Text Guided Diffusion Model

Text-guided diffusion models are generative AI systems that create images, videos, 3D models (including molecules), and audio based on textual descriptions. Current research focuses on improving the fidelity and controllability of these models, exploring techniques like prompt engineering, embedding manipulation within diffusion model architectures (e.g., U-Nets), and ensembling multiple models to enhance generation quality and address limitations such as object count accuracy and spatial layout preservation. These advancements have significant implications for various fields, including medical imaging, drug discovery, surgical training, and creative content generation, by enabling the synthesis of realistic and diverse data where real-world data is scarce or difficult to obtain.