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V3D / sgm /modules /diffusionmodules /denoiser_scaling.py

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cfb7702 3 months ago

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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_data2 / (sigma2 + self.sigma_data**2)
	c_out = sigma * self.sigma_data / (sigma2 + self.sigma_data2) ** 0.5
	c_in = 1 / (sigma2 + self.sigma_data2) ** 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 / (sigma2 + 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 / (sigma2 + 1.0) 0.5
	c_in = 1.0 / (sigma2 + 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 / (sigma2 + 1.0) 0.5
	c_in = 1.0 / (sigma2 + 1.0) 0.5
	c_noise = 0.25 * sigma.log()
	return c_skip, c_out, c_in, c_noise