ComfyUI Node: FreSca

FreSca:

FreSca is a node designed to enhance the capabilities of AI models by applying a Fourier-based filtering technique to the guidance signals used in conditional generation tasks. This node leverages the power of Fourier transforms to selectively filter out certain frequency components from the guidance signal, which can help in refining the output of AI models by focusing on specific features or patterns. The primary goal of FreSca is to improve the quality and relevance of generated content by manipulating the frequency spectrum of the guidance, thus allowing for more controlled and precise outputs. This approach is particularly beneficial in scenarios where the model's output needs to be fine-tuned to emphasize or de-emphasize certain aspects, making it a valuable tool for AI artists looking to achieve specific artistic effects or styles.

FreSca Input Parameters:

model

The model parameter represents the AI model that will be cloned and modified by the FreSca node. This parameter is crucial as it determines the base model on which the Fourier-based filtering will be applied. The model should be compatible with the FreSca node's operations, and it is typically a pre-trained model used for generating content. There are no specific minimum or maximum values for this parameter, but it should be a valid model object that supports the necessary operations.

scale_low

The scale_low parameter defines the lower bound of the frequency scale that will be retained during the Fourier filtering process. This parameter is important for controlling which low-frequency components are preserved in the guidance signal. By adjusting this value, you can influence the level of detail and smoothness in the generated output. The exact range of values for scale_low is not specified, but it should be chosen based on the desired level of detail in the output.

scale_high

The scale_high parameter sets the upper bound of the frequency scale that will be retained during the Fourier filtering process. This parameter is crucial for determining which high-frequency components are preserved in the guidance signal. By tuning this value, you can control the sharpness and fine details in the generated content. Similar to scale_low, the range of values for scale_high is not explicitly defined, but it should be selected based on the desired sharpness and detail in the output.

freq_cutoff

The freq_cutoff parameter specifies the cutoff frequency for the Fourier filtering process. This parameter is essential for determining the threshold at which frequency components are filtered out. By setting an appropriate freq_cutoff, you can control the balance between preserving important features and removing noise or unwanted details. The specific range of values for freq_cutoff is not provided, but it should be chosen based on the desired balance between detail and noise reduction in the output.

FreSca Output Parameters:

filtered_cond

The filtered_cond output parameter represents the condition signal after it has been processed by the Fourier-based filtering technique. This output is crucial as it reflects the modified guidance signal that will be used by the AI model to generate content. The filtered_cond is a combination of the filtered guidance and the original unconditional signal, providing a refined input for the model to produce more controlled and precise outputs.

uncond

The uncond output parameter is the original unconditional signal that was part of the input to the FreSca node. This output is important as it serves as a reference point for the modifications made by the Fourier filtering process. The uncond signal remains unchanged and is used in conjunction with the filtered_cond to guide the model's output generation.

FreSca Usage Tips:

• Experiment with different scale_low and scale_high values to achieve the desired level of detail and sharpness in your generated content. Lower values may result in smoother outputs, while higher values can enhance fine details.
• Adjust the freq_cutoff parameter to find the right balance between preserving important features and reducing noise. This can help in achieving cleaner and more focused outputs.
• Use the FreSca node in combination with other nodes to explore creative effects and styles, leveraging its ability to manipulate the frequency spectrum of the guidance signal.

FreSca Common Errors and Solutions:

Model not compatible

• Explanation: The model provided to the FreSca node is not compatible with the operations required for Fourier-based filtering.
• Solution: Ensure that the model is a valid object that supports the necessary operations and is compatible with the FreSca node's requirements.

Invalid frequency parameters

• Explanation: The scale_low, scale_high, or freq_cutoff parameters are set to invalid values that do not align with the expected range or type.
• Solution: Verify that the frequency parameters are set to appropriate values based on the desired output characteristics and ensure they are within a reasonable range for the specific task.