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)

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

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

Create a 1D, 2D, or 3D average pooling module.

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

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

Compute the entire variational lower-bound, measured in bits-per-dim, as well as other related quantities. :param model: the

Compute the entire variational lower-bound, measured in bits-per-dim, as well as other related quantities. :param model: the

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

Evaluate a function without caching intermediate activations, allowing for reduced memory at the expense of extra compute in the backward pas

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

End DDP training.

Performs all_gather operation on the provided tensors. *** Warning ***: torch.distributed.all_gather has no gradient.

Compute the log-likelihood of a continuous Gaussian distribution. :param x: the targets :param means: the Gaussian mean Tensor. :para

Create a 1D, 2D, or 3D convolution module.

Convert primitive modules to float16.

Convert primitive modules to float32, undoing convert_module_to_f16().

Convert the torso of the model to float16.

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.

Convert the torso of the model to float32.

Convert the torso of the model to float32.

Create a ScheduleSampler from a library of pre-defined samplers. :param name: the name of the sampler. :param diffusion: the diffusion ob

Sample x_{t+1} from the model using DDIM reverse ODE.

Sample x_{t+1} from the model using DDIM reverse ODE.

Generate samples from the model using DDIM. Same usage as p_sample_loop().

Generate samples from the model using DDIM. Same usage as p_sample_loop().

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], it

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" --

Get the device to use for torch.distributed.

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: downsam

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

Write all of the diagnostics from the current iteration

Apply the model to an input batch. :param x: an [N x C x ...] Tensor of inputs. :param timesteps: a 1-D batch of timesteps.