Abstract
It is well known that training a denoising score-based diffusion models requires tens of thousands of epochs and a substantial number of image data to train the model. In this paper, we propose to increase the efficiency in training score-based diffusion models. Our method allows us to decrease the number of epochs needed to train the diffusion model. We accomplish this by solving the log-density Fokker-Planck (FP) Equation numerically to compute the score \textit{before} training. The pre-computed score is embedded into the image to encourage faster training under slice Wasserstein distance. Consequently, it also allows us to decrease the number of images we need to train the neural network to learn an accurate score. We demonstrate through our numerical experiments the improved performance of our proposed method compared to standard score-based diffusion models. Our proposed method achieves a similar quality to the standard method meaningfully faster.
Abstract (translated)
训练基于去噪得分扩散模型的神经网络需要成千上万的训练迭代和大量的图像数据。在本文中，我们提出了一种提高训练基于去噪得分扩散模型的效率的方法。我们的方法允许我们减少训练扩散模型的迭代次数。我们通过数值求解对数密度Fokker-Planck（FP）方程来计算训练前得分。预计算得分被嵌入到图像中，以鼓励在切片Wasserstein距离下进行更快训练。因此，它还允许我们减少需要训练的神经网络图像数量，以学习更准确的得分。通过我们数值实验的结果，证明了与标准基于得分的扩散模型相比，我们所提出的方法的性能得到了显著提高。与标准方法相比，我们的方法具有相似的性能，但意味着更快的训练。
URL
https://arxiv.org/abs/2404.06661
