  Along with the nearing completion of the Square Kilometre Array (SKA), comes
an increasing demand for accurate and reliable automated solutions to extract
valuable information from the vast amount of data it will allow acquiring.
Automated source finding is a particularly important task in this context, as
it enables the detection and classification of astronomical objects.
Deep-learning-based object detection and semantic segmentation models have
proven to be suitable for this purpose. However, training such deep networks
requires a high volume of labeled data, which is not trivial to obtain in the
context of radio astronomy. Since data needs to be manually labeled by experts,
this process is not scalable to large dataset sizes, limiting the possibilities
of leveraging deep networks to address several tasks. In this work, we propose
RADiff, a generative approach based on conditional diffusion models trained
over an annotated radio dataset to generate synthetic images, containing radio
sources of different morphologies, to augment existing datasets and reduce the
problems caused by class imbalances. We also show that it is possible to
generate fully-synthetic image-annotation pairs to automatically augment any
annotated dataset. We evaluate the effectiveness of this approach by training a
semantic segmentation model on a real dataset augmented in two ways: 1) using
synthetic images obtained from real masks, and 2) generating images from
synthetic semantic masks. We show an improvement in performance when applying
augmentation, gaining up to 18% in performance when using real masks and 4%
when augmenting with synthetic masks. Finally, we employ this model to generate
large-scale radio maps with the objective of simulating Data Challenges.
