alphard/ComfyUI-Lora-Manager
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refactor(lora-loader, lora-stacker, loras-widget): enhance handling of model and clip strengths; update formatting and UI interactions. Fixes https://github.com/willmiao/ComfyUI-Lora-Manager/issues/171

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Will Miao
2025-05-09 11:05:59 +08:00
parent 9169bbd04d
commit 969f949330
5 changed files with 362 additions and 53 deletions
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48
py/nodes/lora_loader.py
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@@ -1,10 +1,7 @@
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__)
@@ -51,23 +48,35 @@ class LoraManagerLoader:
_, trigger_words = asyncio.run(get_lora_info(lora_name))
all_trigger_words.extend(trigger_words)
loaded_loras.append(f"{lora_name}: {model_strength}")
# Add clip strength to output if different from model strength
if abs(model_strength - clip_strength) > 0.001:
loaded_loras.append(f"{lora_name}: {model_strength},{clip_strength}")
else:
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)
print(f"Loaded loras list: {loras_list}")
for lora in loras_list:
if not lora.get('active', False):
continue
lora_name = lora['name']
strength = float(lora['strength'])
model_strength = float(lora['strength'])
# Get clip strength - use model strength as default if not specified
clip_strength = float(lora.get('clipStrength', model_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}")
# Apply the LoRA using the resolved path with separate strengths
model, clip = LoraLoader().load_lora(model, clip, lora_path, model_strength, clip_strength)
# Include clip strength in output if different from model strength
if abs(model_strength - clip_strength) > 0.001:
loaded_loras.append(f"{lora_name}: {model_strength},{clip_strength}")
else:
loaded_loras.append(f"{lora_name}: {model_strength}")
# Add trigger words to collection
all_trigger_words.extend(trigger_words)
@@ -75,8 +84,23 @@ class LoraManagerLoader:
# 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]])
# Format loaded_loras with support for both formats
formatted_loras = []
for item in loaded_loras:
parts = item.split(":")
lora_name = parts[0].strip()
strength_parts = parts[1].strip().split(",")
if len(strength_parts) > 1:
# Different model and clip strengths
model_str = strength_parts[0].strip()
clip_str = strength_parts[1].strip()
formatted_loras.append(f"<lora:{lora_name}:{model_str}:{clip_str}>")
else:
# Same strength for both
model_str = strength_parts[0].strip()
formatted_loras.append(f"<lora:{lora_name}:{model_str}>")
formatted_loras_text = " ".join(formatted_loras)
return (model, clip, trigger_words_text, formatted_loras)
return (model, clip, trigger_words_text, formatted_loras_text)