  Investigating the solar magnetic field is crucial to understand the physical
processes in the solar interior as well as their effects on the interplanetary
environment. We introduce a novel method to predict the evolution of the solar
line-of-sight (LoS) magnetogram using image-to-image translation with Denoising
Diffusion Probabilistic Models (DDPMs). Our approach combines "computer science
metrics" for image quality and "physics metrics" for physical accuracy to
evaluate model performance. The results indicate that DDPMs are effective in
maintaining the structural integrity, the dynamic range of solar magnetic
fields, the magnetic flux and other physical features such as the size of the
active regions, surpassing traditional persistence models, also in flaring
situation. We aim to use deep learning not only for visualisation but as an
integrative and interactive tool for telescopes, enhancing our understanding of
unexpected physical events like solar flares. Future studies will aim to
integrate more diverse solar data to refine the accuracy and applicability of
our generative model.
