mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-03-22 13:42:12 -03:00
5a93c40b79
- Changed warning logs to debug logs in CivitaiClient and RecipeScanner for better log granularity. - Updated the mapper registration function name for clarity and adjusted related logging messages. - Enhanced extension loading process to automatically register mappers from NODE_MAPPERS_EXT, improving modularity and maintainability.
367 lines
13 KiB
Python
367 lines
13 KiB
Python
"""
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Node mappers for ComfyUI workflow parsing
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"""
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import logging
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import os
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import importlib.util
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import inspect
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from typing import Dict, List, Any, Optional, Union, Type, Callable, Tuple
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logger = logging.getLogger(__name__)
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# Global mapper registry
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_MAPPER_REGISTRY: Dict[str, Dict] = {}
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# =============================================================================
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# Mapper Definition Functions
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# =============================================================================
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def create_mapper(
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node_type: str,
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inputs_to_track: List[str],
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transform_func: Callable[[Dict], Any] = None
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) -> Dict:
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"""Create a mapper definition for a node type"""
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mapper = {
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"node_type": node_type,
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"inputs_to_track": inputs_to_track,
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"transform": transform_func or (lambda inputs: inputs)
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}
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return mapper
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def register_mapper(mapper: Dict) -> None:
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"""Register a node mapper in the global registry"""
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_MAPPER_REGISTRY[mapper["node_type"]] = mapper
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logger.debug(f"Registered mapper for node type: {mapper['node_type']}")
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def get_mapper(node_type: str) -> Optional[Dict]:
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"""Get a mapper for the specified node type"""
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return _MAPPER_REGISTRY.get(node_type)
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def get_all_mappers() -> Dict[str, Dict]:
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"""Get all registered mappers"""
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return _MAPPER_REGISTRY.copy()
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# =============================================================================
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# Node Processing Function
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# =============================================================================
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def process_node(node_id: str, node_data: Dict, workflow: Dict, parser: 'WorkflowParser') -> Any:
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"""Process a node using its mapper and extract relevant information"""
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node_type = node_data.get("class_type")
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mapper = get_mapper(node_type)
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if not mapper:
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logger.warning(f"No mapper found for node type: {node_type}")
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return None
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result = {}
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# Extract inputs based on the mapper's tracked inputs
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for input_name in mapper["inputs_to_track"]:
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if input_name in node_data.get("inputs", {}):
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input_value = node_data["inputs"][input_name]
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# Check if input is a reference to another node's output
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if isinstance(input_value, list) and len(input_value) == 2:
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try:
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# Format is [node_id, output_slot]
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ref_node_id, output_slot = input_value
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# Convert node_id to string if it's an integer
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if isinstance(ref_node_id, int):
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ref_node_id = str(ref_node_id)
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# Recursively process the referenced node
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ref_value = parser.process_node(ref_node_id, workflow)
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if ref_value is not None:
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result[input_name] = ref_value
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else:
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# If we couldn't get a value from the reference, store the raw value
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result[input_name] = input_value
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except Exception as e:
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logger.error(f"Error processing reference in node {node_id}, input {input_name}: {e}")
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result[input_name] = input_value
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else:
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# Direct value
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result[input_name] = input_value
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# Apply the transform function
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try:
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return mapper["transform"](result)
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except Exception as e:
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logger.error(f"Error in transform function for node {node_id} of type {node_type}: {e}")
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return result
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# =============================================================================
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# Transform Functions
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# =============================================================================
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def transform_ksampler(inputs: Dict) -> Dict:
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"""Transform function for KSampler nodes"""
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result = {
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"seed": str(inputs.get("seed", "")),
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"steps": str(inputs.get("steps", "")),
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"cfg": str(inputs.get("cfg", "")),
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"sampler": inputs.get("sampler_name", ""),
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"scheduler": inputs.get("scheduler", ""),
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}
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# Process positive prompt
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if "positive" in inputs:
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result["prompt"] = inputs["positive"]
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# Process negative prompt
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if "negative" in inputs:
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result["negative_prompt"] = inputs["negative"]
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# Get dimensions from latent image
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if "latent_image" in inputs and isinstance(inputs["latent_image"], dict):
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width = inputs["latent_image"].get("width", 0)
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height = inputs["latent_image"].get("height", 0)
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if width and height:
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result["size"] = f"{width}x{height}"
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# Add clip_skip if present
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if "clip_skip" in inputs:
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result["clip_skip"] = str(inputs.get("clip_skip", ""))
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# Add guidance if present
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if "guidance" in inputs:
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result["guidance"] = str(inputs.get("guidance", ""))
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# Add model if present
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if "model" in inputs:
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result["checkpoint"] = inputs.get("model", {}).get("checkpoint", "")
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result["loras"] = inputs.get("model", {}).get("loras", "")
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return result
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def transform_empty_latent(inputs: Dict) -> Dict:
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"""Transform function for EmptyLatentImage nodes"""
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width = inputs.get("width", 0)
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height = inputs.get("height", 0)
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return {"width": width, "height": height, "size": f"{width}x{height}"}
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def transform_clip_text(inputs: Dict) -> Any:
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"""Transform function for CLIPTextEncode nodes"""
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return inputs.get("text", "")
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def transform_lora_loader(inputs: Dict) -> Dict:
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"""Transform function for LoraLoader nodes"""
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loras_data = inputs.get("loras", [])
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lora_stack = inputs.get("lora_stack", {}).get("lora_stack", [])
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lora_texts = []
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# Process loras array
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if isinstance(loras_data, dict) and "__value__" in loras_data:
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loras_list = loras_data["__value__"]
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elif isinstance(loras_data, list):
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loras_list = loras_data
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else:
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loras_list = []
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# Process each active lora entry
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for lora in loras_list:
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if isinstance(lora, dict) and lora.get("active", False):
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lora_name = lora.get("name", "")
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strength = lora.get("strength", 1.0)
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lora_texts.append(f"<lora:{lora_name}:{strength}>")
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# Process lora_stack if valid
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if lora_stack and isinstance(lora_stack, list):
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if not (len(lora_stack) == 2 and isinstance(lora_stack[0], (str, int)) and isinstance(lora_stack[1], int)):
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for stack_entry in lora_stack:
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lora_name = stack_entry[0]
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strength = stack_entry[1]
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lora_texts.append(f"<lora:{lora_name}:{strength}>")
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result = {
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"checkpoint": inputs.get("model", {}).get("checkpoint", ""),
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"loras": " ".join(lora_texts)
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}
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return result
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def transform_lora_stacker(inputs: Dict) -> Dict:
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"""Transform function for LoraStacker nodes"""
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loras_data = inputs.get("loras", [])
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result_stack = []
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# Handle existing stack entries
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existing_stack = []
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lora_stack_input = inputs.get("lora_stack", [])
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if isinstance(lora_stack_input, dict) and "lora_stack" in lora_stack_input:
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existing_stack = lora_stack_input["lora_stack"]
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elif isinstance(lora_stack_input, list):
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if not (len(lora_stack_input) == 2 and isinstance(lora_stack_input[0], (str, int)) and
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isinstance(lora_stack_input[1], int)):
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existing_stack = lora_stack_input
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# Add existing entries
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if existing_stack:
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result_stack.extend(existing_stack)
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# Process new loras
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if isinstance(loras_data, dict) and "__value__" in loras_data:
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loras_list = loras_data["__value__"]
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elif isinstance(loras_data, list):
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loras_list = loras_data
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else:
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loras_list = []
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for lora in loras_list:
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if isinstance(lora, dict) and lora.get("active", False):
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lora_name = lora.get("name", "")
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strength = float(lora.get("strength", 1.0))
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result_stack.append((lora_name, strength))
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return {"lora_stack": result_stack}
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def transform_trigger_word_toggle(inputs: Dict) -> str:
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"""Transform function for TriggerWordToggle nodes"""
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toggle_data = inputs.get("toggle_trigger_words", [])
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if isinstance(toggle_data, dict) and "__value__" in toggle_data:
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toggle_words = toggle_data["__value__"]
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elif isinstance(toggle_data, list):
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toggle_words = toggle_data
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else:
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toggle_words = []
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# Filter active trigger words
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active_words = []
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for item in toggle_words:
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if isinstance(item, dict) and item.get("active", False):
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word = item.get("text", "")
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if word and not word.startswith("__dummy"):
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active_words.append(word)
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return ", ".join(active_words)
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def transform_flux_guidance(inputs: Dict) -> Dict:
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"""Transform function for FluxGuidance nodes"""
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result = {}
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if "guidance" in inputs:
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result["guidance"] = inputs["guidance"]
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if "conditioning" in inputs:
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conditioning = inputs["conditioning"]
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if isinstance(conditioning, str):
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result["prompt"] = conditioning
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else:
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result["prompt"] = "Unknown prompt"
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return result
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# =============================================================================
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# Node Mapper Definitions
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# =============================================================================
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# Central definition of all supported node types and their configurations
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NODE_MAPPERS = {
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# ComfyUI core nodes
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"KSampler": {
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"inputs_to_track": [
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"seed", "steps", "cfg", "sampler_name", "scheduler",
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"denoise", "positive", "negative", "latent_image",
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"model", "clip_skip"
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],
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"transform_func": transform_ksampler
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},
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"EmptyLatentImage": {
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"inputs_to_track": ["width", "height", "batch_size"],
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"transform_func": transform_empty_latent
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},
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"EmptySD3LatentImage": {
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"inputs_to_track": ["width", "height", "batch_size"],
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"transform_func": transform_empty_latent
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},
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"CLIPTextEncode": {
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"inputs_to_track": ["text", "clip"],
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"transform_func": transform_clip_text
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},
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"FluxGuidance": {
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"inputs_to_track": ["guidance", "conditioning"],
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"transform_func": transform_flux_guidance
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},
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# LoraManager nodes
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"Lora Loader (LoraManager)": {
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"inputs_to_track": ["model", "loras", "lora_stack"],
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"transform_func": transform_lora_loader
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},
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"Lora Stacker (LoraManager)": {
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"inputs_to_track": ["loras", "lora_stack"],
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"transform_func": transform_lora_stacker
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},
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"TriggerWord Toggle (LoraManager)": {
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"inputs_to_track": ["toggle_trigger_words"],
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"transform_func": transform_trigger_word_toggle
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}
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}
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def register_all_mappers() -> None:
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"""Register all mappers from the NODE_MAPPERS dictionary"""
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for node_type, config in NODE_MAPPERS.items():
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mapper = create_mapper(
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node_type=node_type,
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inputs_to_track=config["inputs_to_track"],
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transform_func=config["transform_func"]
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)
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register_mapper(mapper)
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logger.info(f"Registered {len(NODE_MAPPERS)} node mappers")
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# =============================================================================
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# Extension Loading
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# =============================================================================
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def load_extensions(ext_dir: str = None) -> None:
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"""
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Load mapper extensions from the specified directory
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Extension files should define a NODE_MAPPERS_EXT dictionary containing mapper configurations.
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These will be added to the global NODE_MAPPERS dictionary and registered automatically.
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"""
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# Use default path if none provided
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if ext_dir is None:
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# Get the directory of this file
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current_dir = os.path.dirname(os.path.abspath(__file__))
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ext_dir = os.path.join(current_dir, 'ext')
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# Ensure the extension directory exists
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if not os.path.exists(ext_dir):
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os.makedirs(ext_dir, exist_ok=True)
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logger.info(f"Created extension directory: {ext_dir}")
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return
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# Load each Python file in the extension directory
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for filename in os.listdir(ext_dir):
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if filename.endswith('.py') and not filename.startswith('_'):
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module_path = os.path.join(ext_dir, filename)
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module_name = f"workflow.ext.{filename[:-3]}" # Remove .py
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try:
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# Load the module
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spec = importlib.util.spec_from_file_location(module_name, module_path)
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if spec and spec.loader:
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module = importlib.util.module_from_spec(spec)
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spec.loader.exec_module(module)
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# Check if the module defines NODE_MAPPERS_EXT
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if hasattr(module, 'NODE_MAPPERS_EXT'):
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# Add the extension mappers to the global NODE_MAPPERS dictionary
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NODE_MAPPERS.update(module.NODE_MAPPERS_EXT)
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logger.info(f"Added {len(module.NODE_MAPPERS_EXT)} mappers from extension: {filename}")
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else:
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logger.warning(f"Extension {filename} does not define NODE_MAPPERS_EXT dictionary")
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except Exception as e:
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logger.warning(f"Error loading extension {filename}: {e}")
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# Re-register all mappers after loading extensions
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register_all_mappers()
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# Initialize the registry with default mappers
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# register_default_mappers() |