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from comfy.sd import load_lora_for_models
from comfy.utils import load_torch_file
import folder_paths
from .utils import *
class LoraLoaderVanilla:
def __init__(self):
self.loaded_lora = None
@classmethod
def INPUT_TYPES(s):
LORA_LIST = sorted(folder_paths.get_filename_list("loras"), key=str.lower)
return {
"required": {
"model": ("MODEL",),
"clip": ("CLIP", ),
"lora_name": (LORA_LIST, ),
"strength_model": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 2.0, "step": 0.1}),
"strength_clip": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 2.0, "step": 0.1}),
"force_fetch": ("BOOLEAN", {"default": False}),
"append_loraname_if_empty": ("BOOLEAN", {"default": False}),
}
}
RETURN_TYPES = ("MODEL", "CLIP", "LIST", "LIST")
RETURN_NAMES = ("MODEL", "CLIP", "civitai_tags_list", "meta_tags_list")
FUNCTION = "load_lora"
CATEGORY = "autotrigger"
def load_lora(self, model, clip, lora_name, strength_model, strength_clip, force_fetch, append_loraname_if_empty):
meta_tags_list = sort_tags_by_frequency(get_metadata(lora_name, "loras"))
civitai_tags_list = load_and_save_tags(lora_name, force_fetch)
meta_tags_list = append_lora_name_if_empty(meta_tags_list, lora_name, append_loraname_if_empty)
civitai_tags_list = append_lora_name_if_empty(civitai_tags_list, lora_name, append_loraname_if_empty)
lora_path = folder_paths.get_full_path("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:
temp = self.loaded_lora
self.loaded_lora = None
del temp
if lora is None:
lora = load_torch_file(lora_path, safe_load=True)
self.loaded_lora = (lora_path, lora)
model_lora, clip_lora = load_lora_for_models(model, clip, lora, strength_model, strength_clip)
return (model_lora, clip_lora, civitai_tags_list, meta_tags_list)
class LoraLoaderStackedVanilla:
@classmethod
def INPUT_TYPES(s):
LORA_LIST = folder_paths.get_filename_list("loras")
return {
"required": {
"lora_name": (LORA_LIST,),
"lora_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"force_fetch": ("BOOLEAN", {"default": False}),
"append_loraname_if_empty": ("BOOLEAN", {"default": False}),
},
"optional": {
"lora_stack": ("LORA_STACK", ),
}
}
RETURN_TYPES = ("LIST", "LIST", "LORA_STACK",)
RETURN_NAMES = ("civitai_tags_list", "meta_tags_list", "LORA_STACK",)
FUNCTION = "set_stack"
#OUTPUT_NODE = False
CATEGORY = "autotrigger"
def set_stack(self, lora_name, lora_weight, force_fetch, append_loraname_if_empty, lora_stack=None):
civitai_tags_list = load_and_save_tags(lora_name, force_fetch)
meta_tags = get_metadata(lora_name, "loras")
meta_tags_list = sort_tags_by_frequency(meta_tags)
civitai_tags_list = append_lora_name_if_empty(civitai_tags_list, lora_name, append_loraname_if_empty)
meta_tags_list = append_lora_name_if_empty(meta_tags_list, lora_name, append_loraname_if_empty)
loras = [(lora_name,lora_weight,lora_weight,)]
if lora_stack is not None:
loras.extend(lora_stack)
return (civitai_tags_list, meta_tags_list, loras)
class LoraLoaderAdvanced:
def __init__(self):
self.loaded_lora = None
@classmethod
def INPUT_TYPES(s):
LORA_LIST = sorted(folder_paths.get_filename_list("loras"), key=str.lower)
populate_items(LORA_LIST, "loras")
return {
"required": {
"model": ("MODEL",),
"clip": ("CLIP", ),
"lora_name": (LORA_LIST, ),
"strength_model": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 2.0, "step": 0.1}),
"strength_clip": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 2.0, "step": 0.1}),
"force_fetch": ("BOOLEAN", {"default": False}),
"enable_preview": ("BOOLEAN", {"default": False}),
"append_loraname_if_empty": ("BOOLEAN", {"default": False}),
}
}
RETURN_TYPES = ("MODEL", "CLIP", "LIST", "LIST")
RETURN_NAMES = ("MODEL", "CLIP", "civitai_tags_list", "meta_tags_list")
FUNCTION = "load_lora"
CATEGORY = "autotrigger"
def load_lora(self, model, clip, lora_name, strength_model, strength_clip, force_fetch, enable_preview, append_loraname_if_empty):
meta_tags_list = sort_tags_by_frequency(get_metadata(lora_name["content"], "loras"))
civitai_tags_list = load_and_save_tags(lora_name["content"], force_fetch)
civitai_tags_list = append_lora_name_if_empty(civitai_tags_list, lora_name["content"], append_loraname_if_empty)
meta_tags_list = append_lora_name_if_empty(meta_tags_list, lora_name["content"], append_loraname_if_empty)
lora_path = folder_paths.get_full_path("loras", lora_name["content"])
lora = None
if self.loaded_lora is not None:
if self.loaded_lora[0] == lora_path:
lora = self.loaded_lora[1]
else:
temp = self.loaded_lora
self.loaded_lora = None
del temp
if lora is None:
lora = load_torch_file(lora_path, safe_load=True)
self.loaded_lora = (lora_path, lora)
model_lora, clip_lora = load_lora_for_models(model, clip, lora, strength_model, strength_clip)
if enable_preview:
_, preview = copy_preview_to_temp(lora_name["image"])
if preview is not None:
preview_output = {
"filename": preview,
"subfolder": "lora_preview",
"type": "temp"
}
return {"ui": {"images": [preview_output]}, "result": (model_lora, clip_lora, civitai_tags_list, meta_tags_list)}
return (model_lora, clip_lora, civitai_tags_list, meta_tags_list)
class LoraLoaderStackedAdvanced:
@classmethod
def INPUT_TYPES(s):
LORA_LIST = folder_paths.get_filename_list("loras")
populate_items(LORA_LIST, "loras")
return {
"required": {
"lora_name": (LORA_LIST,),
"lora_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"force_fetch": ("BOOLEAN", {"default": False}),
"enable_preview": ("BOOLEAN", {"default": False}),
"append_loraname_if_empty": ("BOOLEAN", {"default": False}),
},
"optional": {
"lora_stack": ("LORA_STACK", ),
}
}
RETURN_TYPES = ("LIST", "LIST", "LORA_STACK",)
RETURN_NAMES = ("civitai_tags_list", "meta_tags_list", "LORA_STACK",)
FUNCTION = "set_stack"
#OUTPUT_NODE = False
CATEGORY = "autotrigger"
def set_stack(self, lora_name, lora_weight, force_fetch, enable_preview, append_loraname_if_empty, lora_stack=None):
civitai_tags_list = load_and_save_tags(lora_name["content"], force_fetch)
meta_tags = get_metadata(lora_name["content"], "loras")
meta_tags_list = sort_tags_by_frequency(meta_tags)
civitai_tags_list = append_lora_name_if_empty(civitai_tags_list, lora_name["content"], append_loraname_if_empty)
meta_tags_list = append_lora_name_if_empty(meta_tags_list, lora_name["content"], append_loraname_if_empty)
loras = [(lora_name["content"],lora_weight,lora_weight,)]
if lora_stack is not None:
loras.extend(lora_stack)
if enable_preview:
_, preview = copy_preview_to_temp(lora_name["image"])
if preview is not None:
preview_output = {
"filename": preview,
"subfolder": "lora_preview",
"type": "temp"
}
return {"ui": {"images": [preview_output]}, "result": (civitai_tags_list, meta_tags_list, loras)}
return {"result": (civitai_tags_list, meta_tags_list, loras)}
# A dictionary that contains all nodes you want to export with their names
# NOTE: names should be globally unique
NODE_CLASS_MAPPINGS = {
"LoraLoaderVanilla": LoraLoaderVanilla,
"LoraLoaderStackedVanilla": LoraLoaderStackedVanilla,
"LoraLoaderAdvanced": LoraLoaderAdvanced,
"LoraLoaderStackedAdvanced": LoraLoaderStackedAdvanced,
}
# A dictionary that contains the friendly/humanly readable titles for the nodes
NODE_DISPLAY_NAME_MAPPINGS = {
"LoraLoaderVanilla": "LoraLoaderVanilla",
"LoraLoaderStackedVanilla": "LoraLoaderStackedVanilla",
"LoraLoaderAdvanced": "LoraLoaderAdvanced",
"LoraLoaderStackedAdvanced": "LoraLoaderStackedAdvanced",
}
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