Diffusion Training

Diffusion training is a powerful technique for generating high-quality synthetic data, particularly images, by iteratively removing noise from random data points until a realistic sample is produced. Current research focuses on improving training efficiency through methods like data pruning, optimized noise schedules, and architectural innovations such as incorporating transformers and U-Net structures into diffusion models. These advancements are significantly impacting various fields, including medical imaging, super-resolution, and robotic control, by enabling the creation of large, high-fidelity datasets for training and evaluation of other machine learning models, and by improving the speed and efficiency of diffusion model training itself.