diffusers/main/en/api/video_processor.md

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.VideoProcessor.preprocess_video]]

diffusers.VideoProcessor.preprocess_video[[diffusers.VideoProcessor.preprocess_video]]

Source

Preprocesses input video(s). Keyword arguments will be forwarded to VaeImageProcessor.preprocess.

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.

Returns:

torch.Tensor` of shape `(batch_size, num_channels, num_frames, height, width)

A 5D tensor holding the batched channels-first video(s).

diffusers.VideoProcessor.postprocess_video[[diffusers.VideoProcessor.postprocess_video]]

Source

Converts a video tensor to a list of frames for export. Keyword arguments will be forwarded to VaeImageProcessor.postprocess.

Parameters:

video (torch.Tensor) : The video as a tensor.

output_type (str, defaults to "np") : Output type of the postprocessed video tensor.