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Count latent samples in datasets for video processing or AI art generation, simplifying workflow and ensuring data accuracy.
The VHS_GetLatentCount
node is designed to help you determine the number of latent samples in a given dataset. This node is particularly useful when working with latent representations in video processing or AI art generation, where understanding the size of your latent data can be crucial for subsequent processing steps. By providing a straightforward method to count the latent samples, this node simplifies the workflow and ensures you have accurate information about your data, which can be essential for tasks such as batching, merging, or selecting specific latent samples.
This parameter expects a dictionary containing latent samples. The dictionary should have a key named samples
which holds the actual latent data. The function of this parameter is to provide the node with the latent data that needs to be counted. There are no specific minimum, maximum, or default values for this parameter, as it depends on the latent data you are working with.
This output parameter returns an integer representing the number of latent samples in the provided dataset. The count is crucial for understanding the size of your latent data, which can influence how you manage and process the data in subsequent steps.
samples
with the latent data to avoid errors.samples
.samples
and that it holds the latent data.samples
key has a value of None.samples
key is not None.© Copyright 2024 RunComfy. All Rights Reserved.