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SUPIR / sgm /modules /diffusionmodules /denoiser_scaling.py

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import torch


class EDMScaling:
    def __init__(self, sigma_data=0.5):
        self.sigma_data = sigma_data

    def __call__(self, sigma):
        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):
        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):
        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

	import torch


	class EDMScaling:
	def __init__(self, sigma_data=0.5):
	self.sigma_data = sigma_data

	def __call__(self, sigma):
	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):
	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):
	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