mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-03-22 05:32:12 -03:00
e7dffbbb1e
- Introduced logging to track unexpected formats in LoRA and trigger word data. - Refactored LoRA processing to support both old and new kwargs formats in LoraLoader and LoraStacker. - Enhanced trigger word processing to handle different data formats in TriggerWordToggle. - Improved code readability and maintainability by extracting common logic into helper methods.
123 lines
5.2 KiB
Python
123 lines
5.2 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
|
|
|
|
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"
|
|
|
|
async def get_lora_info(self, lora_name):
|
|
"""Get the lora path and trigger words from cache"""
|
|
scanner = await LoraScanner.get_instance()
|
|
cache = await scanner.get_cached_data()
|
|
|
|
for item in cache.raw_data:
|
|
if item.get('file_name') == lora_name:
|
|
file_path = item.get('file_path')
|
|
if file_path:
|
|
for root in config.loras_roots:
|
|
root = root.replace(os.sep, '/')
|
|
if file_path.startswith(root):
|
|
relative_path = os.path.relpath(file_path, root).replace(os.sep, '/')
|
|
# Get trigger words from civitai metadata
|
|
civitai = item.get('civitai', {})
|
|
trigger_words = civitai.get('trainedWords', []) if civitai else []
|
|
return relative_path, trigger_words
|
|
return lora_name, [] # Fallback if not found
|
|
|
|
def extract_lora_name(self, lora_path):
|
|
"""Extract the lora name from a lora path (e.g., 'IL\\aorunIllstrious.safetensors' -> 'aorunIllstrious')"""
|
|
# Get the basename without extension
|
|
basename = os.path.basename(lora_path)
|
|
return os.path.splitext(basename)[0]
|
|
|
|
def _get_loras_list(self, kwargs):
|
|
"""Helper to extract loras list from either old or new kwargs format"""
|
|
if 'loras' not in kwargs:
|
|
return []
|
|
|
|
loras_data = kwargs['loras']
|
|
# Handle new format: {'loras': {'__value__': [...]}}
|
|
if isinstance(loras_data, dict) and '__value__' in loras_data:
|
|
return loras_data['__value__']
|
|
# Handle old format: {'loras': [...]}
|
|
elif isinstance(loras_data, list):
|
|
return loras_data
|
|
# Unexpected format
|
|
else:
|
|
logger.warning(f"Unexpected loras format: {type(loras_data)}")
|
|
return []
|
|
|
|
def load_loras(self, model, clip, text, **kwargs):
|
|
"""Loads multiple LoRAs based on the kwargs input and lora_stack."""
|
|
loaded_loras = []
|
|
all_trigger_words = []
|
|
|
|
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 = self.extract_lora_name(lora_path)
|
|
_, trigger_words = asyncio.run(self.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 = self._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(self.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) |