videos: A list of videos each with shape [C, T, H, W].

q: [B, Lq, Nq, C1]. k: [B, Lk, Nk, C1]. v: [B, Lk, Nk, C2]. Nq must be divisible by Nk. q_lens

Sets the discrete timesteps used for the diffusion chain (to be run before inference). Args: num_inference_steps (`int`):

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

x: [B, L, N, C]. grid_sizes: [B, 3]. freqs: [M, C // 2].

"Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the prediction of x_0 at t

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

Initialize the step_index counter for the scheduler.

Autoencoder3d adapted from Stable Diffusion 1.x, 2.x and XL.

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

r""" Args: x(Tensor): Shape [B, L1, C] e(Tensor): Shape [B, C]

Loads a video from the given path and returns it as a tensor with proper channel ordering. Args: video_path (str): Path t

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