ComfyUI Node: Merge sigmas by average

Merge sigmas by average:

The "Merge sigmas by average" node is designed to combine multiple sigma tensors by calculating their average. This node is particularly useful in scenarios where you need to blend different sigma values to achieve a more balanced or smoothed result. By averaging the sigmas, you can create a composite sigma that incorporates the characteristics of all input sigmas, leading to more nuanced and refined outputs. This method is beneficial for tasks that require the integration of multiple sigma sources, ensuring that the final output is a harmonious blend of all inputs.

Merge sigmas by average Input Parameters:

sigmas_1

This is the primary sigma tensor that you want to merge with others. It is a required input and serves as the base for the averaging process. The sigma tensor represents a set of values that are crucial for the sampling process in AI models.

sigmas_2 to sigmas_25

These are optional sigma tensors that can be provided to the node for averaging. Each of these tensors will be included in the averaging process if they are provided. The more sigma tensors you include, the more comprehensive the averaging will be, leading to a more balanced final sigma tensor.

Merge sigmas by average Output Parameters:

SIGMAS

The output is a single sigma tensor that represents the average of all the input sigma tensors. This averaged sigma tensor can be used in subsequent processes where a balanced and integrated sigma value is required. The averaging process ensures that the final sigma tensor incorporates the characteristics of all input sigmas, leading to a more refined and nuanced result.

Merge sigmas by average Usage Tips:

• To achieve a more balanced sigma tensor, provide multiple sigma inputs. The more inputs you provide, the more comprehensive the averaging will be.
• Use this node when you need to blend different sigma values to create a more nuanced and refined output.

Merge sigmas by average Common Errors and Solutions:

TypeError: expected Tensor as element 0 in argument 0, but got NoneType

• Explanation: This error occurs when one of the required sigma inputs is not provided or is None.
• Solution: Ensure that all required sigma inputs are provided and are valid tensors.

RuntimeError: stack expects each tensor to be equal size, but got [size1] at entry 0 and [size2] at entry 1

• Explanation: This error occurs when the sigma tensors provided have different sizes.
• Solution: Ensure that all sigma tensors provided have the same size before passing them to the node.