USM sharpening. borrowed from real-ESRGAN Input image: I; Blurry image: B. 1. K = I + weight * (I - B) 2. Mask = 1 if abs(I - B) > thresho

Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)

Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)

r""" Returns: `dict` of attention processors: A dictionary containing all attention processors used in the model with

r""" Returns: `dict` of attention processors: A dictionary containing all attention processors used in the model with

r""" Returns: `dict` of attention processors: A dictionary containing all attention processors used in the model with

Kai Zhang (github: https://github.com/cszn)

Kai Zhang (github: https://github.com/cszn)

Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of (1-beta) over time from t = [

x: image, NxcxHxW k: kernel, Nx1xhxw

x: image, NxcxHxW k: kernel, Nx1xhxw

calculate SSIM the same outputs as MATLAB's img1, img2: [0, 255]

blur + downsampling Args: x: HxWxC image, [0, 1]/[0, 255] k: hxw, double sf: down-scale factor Return: downsa

blur + downsampling Args: x: HxWxC image, [0, 1]/[0, 255] k: hxw, double sf: down-scale factor Return: downsa

r""" The `self.components` property can be useful to run different pipelines with the same weights and configurations without realloca

r""" The `self.components` property can be useful to run different pipelines with the same weights and configurations without realloca

r""" The `self.components` property can be useful to run different pipelines with the same weights and configurations without realloca

A method that will be available after add_flops_counting_methods() is called on a desired net object. Returns current mean flops consumpt

Convert the torso of the model to float16.

Convert the torso of the model to float16.

Convert the torso of the model to float32.

Convert the torso of the model to float32.