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Load video files from specified path, simplifying import process for AI artists by handling validation and extraction tasks.
The VHS_LoadVideoPath
node is designed to load video files from a specified path, making it an essential tool for AI artists who work with video data. This node simplifies the process of importing video content into your workflow by handling the complexities of video file validation and loading. It ensures that the video file exists and is accessible, and it extracts key information such as frame count, audio data, and video metadata. By using this node, you can seamlessly integrate video content into your projects, enabling you to focus on creative tasks rather than technical details.
The video
parameter specifies the path to the video file you want to load. This parameter is crucial as it directs the node to the correct file location. The path must be valid and accessible; otherwise, the node will raise an error. There are no default values for this parameter, and it must be provided by the user.
The force_rate
parameter allows you to specify a custom frame rate for the video. This can be useful if you need to standardize the frame rate across different videos. The value should be an integer representing frames per second (fps). If not specified, the node will use the video's original frame rate.
The force_size
parameter lets you enforce a specific resolution for the video. This can be useful for ensuring consistency in video dimensions. The value should be a string in the format "widthxheight" (e.g., "1920x1080"). If not specified, the node will use the video's original resolution.
The custom_width
parameter allows you to set a custom width for the video frames. This is useful if you need to resize the video to a specific width while maintaining the aspect ratio. The value should be an integer representing the width in pixels. If not specified, the node will use the video's original width.
The custom_height
parameter allows you to set a custom height for the video frames. This is useful if you need to resize the video to a specific height while maintaining the aspect ratio. The value should be an integer representing the height in pixels. If not specified, the node will use the video's original height.
The frame_load_cap
parameter sets a limit on the number of frames to load from the video. This can be useful for performance optimization, especially when working with long videos. The value should be an integer representing the maximum number of frames to load. If not specified, the node will load all frames.
The skip_first_frames
parameter allows you to skip a specified number of initial frames in the video. This can be useful if you want to ignore the beginning of the video. The value should be an integer representing the number of frames to skip. If not specified, the node will start loading from the first frame.
The select_every_nth
parameter allows you to load every nth frame from the video. This can be useful for reducing the number of frames and focusing on key moments. The value should be an integer representing the interval of frames to load. If not specified, the node will load every frame.
The meta_batch
parameter is used for batch processing of video frames. This can be useful for handling large datasets or performing operations on multiple frames simultaneously. The value should be a batch object. If not specified, the node will process frames individually.
The unique_id
parameter allows you to assign a unique identifier to the video loading process. This can be useful for tracking and managing multiple video loading operations. The value should be a string representing the unique ID. If not specified, the node will generate a default ID.
The memory_limit_mb
parameter sets a memory limit for the video loading process. This can be useful for preventing memory overflow issues when working with large videos. The value should be an integer representing the memory limit in megabytes. If not specified, the node will use the system's available memory.
The vae
parameter allows you to specify a Variational Autoencoder (VAE) model for encoding the video frames. This can be useful for advanced video processing tasks. The value should be a VAE model object. If not specified, the node will not use a VAE model.
The IMAGE
output provides the loaded video frames as a sequence of images. This output is essential for further processing and analysis of the video content.
The frame_count
output returns the total number of frames loaded from the video. This information is useful for understanding the length and structure of the video.
The audio
output provides the audio data extracted from the video. This output is essential for tasks that require synchronization of audio and video content.
The video_info
output returns metadata about the video, including frame rate, resolution, and duration. This information is useful for understanding the properties of the video and for making informed decisions during processing.
The LATENT
output provides the encoded video frames if a VAE model is used. This output is essential for advanced video processing tasks that require latent representations of the video frames.
force_rate
and force_size
parameters to standardize the frame rate and resolution across different videos.frame_load_cap
parameter to optimize performance when working with long videos.skip_first_frames
parameter to focus on the relevant part of the video.select_every_nth
parameter to reduce the number of frames and focus on key moments in the video.<video_path>
<video_path>
memory_limit_mb
parameter or reduce the number of frames to load using the frame_load_cap
parameter.vae
parameter if encoding is needed.© Copyright 2024 RunComfy. All Rights Reserved.