Addressing the lack of significant solar flare data, a Denoising Diffusion Probabilistic Model (DDPM) has been developed to generate synthetic images depicting various solar phenomena. The DDPM, trained on SDO/AIA data, supports machine learning models focusing on solar activity forecasting.

• DDPM used to produce synthetic representations of the sun, including flares of different scales.
• Generates realistic solar imagery, judged by cluster metrics, FID, and F1-score.
• Enhances base model training with synthetic data for improved event forecasting in imbalanced datasets.
• Potential for integration in deep learning applications and advancing physical research.

The use of DDPMs for creating synthetic solar imagery opens new horizons in solar data analysis and could significantly benefit the forecasting and study of solar activities. It shows how synthetic data can be a valuable asset in advancing scientific understanding and model training.