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Adjust image batch size for efficient processing and management in image-related tasks.
The ChangeImageBatchSize __Inspire node is designed to adjust the batch size of a given set of images, making it easier to manage and process image data in batches. This node is particularly useful when you need to ensure that your image batches are of a consistent size, either by expanding the batch with repeated images or trimming it to the desired number. The primary goal of this node is to provide flexibility in handling image batches, which can be crucial for various image processing and AI art generation tasks. By using this node, you can streamline your workflow and ensure that your image batches are optimized for subsequent processing steps.
This parameter represents the input image batch that you want to resize. It is expected to be in the format of a tensor containing multiple images. The function of this parameter is to provide the node with the image data that needs to be adjusted in terms of batch size.
This parameter specifies the desired batch size for the output image batch. It accepts integer values with a default of 1, a minimum of 1, and a maximum of 4096. The batch_size parameter determines how many images the output batch will contain. If the input batch has fewer images than the specified batch size, the last image in the batch will be repeated to meet the required size. Conversely, if the input batch has more images, it will be trimmed to match the specified batch size.
This parameter defines the mode of operation for resizing the image batch. Currently, the only available option is "simple". In this mode, the node either repeats the last image to expand the batch or trims the batch to the specified size. The mode parameter ensures that the resizing operation is performed according to the selected method.
The output parameter is the resized image batch, which is returned as a tensor. This tensor contains the images adjusted to the specified batch size, either by repeating the last image or trimming the batch. The output is crucial for ensuring that subsequent processing steps receive image batches of the desired size, facilitating smoother and more efficient workflows.
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