Prepend each output line with process-specific prefix

Apply rotary embeddings to input tensors using the given frequency tensor. This function applies rotary embeddings to the given query or key

Create a logger that writes to a log file and stdout.

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

input: * tokens: batch_size x nheads x ntokens x dim * positions: batch_size x ntokens x 2 (y and x position of each

input: * tokens: batch_size x nheads x ntokens x dim * positions: batch_size x ntokens x 2 (y and x position of each

r"""Forward method of the `CogVideoXUpBlock3D` class.

Input tensors are assumed to be in shape [B, M, *]

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

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

Load a video from the given path and convert it to a PyTorch tensor.

Take the mean over all non-batch dimensions.

r""" Normalize the encoder hidden states. Requires `self.norm_cross` to be specified when constructing the `Attention` class.

r""" Prepare the attention mask for the attention computation. Args: attention_mask (`torch.Tensor`): The

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