ComfyUI Node: load available lora (2lab)

LoraLoader (2lab):

The LoraLoader (2lab) node is designed to facilitate the loading and application of LoRA (Low-Rank Adaptation) models to existing AI models and CLIP (Contrastive Language-Image Pretraining) models. This node allows you to enhance and fine-tune your models by integrating pre-trained LoRA models, which can significantly improve the performance and adaptability of your AI models for specific tasks. By adjusting the strength parameters, you can control the influence of the LoRA model on both the AI model and the CLIP model, providing a flexible and powerful tool for AI artists to customize their models according to their creative needs.

LoraLoader (2lab) Input Parameters:

model

This parameter represents the base AI model to which the LoRA model will be applied. It is essential for the node to function as it provides the primary structure that will be enhanced by the LoRA model.

clip

This parameter represents the CLIP model, which is used for contrastive language-image pretraining. The LoRA model can also be applied to this model to enhance its performance. This parameter is optional and can be set to None if not needed.

lora_name

This parameter specifies the name of the LoRA model to be loaded. It must be selected from the list of available LoRA models in the designated folder. The correct LoRA model name ensures that the appropriate enhancements are applied to the base models.

strength_model

This parameter controls the strength of the LoRA model's influence on the base AI model. It is a floating-point value with a default of 1.0, a minimum of -100.0, and a maximum of 100.0. Adjusting this value allows you to fine-tune the degree to which the LoRA model affects the base model, providing flexibility in the enhancement process.

strength_clip

This parameter controls the strength of the LoRA model's influence on the CLIP model. Similar to strength_model, it is a floating-point value with a default of 1.0, a minimum of -100.0, and a maximum of 100.0. This parameter allows you to fine-tune the degree to which the LoRA model affects the CLIP model, offering additional customization options.

LoraLoader (2lab) Output Parameters:

MODEL

This output represents the enhanced AI model after the LoRA model has been applied. The modifications made by the LoRA model are reflected in this output, providing an improved version of the original model.

CLIP

This output represents the enhanced CLIP model after the LoRA model has been applied. Similar to the MODEL output, the modifications made by the LoRA model are reflected in this output, providing an improved version of the original CLIP model.

LoraLoader (2lab) Usage Tips:

• Ensure that the lora_name parameter is correctly set to a valid LoRA model name available in your designated folder to avoid errors.
• Experiment with different values for strength_model and strength_clip to find the optimal balance for your specific task. Start with the default value of 1.0 and adjust incrementally.
• If you do not need to enhance the CLIP model, you can set the clip parameter to None and strength_clip to 0 to focus solely on the AI model.

LoraLoader (2lab) Common Errors and Solutions:

lora 'simple_lora_name' not in available list, please check lora.json

• Explanation: This error occurs when the specified lora_name is not found in the list of available LoRA models.
• Solution: Verify that the lora_name parameter is correctly set to a valid LoRA model name. Check the lora.json file to ensure the model is listed and available.

ValueError: lora 'simple_lora_name' not in available list, please check lora.json

• Explanation: This error indicates that the specified LoRA model name does not match any of the available models in the designated folder.
• Solution: Double-check the spelling and format of the lora_name parameter. Ensure that the LoRA model is correctly placed in the designated folder and listed in the lora.json file.