Diffusion Generation

Diffusion generation leverages stochastic processes to create diverse and high-quality data across various modalities, aiming to improve the controllability and fidelity of generative models. Current research focuses on enhancing model architectures like diffusion probabilistic models (DDPMs) and incorporating techniques such as latent tree representations for 3D scenes, variational distribution mapping for text-to-3D generation, and classifier guidance for improved control. These advancements are impacting diverse fields, including image and 3D model generation, drug discovery, and gene therapy, by enabling the creation of realistic and targeted data where traditional methods fall short.