r""" Instantiate a [`ControlNetModel`] from [`UNet2DConditionModel`]. Parameters: unet (`UNet2DConditionModel`):

Generate captions given text embedding features. Returns list[L]. Args: features (`torch.Tensor` of shape `(B, L, D)`):

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

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

r""" Map label strings, *e.g.* from ImageNet, to corresponding class ids. Parameters: label (`str` or `dict` of `str`):

Takes in random seed and returns corresponding noise vector

Computes the solver order at each time step. Args: num_inference_steps (`int`): the number of diffusion

r""" Tokenize a list of prompts and return its tokens with weights of each token. No padding, starting or ending token is included.

r""" Tokenize a list of prompts and return its tokens with weights of each token. No padding, starting or ending token is included.

Creates a torch.Generator based on the random seed of this region

Get the list of modules that are relatively imported in a module file. Args: module_file (`str` or `os.PathLike`): The module file t

Get sample rate: Returns: `int`: sample rate of audio

Args: append_path: optional path to append to the tests dir path Return: The full path to the `tests` dir, so that the tests

When the length of tokens is a multiple of the capacity of the text encoder, it should be split into chunks and sent to the text encoder indi

When the length of tokens is a multiple of the capacity of the text encoder, it should be split into chunks and sent to the text encoder indi

Reads the current version in the __init__.

Update the version in all needed files.

Formats a user-agent string with basic info about a request.

Loads an ONNX Inference session with an ExecutionProvider. Default provider is `CPUExecutionProvider` Arguments: path (`

Returns the log probabilities of the rows from the (cumulative or non-cumulative) transition matrix for each latent pixel in `x_t`.