mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-03-21 21:22:11 -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.
98 lines
4.1 KiB
Python
98 lines
4.1 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 WanVideoLoraSelect:
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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": ("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 = ("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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