Learning non-deterministic dynamics and intrinsic factors from images obtained through physical experiments is at the intersection of machine learning and material science. Disentangling the origins of uncertainties involved in microstructure growth, for example, is of great interest because future states vary due to thermal fluctuation and other environmental factors. To this end we propose a flow-based image-to-image model, called Flow U-Net with Squeeze modules (FUNS), that allows us to disentangle the features while retaining the ability to generate highquality diverse images from condition images. Our model successfully captures probabilistic phenomena by incorporating a U-Net-like architecture into the flowbased model. In addition, our model automatically separates the diversity of target images into condition-dependent/independent parts. We demonstrate that the quality and diversity of the images generated for microstructure growth and CelebA datasets outperform existing variational generative models.