Diffusion Step

The "diffusion step" in diffusion models refers to a single iteration in the iterative process of adding or removing noise from data, aiming to learn complex data distributions for generative tasks. Current research focuses on accelerating this process through techniques like asynchronous denoising, improved solvers (e.g., stochastic Adams methods), and curriculum learning strategies that prioritize coarse-to-fine refinement of generated outputs. These advancements are significantly impacting various fields, enabling real-time applications in areas such as image editing, medical imaging, and speech synthesis, while also improving the efficiency and quality of generative models across diverse domains.