Efficient and Training-Free Single-Image Diffusion Models

Source: Hacker News

View PDF HTML (experimental) 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.

      Comments:
      CVPR 2026; Project Page: [this https URL](https://haojunqiu.github.io/efficient-SID/)
    

      Subjects:
      
        Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
    
      Cite as:
      [arXiv:2606.04299](https://arxiv.org/abs/2606.04299) [cs.CV]
    
    
       
      (or 
          [arXiv:2606.04299v1](https://arxiv.org/abs/2606.04299v1) [cs.CV] for this version)
      
    
    
       
                    [https://doi.org/10.48550/arXiv.2606.04299](https://doi.org/10.48550/arXiv.2606.04299)
          
          
              arXiv-issued DOI via DataCite (pending registration)

        
      
    


  

Submission history

From: Haojun Qiu [view email]
[v1] Wed, 3 Jun 2026 00:05:36 UTC (45,344 KB)