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Register model as LoRA hook for AI artists to enhance behavior with specific configurations, enabling precise adjustments.
The ADE_RegisterLoraHookModelOnly
node is designed to register a model as a LoRA (Low-Rank Adaptation) hook, specifically focusing on the model without involving the CLIP component. This node is particularly useful for AI artists who want to apply LoRA techniques to their models to enhance or modify their behavior in a controlled manner. By leveraging this node, you can load a model with specific LoRA configurations, allowing for fine-tuned adjustments to the model's performance and output. This can be especially beneficial in scenarios where you need to apply targeted modifications to your model's behavior without affecting other components like CLIP. The primary goal of this node is to provide a streamlined and efficient way to integrate LoRA hooks into your models, enabling more precise and customizable model adjustments.
This parameter represents the model you want to register as a LoRA hook. It is essential for defining the base model that will be modified using the LoRA technique. The model should be compatible with the LoRA hook mechanism to ensure proper functionality.
This parameter specifies the name of the checkpoint file that contains the LoRA configurations. It allows you to select the appropriate LoRA settings to be applied to your model. The available options are derived from the list of checkpoint files in your system. Choosing the correct checkpoint file is crucial for achieving the desired modifications to your model.
This parameter controls the strength of the LoRA modifications applied to the model. It is a floating-point value with a default of 1.0, a minimum of -20.0, and a maximum of 20.0, with a step size of 0.01. Adjusting this parameter allows you to fine-tune the intensity of the LoRA effects on your model, enabling you to achieve the desired level of modification.
This output represents the modified model after applying the LoRA hook. It reflects the changes made to the original model based on the specified LoRA configurations and strength settings. This output can be used in subsequent nodes or processes that require the modified model.
This output provides the LoRA hook that was applied to the model. It encapsulates the LoRA configurations and settings used to modify the model. This output can be useful for tracking and managing the applied LoRA modifications, as well as for reusing the same LoRA hook in other contexts or models.
strength_model
values to find the optimal level of modification for your specific use case.ckpt_name
parameter to select the appropriate checkpoint file that contains the desired LoRA configurations for your model.ckpt_name
parameter is correctly set to an existing checkpoint file and that the file is located in the appropriate directory.strength_model
parameter is set to a value outside the allowed range.strength_model
parameter to a value within the range of -20.0 to 20.0 and try again.© Copyright 2024 RunComfy. All Rights Reserved.