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

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

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.

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

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

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

Create a linear module.

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

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 p

DPM-Solver-1 (equivalent to DDIM) from time `s` to time `t`. Args: x: A pytorch tensor. The initial value at time `s`.

python code from: https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_fil

python code from: https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_fil

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

Singlestep solver DPM-Solver-2 from time `s` to time `t`. Args: x: A pytorch tensor. The initial value at time `s`.

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