ComfyUI-Lora-Manager/py/nodes/lora_pool.py

"""
LoRA Pool Node - Defines filter configuration for LoRA selection.

This node provides a visual filter editor that generates a LORA_POOL_CONFIG
object for use by downstream nodes (like LoRA Randomizer).
"""

import logging

logger = logging.getLogger(__name__)


class LoraPoolLM:
    """
    A node that defines LoRA filter criteria through a Vue-based widget.

    Outputs a LORA_POOL_CONFIG that can be consumed by:
    - Frontend: LoRA Randomizer widget reads connected pool's widget value
    - Backend: LoRA Randomizer receives config during workflow execution
    """

    NAME = "Lora Pool (LoraManager)"
    CATEGORY = "Lora Manager/randomizer"

    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "pool_config": ("LORA_POOL_CONFIG", {}),
            },
            "hidden": {
                # Hidden input to pass through unique node ID for frontend
                "unique_id": "UNIQUE_ID",
            },
        }

    RETURN_TYPES = ("POOL_CONFIG",)
    RETURN_NAMES = ("POOL_CONFIG",)

    FUNCTION = "process"
    OUTPUT_NODE = False

    def process(self, pool_config, unique_id=None):
        """
        Pass through the pool configuration filters.

        The config is generated entirely by the frontend widget.
        This function validates and returns only the filters field.

        Args:
            pool_config: Dict containing filter criteria from widget
            unique_id: Node's unique ID (hidden)

        Returns:
            Tuple containing the filters dict from pool_config
        """
        # Validate required structure
        if not isinstance(pool_config, dict):
            logger.warning("Invalid pool_config type, using empty config")
            pool_config = self._default_config()

        # Ensure version field exists
        if "version" not in pool_config:
            pool_config["version"] = 1

        # Extract filters field
        filters = pool_config.get("filters", self._default_config()["filters"])

        # Log for debugging
        logger.debug(f"[LoraPoolLM] Processing filters: {filters}")

        return (filters,)

    @staticmethod
    def _default_config():
        """Return default empty configuration."""
        return {
            "version": 1,
            "filters": {
                "baseModels": [],
                "tags": {"include": [], "exclude": []},
                "folders": {"include": [], "exclude": []},
                "favoritesOnly": False,
                "license": {"noCreditRequired": False, "allowSelling": False},
                "includeEmptyLora": False,
            },
            "preview": {"matchCount": 0, "lastUpdated": 0},
        }