Deep generative models have achieved remarkable performance in various medical image-to-image translation tasks, including image reconstruction, denoising, and multimodal synthesis. However, these models typically learn to change the intensity of an image while preserving structure. In many medical image-to-image translation scenarios, there is often a significant deformation between the source and target images, such as the deformation of the lungs during breathing, adding an additional layer of complexity. Conventional generative models are not suited to capture spatial deformation. To address this, we propose a framework for medical image synthesis under large deformation which consists of two stages: the first stage predicts a dense displacement field to deform the moving image into the fixed image space, and the second stage predicts the intensity changes. We demonstrate our method on inspiratory-expiratory chest computed tomography images from a large cohort of nearly 500 subjects with varying degrees of disease severity. Ablation studies were conducted to understand the contribution of various model components. Our method achieved reliable alignment between the source and target images with a Dice similarity coefficient of 0.90 and a high multiscale structural similarity of 0.863 within the testing cohort.