Expand the tensor `v` to the dim `dims`. Args: `v`: a PyTorch tensor with shape [N]. `dim`: a `int`. Returns: a P

Create a 1D, 2D, or 3D convolution module.

Load model from file. Args: path (str): file path

Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T].

Compute sigma_t of a given continuous-time label t in [0, T].

Compute log(alpha_t) of a given continuous-time label t in [0, T].

Convert the model to the noise prediction model or the data prediction model.

Creates a dataset with the specified parameters. Args: dataset_str: A dataset string description (e.g. ImageNet:split=TRAIN).

Dump model and checkpointables to a file. Args: name (str): name of the file. kwargs (dict): extra arbitrary

Performs a single optimization step. Args: closure (callable, optional): A closure that reevaluates the model and

Compute the intermediate time steps for sampling. Args: skip_type: A `str`. The type for the spacing of the time steps. We support

Compute the continuous-time label t in [0, T] of a given half-logSNR lambda_t.

Create a linear module.

Creates a data loader with the specified parameters. Args: dataset: A dataset (third party, LaViDa or WebDataset). batch_siz

Make a standard normalization layer. :param channels: number of input channels. :return: an nn.Module for normalization.

Setup logging. Args: output: A file name or a directory to save log files. If None, log files will not be saved. If outp

Zero out the parameters of a module and return it.

Init. Args: scale_factor (float): scaling mode (str): interpolation mode

Evaluate a function without caching intermediate activations, allowing for reduced memory at the expense of extra compute in the backward pas

Returns: True if distributed training is enabled

Calculating the high-frequency map.