f0_values: (batchsize, length, dim) where dim indicates fundamental tone and overtones

Initialize the step_index counter for the scheduler.

Initialize the step_index counter for the scheduler.

Resize, center crop, convert to tensor, and normalize.

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

Convert the model output to the corresponding type the DPMSolver/DPMSolver++ algorithm needs. DPM-Solver is designed to discretize an

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

One step for the first-order DPMSolver (equivalent to DDIM). Args: model_output (`torch.Tensor`): The dir

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

r""" Generates video frames from input image and text prompt using diffusion process. Args: frame_num (`int`, *optional*,

r""" Initialize model parameters using Xavier initialization.

r""" Initialize model parameters using Xavier initialization.

Inverse STFT => returns waveform shape (B, T).

Matches the color of a source video chunk to a reference image and blends with the original. Args: source_chunk (torch.Tensor): The

One step for the second-order multistep DPMSolver. Args: model_output_list (`List[torch.Tensor]`): The di

One step for the third-order multistep DPMSolver. Args: model_output_list (`List[torch.Tensor]`): The dir

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

Resize, center crop and normalize for decord loaded video (torch.Tensor type) Parameters: video - video to process (torch.

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