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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]]
diffusers.video_processor.VideoProcessor.preprocess_video[[diffusers.video_processor.VideoProcessor.preprocess_video]]
Preprocesses input video(s).
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.
diffusers.video_processor.VideoProcessor.postprocess_video[[diffusers.video_processor.VideoProcessor.postprocess_video]]
Converts a video tensor to a list of frames for export.
Parameters:
video (torch.Tensor) : The video as a tensor.
output_type (str, defaults to "np") : Output type of the postprocessed video tensor.
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