  Given the rarity of significant solar flares compared to smaller ones,
training effective machine learning models for solar activity forecasting is
challenging due to insufficient data. This study proposes using generative deep
learning models, specifically a Denoising Diffusion Probabilistic Model (DDPM),
to create synthetic images of solar phenomena, including flares of varying
intensities. By employing a dataset from the AIA instrument aboard the SDO
spacecraft, focusing on the 171 {\AA} band that captures various solar
activities, and classifying images with GOES X-ray measurements based on flare
intensity, we aim to address the data scarcity issue. The DDPM's performance is
evaluated using cluster metrics, Frechet Inception Distance (FID), and
F1-score, showcasing promising results in generating realistic solar imagery.
We conduct two experiments: one to train a supervised classifier for event
identification and another for basic flare prediction, demonstrating the value
of synthetic data in managing imbalanced datasets. This research underscores
the potential of DDPMs in solar data analysis and forecasting, suggesting
further exploration into their capabilities for solar flare prediction and
application in other deep learning and physical tasks.
