Diffusion Based Generative Model

Diffusion-based generative models are a class of powerful AI models that create new data samples by reversing a diffusion process, gradually removing noise from random data until a realistic sample is obtained. Current research focuses on improving model efficiency and control, exploring architectures like transformers and UNets, and incorporating various conditioning mechanisms (e.g., text, depth maps, physical priors) to guide the generation process. These models are significantly impacting diverse fields, enabling advancements in image and video editing, speech enhancement, medical imaging, and even extreme video compression through their ability to generate high-quality, diverse, and often physically plausible data.