ComfyUI Node: Primere Hypernetwork

Primere Hypernetwork:

The PrimereHypernetwork node is designed to enhance your AI model by integrating multiple hypernetworks, which are specialized networks that can modify the behavior of the base model. This node allows you to stack and apply up to six different hypernetworks, each with customizable weights, to achieve more nuanced and sophisticated outputs. The primary goal of this node is to provide flexibility and control over the model's performance, enabling you to fine-tune the model's behavior for specific tasks or artistic styles. By leveraging hypernetworks, you can introduce new features or modify existing ones in your model, thereby expanding its capabilities and improving the quality of the generated content.

Primere Hypernetwork Input Parameters:

model

This parameter represents the base model that you want to enhance using hypernetworks. It is the core model that will be modified by the hypernetworks you choose to apply.

model_version

This string parameter specifies the version of the model you are using. The default value is BaseModel_1024, and it is a required input. This helps the node determine compatibility and apply the appropriate hypernetworks.

safe_load

This boolean parameter determines whether to safely load the hypernetwork patches. The default value is True. When set to True, it ensures that the loading process is secure and minimizes the risk of errors or corruptions.

stack_version

This parameter allows you to specify the stack version of the model. The options are SD, SDXL, and Any, with the default being Any. This helps in selecting the appropriate hypernetworks based on the model's stack version.

use_hypernetwork_1

This boolean parameter indicates whether to use the first hypernetwork. The default value is False. When set to True, the first hypernetwork will be applied to the model.

hypernetwork_1

This parameter allows you to select the first hypernetwork from a list of available hypernetworks. It is used in conjunction with use_hypernetwork_1.

hypernetwork_1_weight

This float parameter sets the weight for the first hypernetwork. The default value is 1.0, with a range from -10.0 to 10.0 and a step of 0.01. This weight determines the influence of the hypernetwork on the model.

use_hypernetwork_2

This boolean parameter indicates whether to use the second hypernetwork. The default value is False. When set to True, the second hypernetwork will be applied to the model.

hypernetwork_2

This parameter allows you to select the second hypernetwork from a list of available hypernetworks. It is used in conjunction with use_hypernetwork_2.

hypernetwork_2_weight

This float parameter sets the weight for the second hypernetwork. The default value is 1.0, with a range from -10.0 to 10.0 and a step of 0.01. This weight determines the influence of the hypernetwork on the model.

use_hypernetwork_3

This boolean parameter indicates whether to use the third hypernetwork. The default value is False. When set to True, the third hypernetwork will be applied to the model.

hypernetwork_3

This parameter allows you to select the third hypernetwork from a list of available hypernetworks. It is used in conjunction with use_hypernetwork_3.

hypernetwork_3_weight

This float parameter sets the weight for the third hypernetwork. The default value is 1.0, with a range from -10.0 to 10.0 and a step of 0.01. This weight determines the influence of the hypernetwork on the model.

use_hypernetwork_4

This boolean parameter indicates whether to use the fourth hypernetwork. The default value is False. When set to True, the fourth hypernetwork will be applied to the model.

hypernetwork_4

This parameter allows you to select the fourth hypernetwork from a list of available hypernetworks. It is used in conjunction with use_hypernetwork_4.

hypernetwork_4_weight

This float parameter sets the weight for the fourth hypernetwork. The default value is 1.0, with a range from -10.0 to 10.0 and a step of 0.01. This weight determines the influence of the hypernetwork on the model.

use_hypernetwork_5

This boolean parameter indicates whether to use the fifth hypernetwork. The default value is False. When set to True, the fifth hypernetwork will be applied to the model.

hypernetwork_5

This parameter allows you to select the fifth hypernetwork from a list of available hypernetworks. It is used in conjunction with use_hypernetwork_5.

hypernetwork_5_weight

This float parameter sets the weight for the fifth hypernetwork. The default value is 1.0, with a range from -10.0 to 10.0 and a step of 0.01. This weight determines the influence of the hypernetwork on the model.

use_hypernetwork_6

This boolean parameter indicates whether to use the sixth hypernetwork. The default value is False. When set to True, the sixth hypernetwork will be applied to the model.

hypernetwork_6

This parameter allows you to select the sixth hypernetwork from a list of available hypernetworks. It is used in conjunction with use_hypernetwork_6.

Primere Hypernetwork Output Parameters:

MODEL

This output parameter returns the modified model after applying the selected hypernetworks. It represents the enhanced version of the base model with the integrated hypernetwork patches.

HYPERNETWORK_STACK

This output parameter provides a list of the hypernetworks that were applied to the model, along with their respective weights. It helps in understanding the modifications made to the base model and the influence of each hypernetwork.

Primere Hypernetwork Usage Tips:

• To achieve the best results, carefully select and adjust the weights of the hypernetworks based on the specific task or artistic style you are aiming for.
• Use the safe_load parameter to ensure that the hypernetwork patches are loaded securely, minimizing the risk of errors or corruptions.
• Experiment with different combinations of hypernetworks and their weights to find the optimal configuration for your model.

Primere Hypernetwork Common Errors and Solutions:

"Incompatible model version and stack version"

• Explanation: This error occurs when the specified model_version and stack_version are not compatible with each other.
• Solution: Ensure that the model_version and stack_version are correctly matched. For example, do not use SDXL_2048 with SD stack version.

"Failed to load hypernetwork patch"

• Explanation: This error occurs when the node is unable to load the hypernetwork patch from the specified path.
• Solution: Verify that the hypernetwork path is correct and that the file exists. Also, ensure that the safe_load parameter is set appropriately.

"Hypernetwork weight out of range"

• Explanation: This error occurs when the specified weight for a hypernetwork is outside the allowed range.
• Solution: Adjust the hypernetwork weight to be within the range of -10.0 to 10.0.

"No hypernetworks selected"

• Explanation: This error occurs when no hypernetworks are selected for application.
• Solution: Ensure that at least one hypernetwork is selected by setting the corresponding use_hypernetwork_X parameter to True.