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Manipulate sigma values by applying multiplicative factors for fine-tuning AI model behavior and enhancing adaptability.
The MultiplySigmas node is designed to manipulate a sequence of sigma values, which are often used in sampling processes within AI models. This node allows you to apply a multiplicative factor to a specified range of these sigma values, effectively scaling them to achieve desired effects in your sampling or processing pipeline. By providing a flexible way to adjust the intensity of sigma values over a defined range, this node can be instrumental in fine-tuning the behavior of models, particularly in scenarios where the control of noise levels or detail emphasis is crucial. The main goal of the MultiplySigmas node is to offer a straightforward yet powerful method to modify sigma values, enhancing the adaptability and precision of your AI-driven projects.
The sigmas
parameter represents the sequence of sigma values that you wish to modify. These values are crucial in determining the behavior of sampling processes, and the node requires this input to perform its function. The parameter is mandatory, ensuring that the node has the necessary data to work with.
The factor
parameter is a floating-point value that determines the multiplicative factor applied to the sigma values within the specified range. By adjusting this factor, you can control the degree of scaling applied to the sigmas, which can influence the overall effect on the sampling process. The factor
has a default value of 1, with a minimum of 0 and a maximum of 100, allowing for a wide range of scaling options.
The start
parameter is a floating-point value that specifies the beginning of the range within the sigma sequence where the multiplication will be applied. It is expressed as a fraction of the total length of the sigma sequence, with a default value of 0, a minimum of 0, and a maximum of 1. This parameter allows you to precisely control where the scaling effect begins.
The end
parameter is a floating-point value that marks the end of the range within the sigma sequence for the multiplication effect. Like the start
parameter, it is expressed as a fraction of the total sigma sequence length. The default value is 1, with a minimum of 0 and a maximum of 1, enabling you to define the endpoint of the scaling effect accurately.
The output sigmas
parameter is the modified sequence of sigma values after the specified range has been multiplied by the given factor. This output is crucial as it reflects the changes made to the original sigma values, allowing you to use the adjusted sequence in subsequent processing or sampling tasks. The modified sigmas can significantly impact the behavior of your model, depending on how they are utilized in your workflow.
factor
close to 1 and gradually increase it to observe the impact on your model's output.start
and end
parameters to target specific portions of the sigma sequence, allowing for precise control over which parts of the process are affected by the scaling.sigmas
input is not a tensor or does not support the clone
method.sigmas
input is a tensor or a compatible data structure that supports the clone
method.start
or end
parameters are set incorrectly, leading to an invalid range.start
and end
parameters are within the valid range (0 to 1) and that start
is less than or equal to end
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