#
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

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diffusers.video_processor.VideoProcessor.preprocess_video

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( 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 to `None`) — The height in preprocessed frames of the video. If `None`, will use the `get_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).

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diffusers.video_processor.VideoProcessor.postprocess_video

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( video: Tensor output_type: str = 'np' )

Parameters

- video (`torch.Tensor`) — The video as a tensor.
- output_type (`str`, defaults to `"np"`) — Output type of the postprocessed `video` tensor.

Converts a video tensor to a list of frames for export.

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