Neural networks and deep learning are widely used for solving tasks in multiple domains, including computer vision. In the field of medical image processing, these approaches can bring efficient and fast diagnosis. However, there is a challenge associated with the lack of annotated training data needed to train the models. The collection and especially the annotation of such data can be time-consuming and expensive because of the required knowledge of a domain expert. In this work, we explore the usage of generative models for histological data synthesis that could complement existing training sets and possibly improve the performance of deep learning models. The main area of our research is the synthesis of tissue patches based on the nuclear atypia score used in breast cancer diagnostics. Nuclear atypia is usually manifested by enlarged cells and irregular shapes. We take advantage of diffusion probabilistic models used to synthesise patches of histopathological tissues with a specific atypia score. Finally, through evaluation, we demonstrate the strengths and weaknesses of such patch-level data enhancement for the task of nuclear atypia scoring.