Trains a new DiT model.

Trains a new DiT model.

Run sampling.

r""" Normalize the encoder hidden states. Requires `self.norm_cross` to be specified when constructing the `Attention` class.

Sample x_{t-1} from the model at the given timestep. :param model: the model to sample from. :param x: the current tensor at

Generate samples from the model and yield intermediate samples from each timestep of diffusion. Arguments are the same as p_s

r""" Prepare the attention mask for the attention computation. Args: attention_mask (`torch.Tensor`): The

Get the distribution q(x_t | x_0). :param x_start: the [N x C x ...] tensor of noiseless inputs. :param t: the number of diff

Set requires_grad flag for all parameters in a model.

Set requires_grad flag for all parameters in a model.

Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles custom timesteps. Any kwargs

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 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 los

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

r""" Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`, *optional*):

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