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| import torch | |
| from torch import Tensor | |
| class BaseScheduler: | |
| def alpha(self, t) -> Tensor: | |
| ... | |
| def sigma(self, t) -> Tensor: | |
| ... | |
| def dalpha(self, t) -> Tensor: | |
| ... | |
| def dsigma(self, t) -> Tensor: | |
| ... | |
| def dalpha_over_alpha(self, t) -> Tensor: | |
| return self.dalpha(t) / self.alpha(t) | |
| def dsigma_mul_sigma(self, t) -> Tensor: | |
| return self.dsigma(t)*self.sigma(t) | |
| def drift_coefficient(self, t): | |
| alpha, sigma = self.alpha(t), self.sigma(t) | |
| dalpha, dsigma = self.dalpha(t), self.dsigma(t) | |
| return dalpha/(alpha + 1e-6) | |
| def diffuse_coefficient(self, t): | |
| alpha, sigma = self.alpha(t), self.sigma(t) | |
| dalpha, dsigma = self.dalpha(t), self.dsigma(t) | |
| return dsigma*sigma - dalpha/(alpha + 1e-6)*sigma**2 | |
| def w(self, t): | |
| return self.sigma(t) | |