ComfyUI Node: The Golden Scheduler

The Golden Scheduler:

The Golden Scheduler is a specialized node designed to generate a sequence of sigma values for sampling processes in AI art generation. It leverages a unique method based on the golden ratio to interpolate between the minimum and maximum sigma values of a model. This approach ensures a smooth and aesthetically pleasing transition in the generated images, enhancing the overall quality and coherence of the output. The Golden Scheduler is particularly beneficial for artists looking to achieve high-quality results with minimal manual adjustments, as it automates the sigma calculation process using a mathematically sound technique.

The Golden Scheduler Input Parameters:

model

This parameter specifies the model to be used for generating sigma values. The model should be compatible with the node's requirements and provide access to its sampling object.

steps

This integer parameter determines the number of steps for which sigma values will be generated. The default value is 20, with a minimum of 0 and a maximum of 100000. Increasing the number of steps can lead to finer granularity in the sampling process, potentially improving the quality of the generated images.

sgm

This boolean parameter, with a default value of False, indicates whether an additional step should be included in the sigma calculation. When set to True, the number of steps is incremented by one, which can be useful for certain sampling techniques that require an extra step for optimal results.

The Golden Scheduler Output Parameters:

SIGMAS

This output parameter provides the generated sequence of sigma values as a tensor. These values are crucial for the sampling process, as they dictate the noise levels at each step, ultimately influencing the quality and characteristics of the generated images. The sequence includes a final value of 0 to signify the end of the sampling process.

The Golden Scheduler Usage Tips:

• To achieve smoother transitions in your generated images, experiment with different step values. Higher step counts can provide more detailed control over the sampling process.
• Utilize the sgm parameter when you need an additional step in your sampling technique, as it can enhance the final output quality in specific scenarios.

The Golden Scheduler Common Errors and Solutions:

"Model object not found"

• Explanation: This error occurs when the specified model does not provide the required sampling object.
• Solution: Ensure that the model you are using is compatible with the Golden Scheduler and has the necessary sampling object.

"Invalid step count"

• Explanation: This error arises when the steps parameter is set to a value outside the allowed range.
• Solution: Adjust the steps parameter to be within the range of 0 to 100000.

"SGM parameter type mismatch"

• Explanation: This error happens when the sgm parameter is not a boolean value.
• Solution: Ensure that the sgm parameter is set to either True or False.