Function get_sigmas_polyexponential

k_diffusion/sampling.py:32–36

Constructs an polynomial in log sigma noise schedule.

(n, sigma_min, sigma_max, rho=1., device='cpu')

def get_sigmas_polyexponential(n, sigma_min, sigma_max, rho=1., device='cpu'):
    """Constructs an polynomial in log sigma noise schedule."""
    ramp = torch.linspace(1, 0, n, device=device) ** rho
    sigmas = torch.exp(ramp * (math.log(sigma_max) - math.log(sigma_min)) + math.log(sigma_min))
    return append_zero(sigmas)


def get_sigmas_vp(n, beta_d=19.9, beta_min=0.1, eps_s=1e-3, device='cpu'):