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Use torch.special.expm1
This function provides greater precision than `exp(x) - 1` for small values of `x`. Found with TorchFix https://github.com/pytorch-labs/torchfix/
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@ -40,7 +40,7 @@ def get_sigmas_polyexponential(n, sigma_min, sigma_max, rho=1., device='cpu'):
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def get_sigmas_vp(n, beta_d=19.9, beta_min=0.1, eps_s=1e-3, device='cpu'):
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"""Constructs a continuous VP noise schedule."""
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t = torch.linspace(1, eps_s, n, device=device)
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sigmas = torch.sqrt(torch.exp(beta_d * t ** 2 / 2 + beta_min * t) - 1)
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sigmas = torch.sqrt(torch.expm1(beta_d * t ** 2 / 2 + beta_min * t))
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return append_zero(sigmas)
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