r""" Reshape the tensor from `[batch_size, seq_len, dim]` to `[batch_size, seq_len, heads, dim // heads]` `heads` is the number of hea

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

r""" Reshape the tensor from `[batch_size, seq_len, dim]` to `[batch_size // heads, seq_len, dim * heads]`. `heads` is the number of h

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

r""" Compute the attention scores. Args: query (`torch.Tensor`): The query tensor. key (`torch.Tensor`): The

r""" Instantiate a pretrained PyTorch model from a pretrained model configuration. The model is set in evaluation mode - `model.eval(

r""" Get the attention processor in use. Args: return_deprecated_lora (`bool`, *optional*, defaults to `False`):

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

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

Compute padding tuple.

examples: logger = get_logger('try_logging.txt') logger.debug("Do something.") logger.info("Start print log.") l

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

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

r""" The call function to the pipeline for generation. Args: image (`PIL.Image.Image` or `List[PIL.Image.Image]` or `torc