Validates that the input is a list of expected type.

Get a term for the variational lower-bound. The resulting units are bits (rather than nats, as one might expect). This allows

Get a term for the variational lower-bound. The resulting units are bits (rather than nats, as one might expect). This allows

Convert a BGR image to YCbCr image. The bgr version of rgb2ycbcr. It implements the ITU-R BT.601 conversion for standard-definition telev

embed_dim: output dimension for each position pos: a list of positions to be encoded: size (M,) out: (M, D)

Read an image from bytes. Args: content (bytes): Image bytes got from files or other streams. flag (str): Flags specifying the co

Take the mean over all non-batch dimensions.

Sampling x0 & t based on shape of x1 (if needed) Args: x1 - data point; [batch, *dim]

r""" Set the attention processor to use. Args: processor (`AttnProcessor`): The attention processor to us

Initializes Unit3D module.

A fast approximation of the cumulative distribution function of the standard normal.

Wrapper for grid_sample, uses pixel coordinates

Build loss from options. Args: opt (dict): Configuration. It must contain: type (str): Model type.

Calculate weights and indices, used for imresize function. Args: in_length (int): Input length. out_length (int): Output length.

apply GPT3-Turbo as the combination for original caption and the prompted captions for a video

Compute what the p_mean_variance output would have been, should the model's score function be conditioned by cond_fn. See con

Compute what the p_mean_variance output would have been, should the model's score function be conditioned by cond_fn. See con

Estimate a covariance matrix given data. Covariance indicates the level to which two variables vary together. If we examine N-dimensional sam

cubic function used for calculate_weights_indices.

Compute the log-likelihood of a Gaussian distribution discretizing to a given image. :param x: the target images. It is assumed that this

input: * tokens: batch_size x nheads x ntokens x dim * positions: batch_size x ntokens x 2 (y and x posit

grid_size: int of the grid height and width return: pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or