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@ -38,6 +38,7 @@ class DPMSolver(Scheduler):
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)
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self.estimated_data = deque([tensor([])] * 2, maxlen=2)
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self.last_step_first_order = last_step_first_order
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self._first_step_has_been_run = False
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def _generate_timesteps(self) -> Tensor:
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# We need to use numpy here because:
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@ -80,7 +81,6 @@ class DPMSolver(Scheduler):
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previous_scale_factor = self.cumulative_scale_factors[previous_timestep]
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previous_noise_std = self.noise_std[previous_timestep]
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current_noise_std = self.noise_std[current_timestep]
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estimation_delta = (current_data_estimation - next_data_estimation) / (
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(current_ratio - next_ratio) / (previous_ratio - current_ratio)
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)
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@ -105,7 +105,8 @@ class DPMSolver(Scheduler):
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estimated_denoised_data = (x - noise_ratio * noise) / scale_factor
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self.estimated_data.append(estimated_denoised_data)
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if step == 0 or (self.last_step_first_order and step == self.num_inference_steps - 1):
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if step == 0 or (self.last_step_first_order and step == self.num_inference_steps - 1) or not self._first_step_has_been_run:
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self._first_step_has_been_run = True
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return self.dpm_solver_first_order_update(x=x, noise=estimated_denoised_data, step=step)
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return self.multistep_dpm_solver_second_order_update(x=x, step=step)
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