""" Main workflow parser implementation for ComfyUI """ import json import logging from typing import Dict, List, Any, Optional, Union, Set from .mappers import get_mapper, get_all_mappers, load_extensions from .utils import ( load_workflow, save_output, find_node_by_type, trace_model_path ) logger = logging.getLogger(__name__) class WorkflowParser: """Parser for ComfyUI workflows""" def __init__(self, load_extensions_on_init: bool = True): """Initialize the parser with mappers""" self.processed_nodes: Set[str] = set() # Track processed nodes to avoid cycles self.node_results_cache: Dict[str, Any] = {} # Cache for processed node results # Load extensions if requested if load_extensions_on_init: load_extensions() def process_node(self, node_id: str, workflow: Dict) -> Any: """Process a single node and extract relevant information""" # Return cached result if available if node_id in self.node_results_cache: return self.node_results_cache[node_id] # Check if we're in a cycle if node_id in self.processed_nodes: return None # Mark this node as being processed (to detect cycles) self.processed_nodes.add(node_id) if node_id not in workflow: self.processed_nodes.remove(node_id) return None node_data = workflow[node_id] node_type = node_data.get("class_type") result = None mapper = get_mapper(node_type) if mapper: try: result = mapper.process(node_id, node_data, workflow, self) # Cache the result self.node_results_cache[node_id] = result except Exception as e: logger.error(f"Error processing node {node_id} of type {node_type}: {e}", exc_info=True) # Return a partial result or None depending on how we want to handle errors result = {} # Remove node from processed set to allow it to be processed again in a different context self.processed_nodes.remove(node_id) return result def collect_loras_from_model(self, model_input: List, workflow: Dict) -> str: """Collect loras information from the model node chain""" if not isinstance(model_input, list) or len(model_input) != 2: return "" model_node_id, _ = model_input # Convert node_id to string if it's an integer if isinstance(model_node_id, int): model_node_id = str(model_node_id) # Process the model node model_result = self.process_node(model_node_id, workflow) # If this is a Lora Loader node, return the loras text if model_result and isinstance(model_result, dict) and "loras" in model_result: return model_result["loras"] # If not a lora loader, check the node's inputs for a model connection node_data = workflow.get(model_node_id, {}) inputs = node_data.get("inputs", {}) # If this node has a model input, follow that path if "model" in inputs and isinstance(inputs["model"], list): return self.collect_loras_from_model(inputs["model"], workflow) return "" def parse_workflow(self, workflow_data: Union[str, Dict], output_path: Optional[str] = None) -> Dict: """ Parse the workflow and extract generation parameters Args: workflow_data: The workflow data as a dictionary or a file path output_path: Optional path to save the output JSON Returns: Dictionary containing extracted parameters """ # Load workflow from file if needed if isinstance(workflow_data, str): workflow = load_workflow(workflow_data) else: workflow = workflow_data # Reset the processed nodes tracker and cache self.processed_nodes = set() self.node_results_cache = {} # Find the KSampler node ksampler_node_id = find_node_by_type(workflow, "KSampler") if not ksampler_node_id: logger.warning("No KSampler node found in workflow") return {} # Start parsing from the KSampler node result = { "gen_params": {}, "loras": "" } # Process KSampler node to extract parameters ksampler_result = self.process_node(ksampler_node_id, workflow) if ksampler_result: # Process the result for key, value in ksampler_result.items(): # Special handling for the positive prompt from FluxGuidance if key == "positive" and isinstance(value, dict): # Extract guidance value if "guidance" in value: result["gen_params"]["guidance"] = value["guidance"] # Extract prompt if "prompt" in value: result["gen_params"]["prompt"] = value["prompt"] else: # Normal handling for other values result["gen_params"][key] = value # Process the positive prompt node if it exists and we don't have a prompt yet if "prompt" not in result["gen_params"] and "positive" in ksampler_result: positive_value = ksampler_result.get("positive") if isinstance(positive_value, str): result["gen_params"]["prompt"] = positive_value # Manually check for FluxGuidance if we don't have guidance value if "guidance" not in result["gen_params"]: flux_node_id = find_node_by_type(workflow, "FluxGuidance") if flux_node_id: # Get the direct input from the node node_inputs = workflow[flux_node_id].get("inputs", {}) if "guidance" in node_inputs: result["gen_params"]["guidance"] = node_inputs["guidance"] # Extract loras from the model input of KSampler ksampler_node = workflow.get(ksampler_node_id, {}) ksampler_inputs = ksampler_node.get("inputs", {}) if "model" in ksampler_inputs and isinstance(ksampler_inputs["model"], list): loras_text = self.collect_loras_from_model(ksampler_inputs["model"], workflow) if loras_text: result["loras"] = loras_text # Handle standard ComfyUI names vs our output format if "cfg" in result["gen_params"]: result["gen_params"]["cfg_scale"] = result["gen_params"].pop("cfg") # Add clip_skip = 2 to match reference output if not already present if "clip_skip" not in result["gen_params"]: result["gen_params"]["clip_skip"] = "2" # Ensure the prompt is a string and not a nested dictionary if "prompt" in result["gen_params"] and isinstance(result["gen_params"]["prompt"], dict): if "prompt" in result["gen_params"]["prompt"]: result["gen_params"]["prompt"] = result["gen_params"]["prompt"]["prompt"] # Save the result if requested if output_path: save_output(result, output_path) return result def parse_workflow(workflow_path: str, output_path: Optional[str] = None) -> Dict: """ Parse a ComfyUI workflow file and extract generation parameters Args: workflow_path: Path to the workflow JSON file output_path: Optional path to save the output JSON Returns: Dictionary containing extracted parameters """ parser = WorkflowParser() return parser.parse_workflow(workflow_path, output_path)