PIL.Image -> Tensor[C, H, W], RGB, [-1, 1]

Tensor[C, H, W], RGB, [-1, 1] -> PIL.Image

Helper that gets a list of the model's attention modules. Each module has a `bias` buffer used for causal masking. The Prefix LM conv

Tokenize a list of strings.

Adaptive instance normalization. Adjust the reference features to have the similar color and illuminations as those in the degradate features.

Align and warp faces with face template.

Calculate mean and std for adaptive_instance_normalization. Args: feat (Tensor): 4D tensor. eps (float): A small value added to th

Clone a task queue @param task_queue: the task queue to be cloned @return: the cloned task queue

A counter for the `thop` package to count the operations in an attention operation. Meant to be used like: macs, params = thop.pr

Custom group norm with fixed mean and var @param input: input tensor @param num_groups: number of groups. by default, num_groups = 32

Generates a gaussian mask of weights for tile contributions

Get the best tile size for GPU memory

Get the index (e.g. 2) from the prediction (e.g. 'C')

Get the index (e.g. 2) from the prediction (e.g. 'C')

Load file form http url, will download models if necessary. Ref:https://github.com/1adrianb/face-alignment/blob/master/face_alignment/utils.py

img can be image path or cv2 loaded image.

Restore the parameters stored with the `store` method. Useful to validate the model with EMA parameters without affecting the

Split a list into n (roughly) equal-sized chunks

Apply wavelet decomposition to the input tensor. This function only returns the low frequency & the high frequency.

Apply wavelet decomposition, so that the content will have the same color as the style.

Zero out the parameters of a module and return it.