Score Mismatching for Generative Modeling
Senmao Ye Fei Liu
We propose a new score-based model with one-step sampling. Previously,
score-based models were burdened with heavy computations due to iterative
sampling. For substituting the iterative process, we train a standalone
generator to compress all the time steps with the gradient backpropagated from
the score network. In order to produce meaningful gradients for the generator,
the score network is trained to simultaneously match the real data distribution
and mismatch the fake data distribution. This model has the following
advantages: 1) For sampling, it generates a fake image with only one step
forward. 2) For training, it only needs 10 diffusion steps.3) Compared with
consistency model, it is free of the ill-posed problem caused by consistency
loss. On the popular CIFAR-10 dataset, our model outperforms Consistency Model
and Denoising Score Matching, which demonstrates the potential of the
framework. We further provide more examples on the MINIST and LSUN datasets.
The code is available on GitHub.