File size: 3,224 Bytes
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import comfy.utils
import comfy.sd
class LoraLoaderBypass:
"""
Apply LoRA in bypass mode without modifying base model weights.
Bypass mode computes: output = base_forward(x) + lora_path(x)
This is useful for training and when model weights are offloaded.
"""
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 = "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)
return (model_lora, clip_lora)
class LoraLoaderBypassModelOnly(LoraLoaderBypass):
@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):
return (self.load_lora(model, None, lora_name, strength_model, 0)[0],)
NODE_CLASS_MAPPINGS = {
"LoraLoaderBypass": LoraLoaderBypass,
"LoraLoaderBypassModelOnly": LoraLoaderBypassModelOnly,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"LoraLoaderBypass": "Load LoRA (Bypass) (For debugging)",
"LoraLoaderBypassModelOnly": "Load LoRA (Bypass, Model Only) (for debugging)",
}
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