The topic of Diffusion Models is a hot chapter in machine learning and image processing, offering iterative methods that start from random noise and end with a high quality synthesis of visual (or other!) content. This course will be given in a hybrid seminar format, starting with a series of lectures by the Lecturer (Michael Elad) and then proceeding as a seminar in which each participant studies a specific topic/paper and lectures about it in class.