"""

def __init__(self):

self.loaded_lora = None

@classmethod

def INPUT_TYPES(s):

return {

"required": {

"model": ("MODEL", {"tooltip": "The diffusion model the LoRA will be applied to."}),

"clip": ("CLIP", {"tooltip": "The CLIP model the LoRA will be applied to."}),

"lora_name": (folder_paths.get_filename_list("loras"), {"tooltip": "The name of the LoRA."}),

"strength_model": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01, "tooltip": "How strongly to modify the diffusion model. This value can be negative."}),

"strength_clip": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01, "tooltip": "How strongly to modify the CLIP model. This value can be negative."}),

}

}

RETURN_TYPES = ("MODEL", "CLIP")

OUTPUT_TOOLTIPS = ("The modified diffusion model.", "The modified CLIP model.")

FUNCTION = "load_lora"

CATEGORY = "model/loaders"

DESCRIPTION = "Apply LoRA in bypass mode. Unlike regular LoRA, this doesn't modify model weights - instead it injects the LoRA computation during forward pass. Useful for training scenarios."

EXPERIMENTAL = True

def load_lora(self, model, clip, lora_name, strength_model, strength_clip):

if strength_model == 0 and strength_clip == 0:

return (model, clip)

lora_path = folder_paths.get_full_path_or_raise("loras", lora_name)

lora = None

if self.loaded_lora is not None:

if self.loaded_lora[0] == lora_path:

lora = self.loaded_lora[1]

else:

self.loaded_lora = None

if lora is None:

lora = comfy.utils.load_torch_file(lora_path, safe_load=True)

self.loaded_lora = (lora_path, lora)

model_lora, clip_lora = comfy.sd.load_bypass_lora_for_models(model, clip, lora, strength_model, strength_clip)

@classmethod

def INPUT_TYPES(s):

return {"required": { "model": ("MODEL",),

"lora_name": (folder_paths.get_filename_list("loras"), ),

"strength_model": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01}),

}}

RETURN_TYPES = ("MODEL",)

FUNCTION = "load_lora_model_only"

def load_lora_model_only(self, model, lora_name, strength_model):