
Text-to-image generation relies entirely on the quality and diversity of its training datasets. The images, styles, and content it produces reflect the data it has seen, so gaps or biases in the dataset can lead to inaccurate, repetitive, or biased outputs. High-quality, diverse data results in more realistic and versatile images.


