  Recent advances in generative artificial intelligence have enabled the
creation of high-quality synthetic data that closely mimics real-world data.
This paper explores the adaptation of the Stable Diffusion 2.0 model for
generating synthetic datasets, using Transfer Learning, Fine-Tuning and
generation parameter optimisation techniques to improve the utility of the
dataset for downstream classification tasks. We present a class-conditional
version of the model that exploits a Class-Encoder and optimisation of key
generation parameters. Our methodology led to synthetic datasets that, in a
third of cases, produced models that outperformed those trained on real
datasets.
