mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-03-22 05:32:12 -03:00
c48095d9c6
Remove direct imports of IO type constants from comfy.comfy_types and replace them with string literals "STRING" in input type definitions and return types. This improves code portability and reduces dependency on external type definitions. Changes made across multiple files: - Remove `from comfy.comfy_types import IO` imports - Replace `IO.STRING` with "STRING" in INPUT_TYPES and RETURN_TYPES - Move CLIPTextEncode import to function scope in prompt.py for better dependency management This refactor maintains the same functionality while making the code more self-contained and reducing external dependencies.
86 lines
3.4 KiB
Python
86 lines
3.4 KiB
Python
import os
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from ..utils.utils import get_lora_info
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from .utils import FlexibleOptionalInputType, any_type, extract_lora_name, get_loras_list
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import logging
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logger = logging.getLogger(__name__)
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class LoraStacker:
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NAME = "Lora Stacker (LoraManager)"
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CATEGORY = "Lora Manager/stackers"
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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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"text": ("STRING", {
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"multiline": True,
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"pysssss.autocomplete": False,
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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 = ("LORA_STACK", "STRING", "STRING")
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RETURN_NAMES = ("LORA_STACK", "trigger_words", "active_loras")
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FUNCTION = "stack_loras"
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def stack_loras(self, text, **kwargs):
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"""Stacks multiple LoRAs based on the kwargs input without loading them."""
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stack = []
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active_loras = []
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all_trigger_words = []
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# Process existing lora_stack if available
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lora_stack = kwargs.get('lora_stack', None)
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if (lora_stack):
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stack.extend(lora_stack)
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# Get trigger words from existing stack entries
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for lora_path, _, _ in lora_stack:
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lora_name = extract_lora_name(lora_path)
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_, trigger_words = get_lora_info(lora_name)
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all_trigger_words.extend(trigger_words)
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# 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 = get_lora_info(lora_name)
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# Add to stack without loading
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# replace '/' with os.sep to avoid different OS path format
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stack.append((lora_path.replace('/', os.sep), model_strength, clip_strength))
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active_loras.append((lora_name, model_strength, clip_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 active_loras with support for both formats
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formatted_loras = []
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for name, model_strength, clip_strength in active_loras:
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if abs(model_strength - clip_strength) > 0.001:
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# Different model and clip strengths
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formatted_loras.append(f"<lora:{name}:{str(model_strength).strip()}:{str(clip_strength).strip()}>")
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else:
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# Same strength for both
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formatted_loras.append(f"<lora:{name}:{str(model_strength).strip()}>")
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active_loras_text = " ".join(formatted_loras)
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return (stack, trigger_words_text, active_loras_text)
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