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@@ -276,7 +276,11 @@ class ScoreSdeVeScheduler(SchedulerMixin, ConfigMixin):
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# Make sure sigmas and timesteps have the same device and dtype as original_samples
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timesteps = timesteps.to(original_samples.device)
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sigmas = self.discrete_sigmas.to(original_samples.device)[timesteps]
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noise = torch.randn_like(original_samples) * sigmas[:, None, None, None]
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noise = (
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noise * sigmas[:, None, None, None]
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if noise is not None
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else torch.randn_like(original_samples) * sigmas[:, None, None, None]
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)
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noisy_samples = noise + original_samples
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return noisy_samples
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