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Enhances conditioning by setting defaults and combining pairs for consistent results.
The PairConditioningSetDefaultCombine
node is designed to enhance the conditioning process by setting default values and combining them with existing conditioning pairs. This node is particularly useful in scenarios where you want to ensure that certain default conditioning values are always applied, while still allowing for the flexibility of combining them with other conditioning inputs. By leveraging this node, you can streamline the conditioning process, ensuring consistency and reliability in the results. The primary function of this node is to take two sets of conditioning inputs, apply default values to them, and then combine these into a final output. This approach is beneficial for maintaining a baseline of conditioning while still allowing for dynamic adjustments based on additional inputs.
The positive
parameter represents the initial positive conditioning input that you want to process. This input is crucial as it forms the basis of the positive conditioning that will be combined with the default values. The impact of this parameter is significant as it directly influences the final positive conditioning output. There are no specific minimum, maximum, or default values for this parameter, as it depends on the conditioning data you provide.
The negative
parameter is the initial negative conditioning input that you want to process. Similar to the positive
parameter, this input is essential as it forms the basis of the negative conditioning that will be combined with the default values. The influence of this parameter is substantial as it directly affects the final negative conditioning output. Like the positive
parameter, there are no specific minimum, maximum, or default values for this parameter, as it is determined by the conditioning data you provide.
The positive_DEFAULT
parameter provides the default positive conditioning values that will be combined with the initial positive conditioning input. This parameter ensures that a baseline of positive conditioning is always applied, regardless of the initial input. The presence of this parameter helps maintain consistency in the positive conditioning output. There are no specific minimum, maximum, or default values for this parameter, as it is based on the default conditioning data you provide.
The negative_DEFAULT
parameter offers the default negative conditioning values that will be combined with the initial negative conditioning input. This parameter is crucial for ensuring that a baseline of negative conditioning is consistently applied. The inclusion of this parameter aids in maintaining uniformity in the negative conditioning output. Similar to the other parameters, there are no specific minimum, maximum, or default values for this parameter, as it relies on the default conditioning data you provide.
The hooks
parameter is optional and allows you to specify a group of hooks that can be applied during the conditioning process. Hooks are useful for adding additional processing or modifications to the conditioning inputs. This parameter provides flexibility and customization in the conditioning process, enabling you to tailor the results to specific needs. There are no specific minimum, maximum, or default values for this parameter, as it depends on the hooks you choose to apply.
The positive
output parameter represents the final positive conditioning result after combining the initial positive input with the default positive values. This output is crucial as it reflects the combined effect of both the initial and default positive conditioning, providing a comprehensive positive conditioning result.
The negative
output parameter signifies the final negative conditioning result after merging the initial negative input with the default negative values. This output is important as it showcases the combined impact of both the initial and default negative conditioning, delivering a complete negative conditioning result.
positive_DEFAULT
and negative_DEFAULT
) are well-defined to maintain consistency in the output, especially when the initial inputs may vary significantly.hooks
parameter to introduce additional processing or modifications to the conditioning inputs, allowing for greater flexibility and customization in the results.positive
, negative
, positive_DEFAULT
, negative_DEFAULT
) are not provided.hooks
parameter is incorrectly configured or contains invalid hooks.hooks
parameter is correctly set up and that all specified hooks are valid and compatible with the conditioning process.RunComfy is the premier ComfyUI platform, offering ComfyUI online environment and services, along with ComfyUI workflows featuring stunning visuals. RunComfy also provides AI Playground, enabling artists to harness the latest AI tools to create incredible art.