TODO - docstring here frames_or_frame_dir: (Union[str, Path, torch.Tensor]): Either a directory of images, or a tensor of shape (T,

TODO - docstring here frames_or_frame_dir: (Union[str, Path, torch.Tensor]): Either a directory of images, or a tensor of shape (T,

TODO - docstring here frames_or_frame_dir: (Union[str, Path, torch.Tensor]): Either a directory of images, or a tensor of shape (T,

Multistep DPM-Solver with the order `order` from time `t_prev_list[-1]` to time `t`. Args: x: A pytorch tensor. The initi

Return the noise prediction model.

Find the optimal update_preview_frequency value maximizing performance while minimizing the time between updates.

returns transformation matrix which transforms from pose to world

Updates paths inside attentions to the new naming scheme (local renaming)

Restore the parameters stored with the `store` method. Useful to validate the model with EMA parameters without affecting the

shift pixel for super-resolution with different scale factors Args: x: WxHxC or WxH sf: scale factor upper_left: shift dir

shift pixel for super-resolution with different scale factors Args: x: WxHxC or WxH sf: scale factor upper_left: shift dir

Singlestep solver DPM-Solver-3 from time `s` to time `t`. Args: x: A pytorch tensor. The initial value at time `s`.

grabs all text up to the first occurrence of ':' uses the grabbed text as a sub-prompt, and takes the value following ':' as weight if ':

Save the current parameters for restoring later. Args: parameters: Iterable of `torch.nn.Parameter`; the parameters to be

#Run settings diffusion_steps = int(ddimSteps) #@param [25, 50, 100, 250, 500, 1000] eta = 1.0 #@param {type: 'raw'}

Generate a video from a sequence of prompts and seeds. Optionally, add audio to the video to interpolate to the intensity of the audio.

See https://hydrusnetwork.github.io/hydrus/help/client_api.html for documentation.

Args: med_config (str): path for the mixture of encoder-decoder model's configuration file image_size (int): input im