Extract values from a 1-D numpy array for a batch of indices. :param arr: the 1-D numpy array. :param timesteps: a tensor of indices int

Extract values from a 1-D numpy array for a batch of indices. :param arr: the 1-D numpy array. :param timesteps: a tensor of indices into

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

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

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

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

Write the sequence of args, with no separators, to the console and output files (if you've configured an output file).

Take the mean over all non-batch dimensions.

Take the mean over all non-batch dimensions.

Create a linear module.

Create a linear module.

Zero out the parameters of a module and return it.

Apply the model to get p(x_{t-1} | x_t), as well as a prediction of the initial x, x_0. :param model: the model, which takes

Apply the model to get p(x_{t-1} | x_t), as well as a prediction of the initial x, x_0. :param model: the model, which takes

Compute the mean and variance of the diffusion posterior: q(x_{t-1} | x_t, x_0)

Compute the mean and variance of the diffusion posterior: q(x_{t-1} | x_t, x_0)

Zero out the parameters of a module and return it.

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

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

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