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ODE-based generative modeling: Learning from a single natural image
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AI
DOI：10.1016/j.eswa.2025.127185.png)
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Abstract
En 中文
Single image generation aims to learn the internal statistical distribution from a single natural image to generate diverse samples of arbitrary scales, serving as a tool for image manipulation tasks. Existing methods adopt the same pyramid structure for both training and multi-stage sampling to ensure the stability of the generation model. However, these methods result in a large number of sampling time steps and extra noise at each level of the pyramid to sample a single image. In this work, we propose a Single image generative model based on Ordinary Differential Equation (ODE), dubbed as SinODE. Instead of relying on a repetitive multi-stage sampling process, SinODE reformulates single image sampling as a unified integration framework, reducing sampling times while eliminating unnecessary noise injection. To that end, we build straight paths connecting Gaussian noise to scaled images and generating samples with a multiple piece- wise integration mechanism. Furthermore, our method can employ external text to control the direction of generation, producing personalized new content or style without requiring model fine-tuning. SinODE can also be effortlessly applied to other image manipulation tasks, such as image style transfer and harmonization. Extensive experiments demonstrate that SinODE surpasses current state-of-the-art methods, producing high-quality samples with exceptional diversity.
Keywords:
Single image generation
Diffusion models
Generative adversarial network
Journal
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7.5
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2.9W
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