  Denoising diffusion models (DDMs) have led to staggering performance leaps in
image generation, editing and restoration. However, existing DDMs use very
large datasets for training. Here, we introduce a framework for training a DDM
on a single image. Our method, which we coin SinDDM, learns the internal
statistics of the training image by using a multi-scale diffusion process. To
drive the reverse diffusion process, we use a fully-convolutional light-weight
denoiser, which is conditioned on both the noise level and the scale. This
architecture allows generating samples of arbitrary dimensions, in a
coarse-to-fine manner. As we illustrate, SinDDM generates diverse high-quality
samples, and is applicable in a wide array of tasks, including style transfer
and harmonization. Furthermore, it can be easily guided by external
supervision. Particularly, we demonstrate text-guided generation from a single
image using a pre-trained CLIP model.
