Create a list of timesteps to use from an original diffusion process, given the number of timesteps we want to take from equally-sized portio

Create a list of timesteps to use from an original diffusion process, given the number of timesteps we want to take from equally-sized portio

Save the current parameters for restoring later. Args: parameters: Iterable of `torch.nn.Parameter`; the parameters to be

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

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

Drops labels to enable classifier-free guidance.

x: (N, T, patch_size**2 * C) imgs: (N, H, W, C)

Update the reweighting using losses from a model. Sub-classes should override this method to update the reweighting using lo

Update the reweighting using losses from a model. Sub-classes should override this method to update the reweighting using los

Get a numpy array of weights, one per diffusion step. The weights needn't be normalized, but must be positive.

Get a numpy array of weights, one per diffusion step. The weights needn't be normalized, but must be positive.

Zero out the parameters of a module and return it.