While diffusion models have shown great success in image generation, their noise-inverting generative process does not explicitly consider the structure of images, such as their inherent multi-scale nature. Inspired by diffusion models and the empirical success of coarse-to-fine modelling, we propose a new diffusion-like model that generates images through stochastically reversing the heat equation, a PDE that locally erases fine-scale information when run over the 2D plane of the image. We interpret the solution of the forward heat equation with constant additive noise as a variational approximation in the diffusion latent variable model. Our new model shows emergent qualitative properties not seen in standard diffusion models, such as disentanglement of overall colour and shape in images. Spectral analysis on natural images highlights connections to diffusion models and reveals an implicit coarse-to-fine inductive bias in them.
翻译:虽然扩散模型在图像生成方面表现出巨大的成功,但其噪声反转基因过程并未明确考虑到图像的结构,例如其固有的多尺度性质。受扩散模型和粗质到软质建模成功经验的启发,我们提出了一种新的扩散模型,通过对热方程进行分解,生成图像,这种模型在当地清除了在图像2D平面上运行的微小信息。我们将远热方程式的解决方案解释为扩散潜质变异模型中的一种变异性近似。我们的新模型显示了标准扩散模型中未见的新兴质量特性,如图像中整体颜色和形状的分解。关于自然图像的透视分析突出了与扩散模型的连接,并揭示了这些图像中隐含的粗质到细的感性偏差。