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fix ddpm and ddim __init__
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@ -14,10 +14,10 @@ class DDIM(Scheduler):
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dtype: Dtype = float32,
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) -> None:
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super().__init__(
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num_inference_steps,
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num_train_timesteps,
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initial_diffusion_rate,
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final_diffusion_rate,
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num_inference_steps=num_inference_steps,
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num_train_timesteps=num_train_timesteps,
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initial_diffusion_rate=initial_diffusion_rate,
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final_diffusion_rate=final_diffusion_rate,
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noise_schedule=noise_schedule,
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device=device,
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dtype=dtype,
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@ -16,7 +16,13 @@ class DDPM(Scheduler):
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final_diffusion_rate: float = 1.2e-2,
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device: Device | str = "cpu",
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) -> None:
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super().__init__(num_inference_steps, num_train_timesteps, initial_diffusion_rate, final_diffusion_rate, device)
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super().__init__(
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num_inference_steps=num_inference_steps,
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num_train_timesteps=num_train_timesteps,
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initial_diffusion_rate=initial_diffusion_rate,
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final_diffusion_rate=final_diffusion_rate,
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device=device,
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)
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def _generate_timesteps(self) -> Tensor:
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step_ratio = self.num_train_timesteps // self.num_inference_steps
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@ -50,10 +56,13 @@ class DDPM(Scheduler):
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else tensor(-(self.num_train_timesteps // self.num_inference_steps), device=self.device)
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),
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)
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current_cumulative_factor, previous_cumulative_scale_factor = (self.scale_factors.cumprod(0))[timestep], (
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current_cumulative_factor, previous_cumulative_scale_factor = (
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(self.scale_factors.cumprod(0))[timestep],
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(
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(self.scale_factors.cumprod(0))[previous_timestep]
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if step < len(self.timesteps) - 1
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else tensor(1, device=self.device)
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),
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)
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current_factor = current_cumulative_factor / previous_cumulative_scale_factor
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estimated_denoised_data = (
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