mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-03-22 05:32:12 -03:00
105 lines
4.5 KiB
Python
105 lines
4.5 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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import asyncio
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from .utils import FlexibleOptionalInputType, any_type, get_lora_info, extract_lora_name, get_loras_list
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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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def load_loras(self, model, 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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clip = kwargs.get('clip', None)
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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 = extract_lora_name(lora_path)
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_, trigger_words = asyncio.run(get_lora_info(lora_name))
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all_trigger_words.extend(trigger_words)
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# Add clip strength to output if different from model strength
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if abs(model_strength - clip_strength) > 0.001:
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loaded_loras.append(f"{lora_name}: {model_strength},{clip_strength}")
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else:
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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 = 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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model_strength = float(lora['strength'])
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# Get clip strength - use model strength as default if not specified
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clip_strength = float(lora.get('clipStrength', model_strength))
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# Get lora path and trigger words
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lora_path, trigger_words = asyncio.run(get_lora_info(lora_name))
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# Apply the LoRA using the resolved path with separate strengths
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model, clip = LoraLoader().load_lora(model, clip, lora_path, model_strength, clip_strength)
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# Include clip strength in output if different from model strength
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if abs(model_strength - clip_strength) > 0.001:
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loaded_loras.append(f"{lora_name}: {model_strength},{clip_strength}")
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else:
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loaded_loras.append(f"{lora_name}: {model_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 with support for both formats
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formatted_loras = []
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for item in loaded_loras:
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parts = item.split(":")
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lora_name = parts[0].strip()
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strength_parts = parts[1].strip().split(",")
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if len(strength_parts) > 1:
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# Different model and clip strengths
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model_str = strength_parts[0].strip()
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clip_str = strength_parts[1].strip()
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formatted_loras.append(f"<lora:{lora_name}:{model_str}:{clip_str}>")
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else:
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# Same strength for both
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model_str = strength_parts[0].strip()
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formatted_loras.append(f"<lora:{lora_name}:{model_str}>")
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formatted_loras_text = " ".join(formatted_loras)
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return (model, clip, trigger_words_text, formatted_loras_text) |