mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-03-21 21:22:11 -03:00
194f2f702c
- Change `STRING` input type to `AUTOCOMPLETE_TEXT_LORAS` in LoraManagerLoader, LoraStacker, and WanVideoLoraSelectLM nodes for LoRA syntax input - Change `STRING` input type to `AUTOCOMPLETE_TEXT_EMBEDDINGS` in PromptLoraManager node for prompt input - Remove manual multiline, autocomplete, and dynamicPrompts configurations in favor of built-in autocomplete types - Update placeholder text for consistency across nodes - Remove unused `setupInputWidgetWithAutocomplete` mock from frontend tests - Add Vue app cleanup logic to prevent memory leaks in widget management
83 lines
3.3 KiB
Python
83 lines
3.3 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": ("AUTOCOMPLETE_TEXT_LORAS", {
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"placeholder": "Type LoRA syntax...",
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"tooltip": "Format: <lora:lora_name:strength> separated by spaces or punctuation",
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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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