Copy current parameters into given collection of parameters. Args: parameters: Iterable of `torch.nn.Parameter`; the parameters to be

Obtain normalization modules from the config file.

Sinusoid position encoding table

Parameters to determine the marginal distribution of the SDE, $p_t(x)$.

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)

Zero out the parameters of a module and return it.

Construct an SDE. Args: N: number of discretization time steps.

Sample the base classifier's prediction under noisy corruptions of the input x. :param x: the input [channel x width x height] :para

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

1x1 convolution

3x3 convolution with padding

3x3 convolution with padding

Extract coefficients from a based on t and reshape to make it broadcastable with x_shape.

Returns a flax optimizer object based on `config`.

Get sigmas --- the set of noise levels for SMLD from config files. Args: config: A ConfigDict object parsed from the config file Returns

for ablation_4, ablation_2