Estimate the manifold of given feature vectors. :param session: the TensorFlow session. :param row_batch_size: row batch siz

Compute the entire variational lower-bound, measured in bits-per-dim, as well as other related quantities. :param model: the

Compute the log-likelihood of a continuous Gaussian distribution. :param x: the targets :param means: the Gaussian mean Tensor. :para

Create a ScheduleSampler from a library of pre-defined samplers. :param name: the name of the sampler. :param diffusion: the diffusion ob

Sample x_{t+1} from the model using DDIM reverse ODE.

Generate samples from the model using DDIM. Same usage as p_sample_loop().

Evaluate if new feature vectors are at the manifold.

Forward pass of DiT, but also batches the unconditional forward pass for classifier-free guidance.

Get a pre-defined beta schedule for the given name. The beta schedule library consists of beta schedules which remain similar in the limi

Compute training losses for a single timestep. :param model: the model to evaluate loss on. :param x_start: the [N x C x ...]

r"""Fills the input Tensor with values drawn from a truncated normal distribution. From: https://github.com/rwightman/pytorch-image-models

Update the reweighting using losses from a model. Call this method from each rank with a batch of timesteps and the correspon