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import torch
import numpy as np
from functools import partial
from abc import abstractmethod
from ...util import append_zero
from ...modules.diffusionmodules.util import make_beta_schedule
def generate_roughly_equally_spaced_steps(
num_substeps: int, max_step: int
) -> np.ndarray:
return np.linspace(max_step - 1, 0, num_substeps, endpoint=False).astype(int)[::-1]
class Discretization:
def __call__(self, n, do_append_zero=True, device="cpu", flip=False):
sigmas = self.get_sigmas(n, device=device)
sigmas = append_zero(sigmas) if do_append_zero else sigmas
return sigmas if not flip else torch.flip(sigmas, (0,))
@abstractmethod
def get_sigmas(self, n, device):
pass
class EDMDiscretization(Discretization):
def __init__(self, sigma_min=0.02, sigma_max=80.0, rho=7.0):
self.sigma_min = sigma_min
self.sigma_max = sigma_max
self.rho = rho
def get_sigmas(self, n, device="cpu"):
ramp = torch.linspace(0, 1, n, device=device)
min_inv_rho = self.sigma_min ** (1 / self.rho)
max_inv_rho = self.sigma_max ** (1 / self.rho)
sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** self.rho
return sigmas
class LegacyDDPMDiscretization(Discretization):
def __init__(
self,
linear_start=0.00085,
linear_end=0.0120,
num_timesteps=1000,
):
super().__init__()
self.num_timesteps = num_timesteps
betas = make_beta_schedule(
"linear", num_timesteps, linear_start=linear_start, linear_end=linear_end
)
alphas = 1.0 - betas
self.alphas_cumprod = np.cumprod(alphas, axis=0)
self.to_torch = partial(torch.tensor, dtype=torch.float32)
def get_sigmas(self, n, device="cpu"):
if n < self.num_timesteps:
timesteps = generate_roughly_equally_spaced_steps(n, self.num_timesteps)
alphas_cumprod = self.alphas_cumprod[timesteps]
elif n == self.num_timesteps:
alphas_cumprod = self.alphas_cumprod
else:
raise ValueError
to_torch = partial(torch.tensor, dtype=torch.float32, device=device)
sigmas = to_torch((1 - alphas_cumprod) / alphas_cumprod) ** 0.5
return torch.flip(sigmas, (0,))
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