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
- Comfy dtype:
Usage tips¶
- Infra type:
GPU
- Common nodes:
- IPAdapterApplyFaceID
- LoraLoaderModelOnly
- KSampler
- FreeU_V2
- UltimateSDUpscale
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],)