| from abc import abstractmethod |
| from functools import partial |
|
|
| import numpy as np |
| import torch |
|
|
| from ...modules.diffusionmodules.util import make_beta_schedule |
| from ...util import append_zero |
|
|
|
|
| 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=0.002, 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,)) |
|
|
