The fundamental question of how cells maintain their characteristic size remains open. Cell size measurements made through microscopic time-lapse imaging of microfluidic single-cell cultivations have seriously questioned classical cell growth models and are calling for newer, nuanced models that explain empirical findings better. Yet current models are limited in that they explain cellular growth either only in specific organisms and/or specific micro-environmental conditions. Together with the fact that tools for robust analysis of said time-lapse images are not widely available as yet, the previously mentioned point presents an opportunity to progress the cell growth and size homeostasis discourse through generative (probabilistic) modelling. Our contribution is a novel Model Framework for simulating microfluidic single-cell cultivations of rod-shaped bacteria with 36 different simulation modalities, each integrating dominant cell growth theories and generative modelling techniques. Our framework enables the simulation of diverse microscopic image sequences of the said class of single-cell cultivations as well as the generation of corresponding ground truths. More generally, our framework enables simulations of image sequences that imperfect camera and imaging conditions can produce, along with corresponding segmentation and tracking information. It thus enables the generation of datasets consisting of image sequence inputs and corresponding tabular labels, which can help develop robust machine image analysis networks applicable to real-world microfluidic experiments aimed at progressing the cell growth discourse. We demonstrate the usability of our framework through synthetic experiments and conclude by presenting its limitations as well as opportunities for further work.