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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: Optional = None width: Optional = 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 to
None) -- The width in preprocessed frames of the video. If
None, 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.