Crop out black borders from the image

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.

Scale the image

Apply transformations in sequence: translation → cropping → scaling

Perform only translation

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""" 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