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improve consistency of the dpm scheduler
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@ -1,6 +1,6 @@
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from refiners.foundationals.latent_diffusion.schedulers.scheduler import Scheduler
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from refiners.foundationals.latent_diffusion.schedulers.scheduler import Scheduler
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import numpy as np
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import numpy as np
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from torch import Tensor, device as Device, tensor, exp
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from torch import Tensor, device as Device, tensor, exp, float32, dtype as Dtype
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from collections import deque
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from collections import deque
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@ -17,6 +17,7 @@ class DPMSolver(Scheduler):
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initial_diffusion_rate: float = 8.5e-4,
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initial_diffusion_rate: float = 8.5e-4,
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final_diffusion_rate: float = 1.2e-2,
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final_diffusion_rate: float = 1.2e-2,
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device: Device | str = "cpu",
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device: Device | str = "cpu",
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dtype: Dtype = float32,
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):
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):
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super().__init__(
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super().__init__(
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num_inference_steps=num_inference_steps,
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num_inference_steps=num_inference_steps,
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@ -24,6 +25,7 @@ class DPMSolver(Scheduler):
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initial_diffusion_rate=initial_diffusion_rate,
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initial_diffusion_rate=initial_diffusion_rate,
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final_diffusion_rate=final_diffusion_rate,
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final_diffusion_rate=final_diffusion_rate,
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device=device,
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device=device,
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dtype=dtype,
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)
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)
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self.estimated_data = deque([tensor([])] * 2, maxlen=2)
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self.estimated_data = deque([tensor([])] * 2, maxlen=2)
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self.initial_steps = 0
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self.initial_steps = 0
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@ -52,8 +54,8 @@ class DPMSolver(Scheduler):
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self.noise_std[previous_timestep],
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self.noise_std[previous_timestep],
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self.noise_std[timestep],
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self.noise_std[timestep],
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)
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)
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exp_factor = exp(-(previous_ratio - current_ratio))
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factor = exp(-(previous_ratio - current_ratio)) - 1.0
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denoised_x = (previous_noise_std / current_noise_std) * x - (previous_scale_factor * (exp_factor - 1.0)) * noise
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denoised_x = (previous_noise_std / current_noise_std) * x - (factor * previous_scale_factor) * noise
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return denoised_x
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return denoised_x
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def multistep_dpm_solver_second_order_update(self, x: Tensor, step: int) -> Tensor:
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def multistep_dpm_solver_second_order_update(self, x: Tensor, step: int) -> Tensor:
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@ -76,13 +78,13 @@ class DPMSolver(Scheduler):
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estimation_delta = (current_data_estimation - next_data_estimation) / (
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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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(current_ratio - next_ratio) / (previous_ratio - current_ratio)
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)
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)
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exp_neg_factor = exp(-(previous_ratio - current_ratio))
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factor = exp(-(previous_ratio - current_ratio)) - 1.0
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x_t = (
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denoised_x = (
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(previous_std / current_std) * x
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(previous_std / current_std) * x
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- (previous_scale_factor * (exp_neg_factor - 1.0)) * current_data_estimation
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- (factor * previous_scale_factor) * current_data_estimation
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- 0.5 * (previous_scale_factor * (exp_neg_factor - 1.0)) * estimation_delta
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- 0.5 * (factor * previous_scale_factor) * estimation_delta
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)
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)
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return x_t
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return denoised_x
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def __call__(
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def __call__(
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self,
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self,
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