High-quality synthetic medical images can enlarge training datasets in different deep learning-based applications. Recently, diffusion-based methods for image synthesis have outperformed GAN-based methods, even for medical images. Unfortunately, using diffusion models is costly in terms of training time and computational resources. We propose a two-stage method that combines diffusion models and GANs to tackle this problem. First, we use diffusion models or GANs to generate low-resolution images. Then, we use a GAN-based super-resolution model to interpolate high-resolution images from these low-resolution images. Experimental results on synthetic breast CT slices show that the proposed framework is more efficient and performs better than state-of-the-art methods that generate the images in a single step. The proposed methods will be available at https://github.com/xiaoerlaigeid/Image-Frequency-Score.git.