Text to Image Generation

Text-to-image generation aims to create realistic and diverse images from textual descriptions, focusing on improving controllability, efficiency, and factual accuracy. Current research emphasizes enhancing model architectures like diffusion models and leveraging large language models for prompt understanding and control, including methods for fine-grained manipulation of image components and styles. This field is significant for its potential impact on various applications, from creative content generation to assisting in scientific visualization and medical imaging, while also raising important questions about bias mitigation and factual accuracy in AI-generated content.