mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-03-22 13:42:12 -03:00
82 lines
3.4 KiB
Python
82 lines
3.4 KiB
Python
import logging
|
|
from nodes import LoraLoader
|
|
from comfy.comfy_types import IO # type: ignore
|
|
from ..services.lora_scanner import LoraScanner
|
|
from ..config import config
|
|
import asyncio
|
|
import os
|
|
from .utils import FlexibleOptionalInputType, any_type, get_lora_info, extract_lora_name, get_loras_list
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
class LoraManagerLoader:
|
|
NAME = "Lora Loader (LoraManager)"
|
|
CATEGORY = "Lora Manager/loaders"
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"model": ("MODEL",),
|
|
# "clip": ("CLIP",),
|
|
"text": (IO.STRING, {
|
|
"multiline": True,
|
|
"dynamicPrompts": True,
|
|
"tooltip": "Format: <lora:lora_name:strength> separated by spaces or punctuation",
|
|
"placeholder": "LoRA syntax input: <lora:name:strength>"
|
|
}),
|
|
},
|
|
"optional": FlexibleOptionalInputType(any_type),
|
|
}
|
|
|
|
RETURN_TYPES = ("MODEL", "CLIP", IO.STRING, IO.STRING)
|
|
RETURN_NAMES = ("MODEL", "CLIP", "trigger_words", "loaded_loras")
|
|
FUNCTION = "load_loras"
|
|
|
|
def load_loras(self, model, text, **kwargs):
|
|
"""Loads multiple LoRAs based on the kwargs input and lora_stack."""
|
|
loaded_loras = []
|
|
all_trigger_words = []
|
|
|
|
clip = kwargs.get('clip', None)
|
|
lora_stack = kwargs.get('lora_stack', None)
|
|
# First process lora_stack if available
|
|
if lora_stack:
|
|
for lora_path, model_strength, clip_strength in lora_stack:
|
|
# Apply the LoRA using the provided path and strengths
|
|
model, clip = LoraLoader().load_lora(model, clip, lora_path, model_strength, clip_strength)
|
|
|
|
# Extract lora name for trigger words lookup
|
|
lora_name = extract_lora_name(lora_path)
|
|
_, trigger_words = asyncio.run(get_lora_info(lora_name))
|
|
|
|
all_trigger_words.extend(trigger_words)
|
|
loaded_loras.append(f"{lora_name}: {model_strength}")
|
|
|
|
# Then process loras from kwargs with support for both old and new formats
|
|
loras_list = get_loras_list(kwargs)
|
|
for lora in loras_list:
|
|
if not lora.get('active', False):
|
|
continue
|
|
|
|
lora_name = lora['name']
|
|
strength = float(lora['strength'])
|
|
|
|
# Get lora path and trigger words
|
|
lora_path, trigger_words = asyncio.run(get_lora_info(lora_name))
|
|
|
|
# Apply the LoRA using the resolved path
|
|
model, clip = LoraLoader().load_lora(model, clip, lora_path, strength, strength)
|
|
loaded_loras.append(f"{lora_name}: {strength}")
|
|
|
|
# Add trigger words to collection
|
|
all_trigger_words.extend(trigger_words)
|
|
|
|
# use ',, ' to separate trigger words for group mode
|
|
trigger_words_text = ",, ".join(all_trigger_words) if all_trigger_words else ""
|
|
|
|
# Format loaded_loras as <lora:lora_name:strength> separated by spaces
|
|
formatted_loras = " ".join([f"<lora:{name.split(':')[0].strip()}:{str(strength).strip()}>"
|
|
for name, strength in [item.split(':') for item in loaded_loras]])
|
|
|
|
return (model, clip, trigger_words_text, formatted_loras) |