits more significant color differences. Furthermore, a survey was conducted to assess the recognition of color reproduction effects. The results indicate that the color presentation of the objects “grass,” “fallen leaves,” and “rocks” aligns with the observed color differences. These findings provide valuable insights for optimizing the color-quality rendering of text-generated image models.