  We present SinDiffusion, leveraging denoising diffusion models to capture
internal distribution of patches from a single natural image. SinDiffusion
significantly improves the quality and diversity of generated samples compared
with existing GAN-based approaches. It is based on two core designs. First,
SinDiffusion is trained with a single model at a single scale instead of
multiple models with progressive growing of scales which serves as the default
setting in prior work. This avoids the accumulation of errors, which cause
characteristic artifacts in generated results. Second, we identify that a
patch-level receptive field of the diffusion network is crucial and effective
for capturing the image's patch statistics, therefore we redesign the network
structure of the diffusion model. Coupling these two designs enables us to
generate photorealistic and diverse images from a single image. Furthermore,
SinDiffusion can be applied to various applications, i.e., text-guided image
generation, and image outpainting, due to the inherent capability of diffusion
models. Extensive experiments on a wide range of images demonstrate the
superiority of our proposed method for modeling the patch distribution.
