Breast cancer is, currently, both the most prevalent one of the deadliest cancers worldwide. Successful treatment typically involves early diagnosis through non-invasive imaging techniques like Magnetic Resonance Imaging (MRI). Right away, a discrepancy can be identified regarding patient positioning during the two procedures: MRI acquisition is performed in prone while surgery is done in supine. The breast being a highly deformable tissue, this dichotomy may hinder the process of surgical planning and increase the risk of invasive procedure. As such, there is a clear gap in literature regarding prone-to-supine biomechanical modelling of breast deformation, a task very much dependent on the existence of large high-quality 3D MRI datasets. This work focuses on creating and presenting a generative framework to address the scarcity of said data through a low-cost two stage pipeline which combines 3D diffusion models to produce diverse lower-resolution breast MRI volumes and super-resolution (SR) architectures to ensure that samples meet clinical grade quality. The proposed pipeline successfully generated realistic and diverse 3D samples that match the underlying distribution of a small but clinically realistic dataset (89 patients). Thus, it proves how, even in lower-resource settings, it can be feasible to increase the availability of 3D breast MRI enough that a prone-to-supine biomechanical model could be trained.