The generative diffusion model has been highlighted as a state-of-the-art artificial intelligence technique for image synthesis. Here, we show that a denoising diffusion probabilistic model (DDPM), which is currently a popular diffusion technique, can be used for a domain-specific task generating fundus photographs. The trained DDPM successfully generated synthetic fundus photographs with a resolution of 128 × 128 pixels. We failed to train the DDPM for 256-by-256-pixel images due to the limited computation capacity. In a comparative analysis, the progressive growing generative adversarial network (PGGAN) model synthesized more sharpened images than the DDPM in the retinal vessels and optic discs. Because the DDPM has disadvantages, including difficulty in training and low image quality compared with generative adversarial networks such as PGGAN, further studies are needed to improve diffusion models for domain-specific medical tasks. This is the first study to use a domain-specific generative diffusion model to synthesize fundus photographs.