49 lines
1.1 KiB
Python
49 lines
1.1 KiB
Python
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import torch
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from scipy import integrate
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from ...util import append_dims
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class NoDynamicThresholding:
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def __call__(self, uncond, cond, scale):
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return uncond + scale.view(-1, 1, 1, 1) * (cond - uncond)
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def linear_multistep_coeff(order, t, i, j, epsrel=1e-4):
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if order - 1 > i:
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raise ValueError(f"Order {order} too high for step {i}")
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def fn(tau):
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prod = 1.0
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for k in range(order):
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if j == k:
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continue
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prod *= (tau - t[i - k]) / (t[i - j] - t[i - k])
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return prod
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return integrate.quad(fn, t[i], t[i + 1], epsrel=epsrel)[0]
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def get_ancestral_step(sigma_from, sigma_to, eta=1.0):
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if not eta:
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return sigma_to, 0.0
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sigma_up = torch.minimum(
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sigma_to,
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eta
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* (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5,
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)
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sigma_down = (sigma_to**2 - sigma_up**2) ** 0.5
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return sigma_down, sigma_up
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def to_d(x, sigma, denoised):
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return (x - denoised) / append_dims(sigma, x.ndim)
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def to_neg_log_sigma(sigma):
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return sigma.log().neg()
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def to_sigma(neg_log_sigma):
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return neg_log_sigma.neg().exp()
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