Generative Text to Image Model

Generative text-to-image models synthesize images from textual descriptions, aiming to bridge the gap between human language and visual representation. Current research focuses on improving image quality, mitigating biases (like gender and racial stereotypes) present in training data, and enhancing safety by addressing vulnerabilities to adversarial attacks ("jailbreaking") that can generate inappropriate content. These models have significant implications for various fields, including creative design, gaming, and scientific visualization, but challenges remain in ensuring accuracy, fairness, and responsible use.