mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-03-22 13:42:12 -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
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from nodes import LoraLoader
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from comfy.comfy_types import IO # type: ignore
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from ..services.lora_scanner import LoraScanner
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from ..config import config
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import asyncio
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import os
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from .utils import FlexibleOptionalInputType, any_type
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logger = logging.getLogger(__name__)
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class LoraManagerLoader:
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NAME = "Lora Loader (LoraManager)"
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CATEGORY = "Lora Manager/loaders"
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"model": ("MODEL",),
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"clip": ("CLIP",),
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"text": (IO.STRING, {
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"multiline": True,
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"dynamicPrompts": True,
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"tooltip": "Format: <lora:lora_name:strength> separated by spaces or punctuation",
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"placeholder": "LoRA syntax input: <lora:name:strength>"
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}),
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},
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"optional": FlexibleOptionalInputType(any_type),
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}
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RETURN_TYPES = ("MODEL", "CLIP", IO.STRING, IO.STRING)
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RETURN_NAMES = ("MODEL", "CLIP", "trigger_words", "loaded_loras")
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FUNCTION = "load_loras"
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async def get_lora_info(self, lora_name):
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"""Get the lora path and trigger words from cache"""
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scanner = await LoraScanner.get_instance()
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cache = await scanner.get_cached_data()
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for item in cache.raw_data:
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if item.get('file_name') == lora_name:
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file_path = item.get('file_path')
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if file_path:
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for root in config.loras_roots:
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root = root.replace(os.sep, '/')
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if file_path.startswith(root):
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relative_path = os.path.relpath(file_path, root).replace(os.sep, '/')
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# Get trigger words from civitai metadata
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civitai = item.get('civitai', {})
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trigger_words = civitai.get('trainedWords', []) if civitai else []
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return relative_path, trigger_words
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return lora_name, [] # Fallback if not found
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def extract_lora_name(self, lora_path):
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"""Extract the lora name from a lora path (e.g., 'IL\\aorunIllstrious.safetensors' -> 'aorunIllstrious')"""
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# Get the basename without extension
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basename = os.path.basename(lora_path)
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return os.path.splitext(basename)[0]
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def _get_loras_list(self, kwargs):
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"""Helper to extract loras list from either old or new kwargs format"""
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if 'loras' not in kwargs:
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return []
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loras_data = kwargs['loras']
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# Handle new format: {'loras': {'__value__': [...]}}
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if isinstance(loras_data, dict) and '__value__' in loras_data:
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return loras_data['__value__']
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# Handle old format: {'loras': [...]}
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elif isinstance(loras_data, list):
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return loras_data
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# Unexpected format
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else:
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logger.warning(f"Unexpected loras format: {type(loras_data)}")
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return []
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def load_loras(self, model, clip, text, **kwargs):
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"""Loads multiple LoRAs based on the kwargs input and lora_stack."""
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loaded_loras = []
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all_trigger_words = []
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lora_stack = kwargs.get('lora_stack', None)
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# First process lora_stack if available
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if lora_stack:
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for lora_path, model_strength, clip_strength in lora_stack:
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# Apply the LoRA using the provided path and strengths
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model, clip = LoraLoader().load_lora(model, clip, lora_path, model_strength, clip_strength)
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# Extract lora name for trigger words lookup
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lora_name = self.extract_lora_name(lora_path)
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_, trigger_words = asyncio.run(self.get_lora_info(lora_name))
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all_trigger_words.extend(trigger_words)
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loaded_loras.append(f"{lora_name}: {model_strength}")
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# Then process loras from kwargs with support for both old and new formats
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loras_list = self._get_loras_list(kwargs)
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for lora in loras_list:
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if not lora.get('active', False):
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continue
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lora_name = lora['name']
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strength = float(lora['strength'])
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# Get lora path and trigger words
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lora_path, trigger_words = asyncio.run(self.get_lora_info(lora_name))
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# Apply the LoRA using the resolved path
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model, clip = LoraLoader().load_lora(model, clip, lora_path, strength, strength)
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loaded_loras.append(f"{lora_name}: {strength}")
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# Add trigger words to collection
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all_trigger_words.extend(trigger_words)
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# use ',, ' to separate trigger words for group mode
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trigger_words_text = ",, ".join(all_trigger_words) if all_trigger_words else ""
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# Format loaded_loras as <lora:lora_name:strength> separated by spaces
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formatted_loras = " ".join([f"<lora:{name.split(':')[0].strip()}:{str(strength).strip()}>"
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for name, strength in [item.split(':') for item in loaded_loras]])
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return (model, clip, trigger_words_text, formatted_loras) |