Diffusion Based Text to Image

Diffusion-based text-to-image models aim to generate high-quality, realistic images from textual descriptions, focusing on improving image fidelity, controllability, and safety. Current research emphasizes enhancing the models' ability to accurately render text within images, mitigating biases and safety concerns (like generating unsafe content through prompt manipulation), and improving compositional generation of complex scenes with multiple objects. These advancements are significant for both the scientific community, pushing the boundaries of multimodal generation and AI safety, and for practical applications in creative content generation, design, and various other fields.