Efficient and Training-Free Single-Image Diffusion Models
We consider the problem of generating images whose internal structure -- defined by the distribution of patches across multiple scales -- matches that of a single reference image. Recent approaches address this problem by training a diffusion model on a single image. But even in this setting, training is computationally expensive and requires hours of optimization. Instead, we model the image using a dataset of its patches at different scales. As this dataset is finite and the dimensionality of its patches is small, the score function for a noisy patch can be computed tractably using an optimal, closed-form denoiser, eliminating the need for neural network training. We integrate this patc...
| Authors | Haojun Qiu, Kiriakos N. Kutulakos, David B. Lindell |
|---|---|
| Primary category | cs.CV |
| Published | 3 June 2026 |
| Last updated at source | 3 June 2026 |
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