Init. Args: path (str, optional): Path to saved model. Defaults to None. features (int, optional): Number of feat

Init. Args: features (int): number of features

Init. Args: features (int): number of features

Init. Args: features (int): number of features

Init. Args: features (int): number of features

Init. Args: width (int): desired output width height (int): desired output height resize_target (boo

Init. Args: path (str, optional): Path to saved model. Defaults to None. features (int, optional): Number of feat

Create a wrapper class for the forward SDE (VP type). *** Update: We support discrete-time diffusion models by implementing a picewi

Construct a DPM-Solver. We support both the noise prediction model ("predicting epsilon") and the data prediction model ("predicting x0").

Get the prior KL term for the variational lower-bound, measured in bits-per-dim. This term can't be optimized, as it only

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) > thre

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)

Rezise the sample to ensure the given size. Keeps aspect ratio. Args: sample (dict): sample size (tuple): image size R

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:

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

Convert the torso of the model to float16.

Convert the torso of the model to float32.

This is the degradation model of BSRGAN from the paper "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"

This is the degradation model of BSRGAN from the paper "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"

This is an extended degradation model by combining the degradation models of BSRGAN and Real-ESRGAN ---------- img: HXWXC, [0, 1]

This is the degradation model of BSRGAN from the paper "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"

This is the degradation model of BSRGAN from the paper "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"

Overwrite model.train with this function to make sure train/eval mode does not change anymore.

Overwrite model.train with this function to make sure train/eval mode does not change anymore.

Overwrite model.train with this function to make sure train/eval mode does not change anymore.

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

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