Efficient and Training-Free Single-Image Diffusion Models (arxiv.org) AI

The paper proposes a “training-free” single-image diffusion approach that models an input image using a finite dataset of its multi-scale patches, enabling an analytic closed-form denoiser instead of neural network training and improving generation quality and diversity; it demonstrates applications such as unconditional generation and text-guided stylization, and reports accelerations for megapixel to gigapixel outputs.