ComfyUI Node: MultipleLoraLoader5 🍌

MultipleLoraLoader5 🍌| MultipleLoraLoader5 🍌:

The MultipleLoraLoader5| MultipleLoraLoader5 🍌 node is designed to facilitate the loading and application of multiple LoRA (Low-Rank Adaptation) models to a primary model and an optional CLIP model. This node allows you to specify up to five different LoRA models, each with its own strength and application settings, providing a flexible and powerful way to enhance your AI-generated art. By enabling the normalization option, you can ensure that the combined strength of the applied LoRA models is balanced, which helps in maintaining the overall quality and consistency of the output. This node is particularly useful for AI artists who want to experiment with various LoRA models to achieve unique and refined results in their artwork.

MultipleLoraLoader5 🍌| MultipleLoraLoader5 🍌 Input Parameters:

model

This parameter specifies the primary model to which the LoRA models will be applied. It is a required input and serves as the base model for the modifications.

normalize

This boolean parameter determines whether the strengths of the applied LoRA models should be normalized. If set to true, the combined strength of the LoRA models will be scaled to match the value specified in normalize_sum. The default value is false.

normalize_sum

This float parameter sets the target sum for the normalized strengths of the LoRA models. It is only relevant if normalize is set to true. The default value is 1.0, with a minimum of -50.0 and a maximum of 50.0, adjustable in steps of 0.01.

lora_name_0

This parameter specifies the name of the first LoRA model to be applied. It can be set to "None" or any available LoRA model name from the list.

strength_model_0

This float parameter sets the strength of the first LoRA model when applied to the primary model. The default value is 1.0, with a minimum of -20.0 and a maximum of 20.0, adjustable in steps of 0.01.

apply_0

This boolean parameter determines whether the first LoRA model should be applied. The default value is true.

lora_name_1

This parameter specifies the name of the second LoRA model to be applied. It can be set to "None" or any available LoRA model name from the list.

strength_model_1

This float parameter sets the strength of the second LoRA model when applied to the primary model. The default value is 1.0, with a minimum of -20.0 and a maximum of 20.0, adjustable in steps of 0.01.

apply_1

This boolean parameter determines whether the second LoRA model should be applied. The default value is true.

lora_name_2

This parameter specifies the name of the third LoRA model to be applied. It can be set to "None" or any available LoRA model name from the list.

strength_model_2

This float parameter sets the strength of the third LoRA model when applied to the primary model. The default value is 1.0, with a minimum of -20.0 and a maximum of 20.0, adjustable in steps of 0.01.

apply_2

This boolean parameter determines whether the third LoRA model should be applied. The default value is true.

lora_name_3

This parameter specifies the name of the fourth LoRA model to be applied. It can be set to "None" or any available LoRA model name from the list.

strength_model_3

This float parameter sets the strength of the fourth LoRA model when applied to the primary model. The default value is 1.0, with a minimum of -20.0 and a maximum of 20.0, adjustable in steps of 0.01.

apply_3

This boolean parameter determines whether the fourth LoRA model should be applied. The default value is true.

lora_name_4

This parameter specifies the name of the fifth LoRA model to be applied. It can be set to "None" or any available LoRA model name from the list.

strength_model_4

This float parameter sets the strength of the fifth LoRA model when applied to the primary model. The default value is 1.0, with a minimum of -20.0 and a maximum of 20.0, adjustable in steps of 0.01.

apply_4

This boolean parameter determines whether the fifth LoRA model should be applied. The default value is true.

clip_optional

This optional parameter specifies the CLIP model to which the LoRA models will also be applied. If not provided, only the primary model will be modified.

MultipleLoraLoader5 🍌| MultipleLoraLoader5 🍌 Output Parameters:

model

The modified primary model with the applied LoRA models. This output reflects the cumulative effect of all the specified LoRA models and their respective strengths.

clip

The modified CLIP model with the applied LoRA models, if the clip_optional parameter was provided. This output reflects the cumulative effect of all the specified LoRA models and their respective strengths on the CLIP model.

MultipleLoraLoader5 🍌| MultipleLoraLoader5 🍌 Usage Tips:

• To achieve a balanced application of multiple LoRA models, enable the normalize parameter and set an appropriate value for normalize_sum.
• Experiment with different strengths for each LoRA model to fine-tune the impact on your primary model and achieve the desired artistic effect.
• If you do not want to apply a specific LoRA model, set its apply parameter to false or its lora_name to "None".

MultipleLoraLoader5 🍌| MultipleLoraLoader5 🍌 Common Errors and Solutions:

"LoRA model not found"

• Explanation: This error occurs when the specified LoRA model name does not exist in the available list.
• Solution: Ensure that the lora_name parameters are set to valid LoRA model names from the list provided.

"Strength sum is zero"

• Explanation: This error occurs when the combined strength of the applied LoRA models is zero, which can happen if all strength_model parameters are set to zero or all apply parameters are set to false.
• Solution: Adjust the strength_model parameters to non-zero values and ensure that at least one apply parameter is set to true.

"Normalization scale is invalid"

• Explanation: This error occurs when the normalization scale cannot be computed due to a zero or negative normalize_sum.
• Solution: Set the normalize_sum parameter to a positive value greater than zero.