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Enhance AI model conditioning with adjustable guidance levels for optimal output control.
FlexGuidance is a node designed to enhance the conditioning process in AI models by allowing users to adjust the guidance level applied during the conditioning phase. This node is particularly useful for fine-tuning the influence of guidance on the model's output, providing a flexible approach to control the strength of guidance applied. By offering the option to bypass the guidance embedder, FlexGuidance gives users the ability to either apply a specified guidance value or disable it entirely, depending on the desired outcome. This flexibility is crucial for artists and developers who wish to experiment with different levels of guidance to achieve optimal results in their AI-generated art or other creative projects.
This parameter represents the initial conditioning input that the node will process. It is a crucial component as it forms the basis upon which guidance adjustments are applied. The conditioning input is typically derived from previous nodes or processes in the AI model pipeline.
The guidance parameter is a floating-point value that determines the strength of the guidance applied during the conditioning process. It has a default value of 3.5, with a minimum of 0.0 and a maximum of 100.0, adjustable in increments of 0.1. This parameter allows users to control how much influence the guidance has on the conditioning, enabling fine-tuning of the model's behavior and output.
This parameter is a toggle option with two possible values: "yes" or "no," with "no" as the default. When set to "yes," the guidance embedder is bypassed, effectively disabling the guidance influence on the conditioning. This option is useful for scenarios where users want to experiment with the model's performance without any guidance or when the guidance is not needed for a particular task.
The output parameter is the modified conditioning, which incorporates the adjustments made based on the guidance parameter. This output is crucial as it represents the conditioned input that will be used in subsequent stages of the AI model pipeline. The modified conditioning reflects the influence of the guidance level set by the user, providing a tailored input for further processing.
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