Video Processor
The VideoProcessor provides a unified API for video pipelines to prepare inputs for VAE encoding and post-processing outputs once they’re decoded. The class inherits VaeImageProcessor so it includes transformations such as resizing, normalization, and conversion between PIL Image, PyTorch, and NumPy arrays.
VideoProcessor
diffusers.video_processor.VideoProcessor.preprocess_video
< source >( video height: typing.Optional[int] = None width: typing.Optional[int] = None )
Parameters
- video (
List[PIL.Image],List[List[PIL.Image]],torch.Tensor,np.array,List[torch.Tensor],List[np.array]) — The input video. It can be one of the following:- List of the PIL images.
- List of list of PIL images.
- 4D Torch tensors (expected shape for each tensor
(num_frames, num_channels, height, width)). - 4D NumPy arrays (expected shape for each array
(num_frames, height, width, num_channels)). - List of 4D Torch tensors (expected shape for each tensor
(num_frames, num_channels, height, width)). - List of 4D NumPy arrays (expected shape for each array
(num_frames, height, width, num_channels)). - 5D NumPy arrays: expected shape for each array
(batch_size, num_frames, height, width, num_channels). - 5D Torch tensors: expected shape for each array
(batch_size, num_frames, num_channels, height, width).
- height (
int, optional, defaults toNone) — The height in preprocessed frames of the video. IfNone, will use theget_default_height_width()to get default height. - width (
int, optional, defaults toNone) -- The width in preprocessed frames of the video. IfNone, will use get_default_height_width()to get the default width.
Preprocesses input video(s).
diffusers.video_processor.VideoProcessor.postprocess_video
< source >( video: Tensor output_type: str = 'np' )
Converts a video tensor to a list of frames for export.