In recent years, text-to-image generation models have gained immense popularity and widespread use. The increasing availability of computational resources has accelerated the development of more sophisticated methods, but this rapid growth has introduced a challenge: comparing models to determine which is best suited for a given task. Currently, this problem is often addressed through manual evaluations, where humans assess and rank model outputs. However, this approach is inefficient and time-consuming, requiring extensive human input and subjective judgment. With the exponential growth of text-to-image models, manually assessing each model’s output quality has become a Sisyphean task. Automated image quality assessment (IQA) models offer a promising alternative, enabling us to reduce reliance on subjective human evaluations and instead use predicted values as metrics that indicate how well a generated image may appeal to users. In this paper, we extend our previous research on predicting the Mean Opinion Score (MOS) for image quality and propose a novel, efficient method for evaluating the quality of text-to-image generation models. Our approach uses a ConvNeXt-based architecture, representing an upgrade to previous solutions, and provides robust and innovative metrics applicable to a wide range of text-to-image generation tasks. This model improves the speed and reliability of quality assessments, offering a scalable solution to meet the growing demand for automated evaluation in the text-to-image generation space.