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from pathlib import Path
import folder_paths
import comfy.utils
import comfy.sd
from .logger import logger
from .utils_model import get_available_motion_loras, get_motion_lora_path
from .motion_lora import MotionLoraInfo, MotionLoraList
class AnimateDiffLoraLoader:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"lora_name": (get_available_motion_loras(),),
"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}),
},
"optional": {
"prev_motion_lora": ("MOTION_LORA",),
}
}
RETURN_TYPES = ("MOTION_LORA",)
CATEGORY = "Animate Diff 🎭🅐🅓"
FUNCTION = "load_motion_lora"
def load_motion_lora(self, lora_name: str, strength: float, prev_motion_lora: MotionLoraList=None):
if prev_motion_lora is None:
prev_motion_lora = MotionLoraList()
else:
prev_motion_lora = prev_motion_lora.clone()
# check if motion lora with name exists
lora_path = get_motion_lora_path(lora_name)
if not Path(lora_path).is_file():
raise FileNotFoundError(f"Motion lora with name '{lora_name}' not found.")
# create motion lora info to be loaded in AnimateDiff Loader
lora_info = MotionLoraInfo(name=lora_name, strength=strength)
prev_motion_lora.add_lora(lora_info)
return (prev_motion_lora,)
class MaskedLoraLoader:
def __init__(self):
self.loaded_lora = None
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"clip": ("CLIP", ),
"lora_name": (folder_paths.get_filename_list("loras"), ),
"strength_model": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}),
"strength_clip": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}),
}}
#RETURN_TYPES = ()
RETURN_TYPES = ("MODEL", "CLIP")
FUNCTION = "load_lora"
CATEGORY = "loaders"
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("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 = comfy.utils.load_torch_file(lora_path, safe_load=True)
self.loaded_lora = (lora_path, lora)
from pathlib import Path
with open(Path(__file__).parent.parent.parent / "sd_lora_keys.txt", "w") as lfile:
for key in lora:
lfile.write(f"{key}:\t{lora[key].size()}\n")
#model_lora, clip_lora = comfy.sd.load_lora_for_models(model, clip, lora, strength_model, strength_clip)
#return (model_lora, clip_lora)
return (model, clip)
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