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Handle dynamic multi-value inputs for flexible and complex animations in AnimateDiff framework.
The ADE_MultivalDynamic node is designed to handle dynamic multi-value inputs within the AnimateDiff framework. This node is particularly useful for scenarios where you need to process or manipulate multiple values dynamically, allowing for more flexible and complex animations or effects. By leveraging this node, you can efficiently manage and apply a series of values, enhancing the versatility and control over your animation projects. The primary goal of this node is to provide a robust mechanism for handling dynamic data inputs, making it an essential tool for AI artists looking to create intricate and detailed animations.
This parameter accepts a list of dynamic values that the node will process. The function of this parameter is to provide the node with the necessary data to perform its operations. The impact of this parameter on the node's execution is significant, as it directly influences the output results. The values can vary widely depending on the specific use case, and there are no strict minimum or maximum values, allowing for a high degree of flexibility. The default value is typically an empty list, but it should be populated with relevant data for meaningful results.
This parameter determines the mode in which the input values will be processed. It can have several options, such as "average", "sum", "max", or "min", each affecting the node's behavior differently. For example, selecting "average" will compute the average of the input values, while "sum" will add them together. The choice of processing mode can significantly impact the final output, making it crucial to select the appropriate mode based on the desired outcome. The default value is usually "average".
This output parameter represents the result of the processing operation performed on the input values. Its function is to provide the final computed value based on the selected processing mode. The importance of this parameter lies in its role as the primary output of the node, which can then be used in subsequent nodes or operations. The interpretation of the output value depends on the processing mode chosen; for instance, it could be an average, sum, maximum, or minimum of the input values.
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