Efficient and Training-Free Single-Image Diffusion Models

• URL: http://arxiv.org/abs/2606.04299v1
• Date: Wed, 03 Jun 2026 00:05:36 GMT
• Title: Efficient and Training-Free Single-Image Diffusion Models
• Abstract summary: We consider the problem of generating images whose internal structure matches that of a single reference image.<n>We integrate a patch-based denoiser into an efficient, training-free image diffusion model.<n>Our approach achieves state-of-the-art generation quality and diversity compared to trained single-image diffusion models.
• Score: 17.578119446864132
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 patch-based denoiser into an efficient, training-free image diffusion model, and we describe how our method connects to classical patch-based image restoration techniques. Our approach achieves state-of-the-art generation quality and diversity compared to trained single-image diffusion models, and we demonstrate applications, including unconditional image generation, text-guided stylization, image symmetrization, and retargeting. Further, we show that our approach is compatible with latent space diffusion, and we show multiple additional acceleration techniques to achieve megapixel single-image generation in one second, and gigapixel generation in minutes.

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