mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-03-21 21:22:11 -03:00
565b61d1c2
Introduce a new TextLM node to the Lora Manager extension, providing a simple text input with autocomplete functionality for tags and styles. The node is integrated into the module's import system and node class mappings, enabling users to utilize autocomplete features for efficient prompt creation.
118 lines
4.4 KiB
Python
118 lines
4.4 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 any_type
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import logging
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# 初始化日志记录器
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logger = logging.getLogger(__name__)
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# 定义新节点的类
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class WanVideoLoraTextSelectLM:
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# 节点在UI中显示的名称
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NAME = "WanVideo Lora Select From Text (LoraManager)"
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# 节点所属的分类
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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_lora": ("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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"lora_syntax": ("STRING", {
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"multiline": True,
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"forceInput": True,
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"tooltip": "Connect a TEXT output for LoRA syntax: <lora:name:strength>"
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}),
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},
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"optional": {
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"prev_lora": ("WANVIDLORA",),
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"blocks": ("BLOCKS",)
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}
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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_from_syntax"
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def process_loras_from_syntax(self, lora_syntax, low_mem_load=False, merge_lora=True, **kwargs):
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text_to_process = lora_syntax
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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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loras_list = []
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all_trigger_words = []
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active_loras = []
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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_lora:
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low_mem_load = False
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parts = text_to_process.split('<lora:')
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for part in parts[1:]:
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end_index = part.find('>')
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if end_index == -1:
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continue
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content = part[:end_index]
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lora_parts = content.split(':')
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lora_name_raw = ""
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model_strength = 1.0
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clip_strength = 1.0
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if len(lora_parts) == 2:
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lora_name_raw = lora_parts[0].strip()
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try:
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model_strength = float(lora_parts[1])
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clip_strength = model_strength
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except (ValueError, IndexError):
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logger.warning(f"Invalid strength for LoRA '{lora_name_raw}'. Skipping.")
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continue
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elif len(lora_parts) >= 3:
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lora_name_raw = lora_parts[0].strip()
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try:
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model_strength = float(lora_parts[1])
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clip_strength = float(lora_parts[2])
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except (ValueError, IndexError):
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logger.warning(f"Invalid strengths for LoRA '{lora_name_raw}'. Skipping.")
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continue
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else:
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continue
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lora_path, trigger_words = get_lora_info(lora_name_raw)
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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_lora,
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}
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loras_list.append(lora_item)
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active_loras.append((lora_name_raw, model_strength, clip_strength))
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all_trigger_words.extend(trigger_words)
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trigger_words_text = ",, ".join(all_trigger_words) if all_trigger_words else ""
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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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formatted_loras.append(f"<lora:{name}:{str(model_strength).strip()}:{str(clip_strength).strip()}>")
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else:
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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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