  Fonts have huge variations in their styles and give readers different
impressions. Therefore, generating new fonts is worthy of giving new
impressions to readers. In this paper, we employ diffusion models to generate
new font styles by interpolating a pair of reference fonts with different
styles. More specifically, we propose three different interpolation approaches,
image-blending, condition-blending, and noise-blending, with the diffusion
models. We perform qualitative and quantitative experimental analyses to
understand the style generation ability of the three approaches. According to
experimental results, three proposed approaches can generate not only expected
font styles but also somewhat serendipitous font styles. We also compare the
approaches with a state-of-the-art style-conditional Latin-font generative
network model to confirm the validity of using the diffusion models for the
style interpolation task.
