Text to Image Diffusion Model

Text-to-image diffusion models generate images from textual descriptions, aiming for high-fidelity and precise alignment. Current research focuses on improving controllability, addressing safety concerns (e.g., preventing generation of inappropriate content), and enhancing personalization capabilities through techniques like continual learning and latent space manipulation. These advancements are significant for various applications, including medical imaging, artistic creation, and data augmentation, while also raising important ethical considerations regarding model safety and bias.