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

Importance-sample timesteps for a batch. :param batch_size: the number of timesteps. :param device: the torch device to save

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

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