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[Bug] scheduling_ddpm: fix variance in the case of learned_range type. (#2090)

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scheduling_ddpm: fix variance in the case of learned_range type.

In the case of learned_range variance type, there are missing logs
and exponent comparing to the theory (see "Improved Denoising Diffusion
Probabilistic Models" section 3.1 equation 15:
https://arxiv.org/pdf/2102.09672.pdf).
This commit is contained in:
Dudu Moshe
2023-02-03 10:42:42 +02:00
committed by GitHub
parent 2bbd532990
commit ecadcdefe1
1 changed files with 5 additions and 2 deletions
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7
src/diffusers/schedulers/scheduling_ddpm.py
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@@ -218,8 +218,8 @@ class DDPMScheduler(SchedulerMixin, ConfigMixin):
elif variance_type == "learned":
return predicted_variance
elif variance_type == "learned_range":
min_log = variance
max_log = self.betas[t]
min_log = torch.log(variance)
max_log = torch.log(self.betas[t])
frac = (predicted_variance + 1) / 2
variance = frac * max_log + (1 - frac) * min_log
@@ -304,6 +304,9 @@ class DDPMScheduler(SchedulerMixin, ConfigMixin):
)
if self.variance_type == "fixed_small_log":
variance = self._get_variance(t, predicted_variance=predicted_variance) * variance_noise
elif self.variance_type == "learned_range":
variance = self._get_variance(t, predicted_variance=predicted_variance)
variance = torch.exp(0.5 * variance) * variance_noise
else:
variance = (self._get_variance(t, predicted_variance=predicted_variance) ** 0.5) * variance_noise