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@ -43,10 +43,13 @@ class DDIM(Scheduler):
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else tensor(data=[0], device=self.device, dtype=self.dtype)
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),
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)
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current_scale_factor, previous_scale_factor = self.cumulative_scale_factors[timestep], (
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current_scale_factor, previous_scale_factor = (
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self.cumulative_scale_factors[timestep],
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(
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self.cumulative_scale_factors[previous_timestep]
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if previous_timestep > 0
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else self.cumulative_scale_factors[0]
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),
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)
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predicted_x = (x - sqrt(1 - current_scale_factor**2) * noise) / current_scale_factor
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denoised_x = previous_scale_factor * predicted_x + sqrt(1 - previous_scale_factor**2) * noise
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@ -112,9 +112,7 @@ def test_scheduler_remove_noise():
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noise = randn(1, 4, 32, 32)
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for step, timestep in enumerate(diffusers_scheduler.timesteps):
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diffusers_output = cast(
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Tensor, diffusers_scheduler.step(noise, timestep, sample).pred_original_sample
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) # type: ignore
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diffusers_output = cast(Tensor, diffusers_scheduler.step(noise, timestep, sample).pred_original_sample) # type: ignore
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refiners_output = refiners_scheduler.remove_noise(x=sample, noise=noise, step=step)
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assert allclose(diffusers_output, refiners_output, rtol=0.01), f"outputs differ at step {step}"
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