Challenges and Limitations in AI Image Generation

Despite its impressive capabilities, AI image generation still faces multiple challenges:

• Artifact Generation – AI models sometimes produce distorted or unrealistic features, especially in complex compositions.
• Bias and Ethical Issues – Training data can introduce **cultural, racial, or gender biases**, affecting AI outputs.
• Overfitting – Models trained on limited datasets may produce repetitive or over-optimized results, reducing diversity.
• Computational Costs – High-quality AI image generation requires significant GPU power, making it resource-intensive.
• Text Rendering Limitations – AI struggles with integrating readable text within images due to its difficulty in handling typography.

Ongoing research aims to mitigate these challenges by improving **data diversity, reducing biases, and refining image generation pipelines**.