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LoraLoaderModelOnly

Documentation

  • Class name: LoraLoaderModelOnly
  • Category: loaders
  • Output node: False

This node specializes in loading a LoRA model without requiring a CLIP model, focusing on enhancing or modifying a given model based on LoRA parameters. It allows for the dynamic adjustment of the model's strength through LoRA parameters, facilitating fine-tuned control over the model's behavior.

Input types

Required

  • model
    • The model to which LoRA adjustments will be applied. It serves as the base for modifications.
    • Comfy dtype: MODEL
    • Python dtype: torch.nn.Module
  • lora_name
    • The name of the LoRA file to be loaded. This specifies which LoRA adjustments to apply to the model.
    • Comfy dtype: COMBO[STRING]
    • Python dtype: str
  • strength_model
    • Determines the intensity of the LoRA adjustments applied to the model. A higher value indicates stronger modifications.
    • Comfy dtype: FLOAT
    • Python dtype: float

Output types

  • model
    • Comfy dtype: MODEL
    • The modified model with LoRA adjustments applied, reflecting changes in model behavior or capabilities.
    • Python dtype: torch.nn.Module

Usage tips

Source code

class LoraLoaderModelOnly(LoraLoader):
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "model": ("MODEL",),
                              "lora_name": (folder_paths.get_filename_list("loras"), ),
                              "strength_model": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}),
                              }}
    RETURN_TYPES = ("MODEL",)
    FUNCTION = "load_lora_model_only"

    def load_lora_model_only(self, model, lora_name, strength_model):
        return (self.load_lora(model, None, lora_name, strength_model, 0)[0],)