Efficient Denoising using Score Embedding in Score-based Diffusion
  Models
Andrew S. Na William Gao Justin W. L. Wan
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.