ComfyUI Node: Scaled Soft Weights 🛂🅐🅒🅝

Scaled Soft Weights 🛂🅐🅒🅝:

The ScaledSoftControlNetWeights node is designed to provide a flexible and dynamic way to control the influence of ControlNet weights in your AI art generation process. This node allows you to scale and adjust the weights based on a mask, providing a more nuanced and refined control over the generated outputs. By normalizing the mask and applying linear conversion, it ensures that the weights are appropriately scaled between specified minimum and maximum multipliers. This node is particularly useful for artists looking to fine-tune the impact of ControlNet on their creations, offering a balance between automated control and manual adjustments.

Scaled Soft Weights 🛂🅐🅒🅝 Input Parameters:

base_multiplier

This parameter sets the base multiplier for the weights. It determines the initial scaling factor applied to the weights before any other adjustments. The default value is 0.825, with a minimum of 0.0 and a maximum of 1.0. Adjusting this value will directly impact the strength of the ControlNet weights.

flip_weights

This boolean parameter, when set to True, flips the weights. This can be useful for inverting the influence of the ControlNet weights. The default value is False.

uncond_multiplier

This parameter sets the unconditional multiplier, which scales the weights in scenarios where unconditional control is applied. The default value is 1.0, with a minimum of 0.0 and a maximum of 1.0. Adjusting this value will affect the overall influence of the ControlNet weights in unconditional contexts.

cn_extras

This optional parameter allows you to pass additional ControlNet weight settings as a dictionary. These extra settings can provide further customization and fine-tuning of the ControlNet weights.

Scaled Soft Weights 🛂🅐🅒🅝 Output Parameters:

CONTROL_NET_WEIGHTS

This output parameter provides the adjusted ControlNet weights after applying the specified scaling and adjustments. These weights can be used in subsequent nodes to influence the AI art generation process.

TIMESTEP_KEYFRAME

This output parameter provides a TimestepKeyframe object that includes the control weights. This keyframe can be used to manage and apply the weights at specific timesteps during the generation process, allowing for dynamic and time-based control.

Scaled Soft Weights 🛂🅐🅒🅝 Usage Tips:

• To achieve a subtle influence of ControlNet weights, start with a lower base_multiplier and gradually increase it until you reach the desired effect.
• Use the flip_weights parameter to experiment with inverse effects, which can sometimes yield interesting and unexpected results.
• Utilize the cn_extras parameter to pass additional settings and fine-tune the ControlNet weights for more complex and customized control.

Scaled Soft Weights 🛂🅐🅒🅝 Common Errors and Solutions:

"Mask normalization failed: x_min equals x_max"

• Explanation: This error occurs when the minimum and maximum values of the mask are the same, leading to a failure in normalization.
• Solution: Ensure that the mask has a range of values. If necessary, adjust the mask to have varying values before passing it to the node.

"Invalid base_multiplier value"

• Explanation: This error occurs when the base_multiplier value is outside the allowed range (0.0 to 1.0).
• Solution: Check and adjust the base_multiplier value to be within the specified range.

"Invalid uncond_multiplier value"

• Explanation: This error occurs when the uncond_multiplier value is outside the allowed range (0.0 to 1.0).
• Solution: Check and adjust the uncond_multiplier value to be within the specified range.

"cn_extras must be a dictionary"

• Explanation: This error occurs when the cn_extras parameter is not provided as a dictionary.
• Solution: Ensure that the cn_extras parameter is passed as a dictionary with appropriate key-value pairs.