r""" Sets the attention processor to use to compute attention. Parameters: processor (`dict` of `AttentionProcessor` or o

shift pixel for super-resolution with different scale factors Args: x: WxHxC or WxH sf: scale factor upper_left: shift dir

shift pixel for super-resolution with different scale factors Args: x: WxHxC or WxH sf: scale factor upper_left: shift dir

Save the current parameters for restoring later. Args: parameters: Iterable of `torch.nn.Parameter`; the parameters to be

Create sinusoidal timestep embeddings. :param timesteps: a 1-D Tensor of N indices, one per batch element. These may be

Appends dimensions to the end of a tensor until it has target_dims dimensions.

Compute padding tuple.

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

generate an anisotropic Gaussian kernel Args: ksize : e.g., 15, kernel size theta : [0, pi], rotation angle range l1

generate an anisotropic Gaussian kernel Args: ksize : e.g., 15, kernel size theta : [0, pi], rotation angle range l1

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

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

bgr version of rgb2ycbcr only_y: only return Y channel Input: uint8, [0, 255] float, [0, 1]

Calculation of the statistics used by the FID. Params: -- files : List of image files paths -- model : Instance of inception m

Calculates the FID of two paths

Numpy implementation of the Frechet Distance. The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1) and X_2 ~ N(mu_2, C_2

Create translation motion field Args: motion_field_strength_x: motion strength along x-axis motion_field_strength_y: motion

Creates translation motion and warps the latents accordingly Args: motion_field_strength_x: motion strength along x-axis mot

r""" Download and cache a PyTorch diffusion pipeline from pretrained pipeline weights. Parameters: pretrained_model_name

r""" Download and cache a PyTorch diffusion pipeline from pretrained pipeline weights. Parameters: pretrained_model_name

r""" Download and cache a PyTorch diffusion pipeline from pretrained pipeline weights. Parameters: pretrained_model_name

Download a Google Drive file from and place it in root. Args: file_id (str): id of file to be downloaded root (str): Directory t

r""" Enable sliced attention computation. When this option is enabled, the attention module splits the input tensor in slices to compu

r""" Enable sliced attention computation. When this option is enabled, the attention module splits the input tensor in slices to compu

r""" Enable sliced attention computation. When this option is enabled, the attention module splits the input tensor in slices to compu

r""" Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared to `enable_sequenti

r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`, *optional*):

r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`, *optional*):

Perform DDPM forward process from time t0 to t1. This is the same as adding noise with corresponding variance. Args: x_t

Calculates the activations of the pool_3 layer for all images. Params: -- files : List of image files paths -- model : Instan

Simple helper method to retrieve class object of module as well as potential parent class objects