mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-03-22 13:42:12 -03:00
95 lines
4.0 KiB
Python
95 lines
4.0 KiB
Python
import folder_paths # type: ignore
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from ..utils.utils import get_lora_info
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from .utils import FlexibleOptionalInputType, any_type, get_loras_list
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import logging
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logger = logging.getLogger(__name__)
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class WanVideoLoraSelectLM:
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NAME = "WanVideo Lora Select (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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"low_mem_load": ("BOOLEAN", {"default": False, "tooltip": "Load LORA models with less VRAM usage, slower loading. This affects ALL LoRAs, not just the current ones. No effect if merge_loras is False"}),
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"merge_loras": ("BOOLEAN", {"default": True, "tooltip": "Merge LoRAs into the model, otherwise they are loaded on the fly. Always disabled for GGUF and scaled fp8 models. This affects ALL LoRAs, not just the current one"}),
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"text": ("AUTOCOMPLETE_TEXT_LORAS", {
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"placeholder": "Search LoRAs to add...",
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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 = ("WANVIDLORA", "STRING", "STRING")
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RETURN_NAMES = ("lora", "trigger_words", "active_loras")
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FUNCTION = "process_loras"
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def process_loras(self, text, low_mem_load=False, merge_loras=True, **kwargs):
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loras_list = []
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all_trigger_words = []
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active_loras = []
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# Process existing prev_lora if available
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prev_lora = kwargs.get('prev_lora', None)
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if prev_lora is not None:
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loras_list.extend(prev_lora)
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if not merge_loras:
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low_mem_load = False # Unmerged LoRAs don't need low_mem_load
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# Get blocks if available
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blocks = kwargs.get('blocks', {})
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selected_blocks = blocks.get("selected_blocks", {})
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layer_filter = blocks.get("layer_filter", "")
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# Process loras from kwargs with support for both old and new formats
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loras_from_widget = get_loras_list(kwargs)
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for lora in loras_from_widget:
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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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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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# Create lora item for WanVideo format
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lora_item = {
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"path": folder_paths.get_full_path("loras", lora_path),
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"strength": model_strength,
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"name": lora_path.split(".")[0],
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"blocks": selected_blocks,
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"layer_filter": layer_filter,
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"low_mem_load": low_mem_load,
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"merge_loras": merge_loras,
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}
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# Add to list and collect active loras
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loras_list.append(lora_item)
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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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# Format trigger_words for output
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trigger_words_text = ",, ".join(all_trigger_words) if all_trigger_words else ""
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# Format active_loras for output
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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 (loras_list, trigger_words_text, active_loras_text)
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