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@ -4,16 +4,16 @@ import torch
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import k_diffusion
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import k_diffusion
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import numpy as np
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from modules import shared
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def to_d(x, sigma, denoised):
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def to_d(x, sigma, denoised):
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"""Converts a denoiser output to a Karras ODE derivative."""
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"""Converts a denoiser output to a Karras ODE derivative."""
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return (x - denoised) / sigma
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return (x - denoised) / sigma
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k_diffusion.sampling.to_d = to_d
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k_diffusion.sampling.to_d = to_d
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import numpy as np
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from modules import shared
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@dataclasses.dataclass
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@dataclasses.dataclass
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class Scheduler:
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class Scheduler:
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name: str
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name: str
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