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Initiate loop structure for repeated execution based on conditions, automating tasks in AI art projects.
The LoopOpen
node is designed to initiate a loop structure within your workflow, allowing for repeated execution of a set of nodes based on specified conditions. This node is particularly useful for tasks that require iterative processing, such as refining an image through multiple passes or applying a series of transformations until a desired outcome is achieved. By leveraging the LoopOpen
node, you can automate repetitive tasks, thereby enhancing efficiency and consistency in your AI art projects. The node works by defining the starting point of the loop and setting up the necessary parameters to control the loop's behavior, such as the data to be iterated over and auxiliary inputs.
This parameter specifies the number of iterations the loop should execute. It is an integer value with a default of 1, a minimum of 0, and a maximum of 100000. Adjusting this value controls how many times the loop will run, which can be useful for fine-tuning the iterative process to achieve the desired result.
These optional parameters allow you to pass initial values into the loop. Each initial_value
parameter can accept any type of data (*
), providing flexibility in what you can iterate over. These values are used within the loop and can be modified during each iteration, enabling complex data transformations and accumulations.
This input is used to pass the primary data that will be iterated over in the loop. It can be any type of data and is essential for tasks that require processing a dataset or a sequence of elements.
These auxiliary inputs allow you to pass additional data into the loop. Similar to initial_value
parameters, they can accept any type of data and are used to provide supplementary information or context needed for the loop's operations.
This output is a dictionary containing information about the loop, such as its current status and configuration. It is essential for connecting to the LoopClose
node to ensure the loop operates correctly and maintains its state across iterations.
This output provides the data that has been iterated over and potentially modified during the loop. It allows you to track changes and use the updated data in subsequent nodes.
These outputs correspond to the auxiliary inputs and provide the updated values after each iteration. They are useful for monitoring and utilizing any additional data that was passed into the loop.
remaining
parameter is set appropriately to control the number of iterations based on your specific task requirements.initial_value
and aux
parameters to pass all necessary data into the loop, enabling complex and multi-faceted iterative processes.LOOP
output to a LoopClose
node to properly manage the loop's execution and maintain state across iterations.<original_id>
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