mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-03-22 05:32:12 -03:00
6a17e75782
- Document dual UI systems: standalone web UI and ComfyUI custom node widgets - Add ComfyUI widget development guidelines including styling and constraints - Update terminology in LoraRandomizerNode from 'frontend/backend' to 'fixed/always' for clarity - Include UI constraints for ComfyUI widgets: minimize vertical space, avoid dynamic height changes, keep UI simple
314 lines
11 KiB
Python
314 lines
11 KiB
Python
"""
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Lora Randomizer Node - Randomly selects LoRAs from a pool with configurable settings.
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This node accepts optional pool_config input to filter available LoRAs, and outputs
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a LORA_STACK with randomly selected LoRAs. Returns UI updates with new random LoRAs
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and tracks the last used combination for reuse.
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"""
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import logging
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import random
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import os
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from ..utils.utils import get_lora_info
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from .utils import extract_lora_name
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logger = logging.getLogger(__name__)
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class LoraRandomizerNode:
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"""Node that randomly selects LoRAs from a pool"""
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NAME = "Lora Randomizer (LoraManager)"
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CATEGORY = "Lora Manager/randomizer"
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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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"randomizer_config": ("RANDOMIZER_CONFIG", {}),
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"loras": ("LORAS", {}),
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},
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"optional": {
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"pool_config": ("POOL_CONFIG", {}),
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},
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}
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RETURN_TYPES = ("LORA_STACK",)
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RETURN_NAMES = ("LORA_STACK",)
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FUNCTION = "randomize"
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OUTPUT_NODE = False
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async def randomize(self, randomizer_config, loras, pool_config=None):
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"""
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Randomize LoRAs based on configuration and pool filters.
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Args:
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randomizer_config: Dict with randomizer settings (count, strength ranges, roll_mode)
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loras: List of LoRA dicts from LORAS widget (includes locked state)
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pool_config: Optional config from LoRA Pool node for filtering
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Returns:
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Dictionary with 'result' (LORA_STACK tuple) and 'ui' (for widget display)
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"""
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from ..services.service_registry import ServiceRegistry
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roll_mode = randomizer_config.get("roll_mode", "always")
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logger.debug(f"[LoraRandomizerNode] roll_mode: {roll_mode}")
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if roll_mode == "fixed":
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ui_loras = loras
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else:
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scanner = await ServiceRegistry.get_lora_scanner()
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ui_loras = await self._generate_random_loras_for_ui(
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scanner, randomizer_config, loras, pool_config
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)
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execution_stack = self._build_execution_stack_from_input(loras)
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return {
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"result": (execution_stack,),
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"ui": {"loras": ui_loras, "last_used": loras},
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}
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def _build_execution_stack_from_input(self, loras):
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"""
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Build LORA_STACK tuple from input loras list for execution.
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Args:
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loras: List of LoRA dicts with name, strength, clipStrength, active
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Returns:
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List of tuples (lora_path, model_strength, clip_strength)
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"""
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lora_stack = []
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for lora in loras:
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if not lora.get("active", False):
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continue
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# Get file path
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lora_path, trigger_words = get_lora_info(lora["name"])
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if not lora_path:
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logger.warning(
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f"[LoraRandomizerNode] Could not find path for LoRA: {lora['name']}"
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)
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continue
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# Normalize path separators
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lora_path = lora_path.replace("/", os.sep)
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# Extract strengths
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model_strength = lora.get("strength", 1.0)
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clip_strength = lora.get("clipStrength", model_strength)
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lora_stack.append((lora_path, model_strength, clip_strength))
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return lora_stack
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async def _generate_random_loras_for_ui(
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self, scanner, randomizer_config, input_loras, pool_config=None
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):
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"""
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Generate new random loras for UI display.
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Args:
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scanner: LoraScanner instance
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randomizer_config: Dict with randomizer settings
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input_loras: Current input loras (for extracting locked loras)
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pool_config: Optional pool filters
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Returns:
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List of LoRA dicts for UI display
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"""
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# Parse randomizer settings
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count_mode = randomizer_config.get("count_mode", "range")
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count_fixed = randomizer_config.get("count_fixed", 5)
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count_min = randomizer_config.get("count_min", 3)
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count_max = randomizer_config.get("count_max", 7)
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model_strength_min = randomizer_config.get("model_strength_min", 0.0)
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model_strength_max = randomizer_config.get("model_strength_max", 1.0)
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use_same_clip_strength = randomizer_config.get("use_same_clip_strength", True)
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clip_strength_min = randomizer_config.get("clip_strength_min", 0.0)
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clip_strength_max = randomizer_config.get("clip_strength_max", 1.0)
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# Determine target count
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if count_mode == "fixed":
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target_count = count_fixed
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else:
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target_count = random.randint(count_min, count_max)
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# Extract locked LoRAs from input
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locked_loras = [lora for lora in input_loras if lora.get("locked", False)]
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locked_count = len(locked_loras)
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# Get available loras from cache
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try:
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cache_data = await scanner.get_cached_data(force_refresh=False)
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if cache_data and hasattr(cache_data, "raw_data"):
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available_loras = cache_data.raw_data
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else:
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available_loras = []
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except Exception as e:
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logger.warning(f"[LoraRandomizerNode] Failed to get lora cache: {e}")
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available_loras = []
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# Apply pool filters if provided
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if pool_config:
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available_loras = await self._apply_pool_filters(
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available_loras, pool_config, scanner
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)
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# Calculate how many new LoRAs to select
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slots_needed = target_count - locked_count
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if slots_needed < 0:
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slots_needed = 0
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# Too many locked, trim to target
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locked_loras = locked_loras[:target_count]
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locked_count = len(locked_loras)
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# Filter out locked LoRAs from available pool
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locked_names = {lora["name"] for lora in locked_loras}
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available_pool = [
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l for l in available_loras if l["file_name"] not in locked_names
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]
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# Ensure we don't try to select more than available
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if slots_needed > len(available_pool):
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slots_needed = len(available_pool)
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# Random sample
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selected = []
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if slots_needed > 0:
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selected = random.sample(available_pool, slots_needed)
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# Generate random strengths for selected LoRAs
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result_loras = []
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for lora in selected:
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model_str = round(random.uniform(model_strength_min, model_strength_max), 2)
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if use_same_clip_strength:
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clip_str = model_str
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else:
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clip_str = round(
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random.uniform(clip_strength_min, clip_strength_max), 2
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)
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result_loras.append(
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{
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"name": lora["file_name"],
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"strength": model_str,
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"clipStrength": clip_str,
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"active": True,
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"expanded": abs(model_str - clip_str) > 0.001,
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"locked": False,
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}
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)
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# Merge with locked LoRAs
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result_loras.extend(locked_loras)
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return result_loras
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async def _apply_pool_filters(self, available_loras, pool_config, scanner):
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"""
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Apply pool_config filters to available LoRAs.
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Args:
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available_loras: List of all LoRA dicts
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pool_config: Dict with filter settings from LoRA Pool node
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scanner: Scanner instance for accessing filter utilities
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Returns:
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Filtered list of LoRA dicts
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"""
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from ..services.lora_service import LoraService
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from ..services.model_query import FilterCriteria
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# Create lora service instance for filtering
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lora_service = LoraService(scanner)
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# Extract filter parameters from pool_config
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selected_base_models = pool_config.get("baseModels", [])
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tags_dict = pool_config.get("tags", {})
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include_tags = tags_dict.get("include", [])
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exclude_tags = tags_dict.get("exclude", [])
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folders_dict = pool_config.get("folders", {})
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include_folders = folders_dict.get("include", [])
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exclude_folders = folders_dict.get("exclude", [])
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license_dict = pool_config.get("license", {})
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no_credit_required = license_dict.get("noCreditRequired", False)
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allow_selling = license_dict.get("allowSelling", False)
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# Build tag filters dict
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tag_filters = {}
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for tag in include_tags:
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tag_filters[tag] = "include"
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for tag in exclude_tags:
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tag_filters[tag] = "exclude"
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# Build folder filter
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# LoRA Pool uses include/exclude folders, we need to apply this logic
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# For now, we'll filter based on folder path matching
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if include_folders or exclude_folders:
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filtered = []
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for lora in available_loras:
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folder = lora.get("folder", "")
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# Check exclude folders first
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excluded = False
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for exclude_folder in exclude_folders:
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if folder.startswith(exclude_folder):
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excluded = True
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break
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if excluded:
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continue
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# Check include folders
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if include_folders:
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included = False
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for include_folder in include_folders:
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if folder.startswith(include_folder):
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included = True
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break
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if not included:
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continue
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filtered.append(lora)
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available_loras = filtered
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# Apply base model filter
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if selected_base_models:
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available_loras = [
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lora
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for lora in available_loras
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if lora.get("base_model") in selected_base_models
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]
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# Apply tag filters
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if tag_filters:
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criteria = FilterCriteria(tags=tag_filters)
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available_loras = lora_service.filter_set.apply(available_loras, criteria)
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# Apply license filters
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# Note: no_credit_required=True means filter out models where credit is NOT required
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# (i.e., keep only models where credit IS required)
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if no_credit_required:
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available_loras = [
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lora
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for lora in available_loras
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if not (lora.get("license_flags", 127) & (1 << 0))
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]
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# allow_selling=True means keep only models where selling generated content is allowed
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if allow_selling:
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available_loras = [
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lora
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for lora in available_loras
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if bool(lora.get("license_flags", 127) & (1 << 1))
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]
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return available_loras
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