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Running
on
Zero
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from abc import ABC, abstractmethod
from typing import Tuple
import torch
class DenoiserScaling(ABC):
@abstractmethod
def __call__(
self, sigma: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
pass
class EDMScaling:
def __init__(self, sigma_data: float = 0.5):
self.sigma_data = sigma_data
def __call__(
self, sigma: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
c_skip = self.sigma_data**2 / (sigma**2 + self.sigma_data**2)
c_out = sigma * self.sigma_data / (sigma**2 + self.sigma_data**2) ** 0.5
c_in = 1 / (sigma**2 + self.sigma_data**2) ** 0.5
c_noise = 0.25 * sigma.log()
return c_skip, c_out, c_in, c_noise
class EpsScaling:
def __call__(
self, sigma: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
c_skip = torch.ones_like(sigma, device=sigma.device)
c_out = -sigma
c_in = 1 / (sigma**2 + 1.0) ** 0.5
c_noise = sigma.clone()
return c_skip, c_out, c_in, c_noise
class VScaling:
def __call__(
self, sigma: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
c_skip = 1.0 / (sigma**2 + 1.0)
c_out = -sigma / (sigma**2 + 1.0) ** 0.5
c_in = 1.0 / (sigma**2 + 1.0) ** 0.5
c_noise = sigma.clone()
return c_skip, c_out, c_in, c_noise
class VScalingWithEDMcNoise(DenoiserScaling):
def __call__(
self, sigma: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
c_skip = 1.0 / (sigma**2 + 1.0)
c_out = -sigma / (sigma**2 + 1.0) ** 0.5
c_in = 1.0 / (sigma**2 + 1.0) ** 0.5
c_noise = 0.25 * sigma.log()
return c_skip, c_out, c_in, c_noise
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