r""" Convert the model output to the corresponding type the UniPC algorithm needs. Args: model_output (`torch.Tensor`):

Convert the diffusion network's x0 prediction to noise predidction. x0: the predicted clean data with shape [B, C, H, W] xt:

Generate image/videos from noise and train the critic with generated samples. The noisy input to the generator is backward simulated.

r""" Initialize model parameters using Xavier initialization.

r""" Initialize model parameters using Xavier initialization.

One step for the UniC (B(h) version). Args: this_model_output (`torch.Tensor`): The model outputs at `x_

One step for the UniP (B(h) version). Alternatively, `self.solver_p` is used if is specified. Args: model_output (`torch

A few custom initialization steps that should be called after the object is created. Currently, the only one we have is to bind a few

Predict the sample from the previous timestep by reversing the SDE. This function propagates the sample with the multistep UniPC.

r""" Reconstruct video tensors from patch embeddings. Args: x (List[Tensor]): List of patchified features

Base class for denoising loss. Input: - x: the clean data with shape [B, F, C, H, W] - x_pred: the predicted

r""" Initialize the diffusion model backbone. Args: model_type (`str`, *optional*, defaults to 't2v'): Mo

r""" Initialize the diffusion model backbone. Args: model_type (`str`, *optional*, defaults to 't2v'): Mo