mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-03-23 22:22:11 -03:00
Remove deprecated workflow parameters and associated files
- Deleted the `__init__.py`, `cli.py`, `extension_manager.py`, `integration_example.py`, `README.md`, `simple_test.py`, `test_parser.py`, `verify_workflow.py`, and `workflow_parser.py` files as they are no longer needed. - Updated `.gitignore` to exclude new output files and test scripts. - Cleaned up the node processors directory by removing all processor implementations and their registration logic.
This commit is contained in:
Will Miao
3
py/workflow/__init__.py
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3
py/workflow/__init__.py
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@@ -0,0 +1,3 @@
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"""
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ComfyUI workflow parsing module to extract generation parameters
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"""
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58
py/workflow/cli.py
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58
py/workflow/cli.py
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@@ -0,0 +1,58 @@
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"""
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Command-line interface for the ComfyUI workflow parser
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"""
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import argparse
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import json
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import os
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import logging
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import sys
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from .parser import parse_workflow
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
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handlers=[logging.StreamHandler()]
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)
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logger = logging.getLogger(__name__)
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def main():
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"""Entry point for the CLI"""
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parser = argparse.ArgumentParser(description='Parse ComfyUI workflow files')
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parser.add_argument('input', help='Input workflow JSON file path')
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parser.add_argument('-o', '--output', help='Output JSON file path')
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parser.add_argument('-p', '--pretty', action='store_true', help='Pretty print JSON output')
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parser.add_argument('--debug', action='store_true', help='Enable debug logging')
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args = parser.parse_args()
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# Set logging level
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if args.debug:
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logging.getLogger().setLevel(logging.DEBUG)
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# Validate input file
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if not os.path.isfile(args.input):
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logger.error(f"Input file not found: {args.input}")
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sys.exit(1)
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# Parse workflow
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try:
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result = parse_workflow(args.input, args.output)
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# Print result to console if output file not specified
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if not args.output:
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if args.pretty:
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print(json.dumps(result, indent=4))
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else:
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print(json.dumps(result))
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else:
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logger.info(f"Output saved to: {args.output}")
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except Exception as e:
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logger.error(f"Error parsing workflow: {e}")
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if args.debug:
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import traceback
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traceback.print_exc()
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sys.exit(1)
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if __name__ == "__main__":
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main()
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37
py/workflow/main.py
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37
py/workflow/main.py
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@@ -0,0 +1,37 @@
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"""
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Main entry point for the workflow parser module
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"""
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import os
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import sys
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import logging
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from typing import Dict, Optional, Union
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# Add the parent directory to sys.path to enable imports
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SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
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ROOT_DIR = os.path.abspath(os.path.join(SCRIPT_DIR, '..', '..'))
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sys.path.insert(0, os.path.dirname(SCRIPT_DIR))
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from .parser import parse_workflow
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logger = logging.getLogger(__name__)
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def parse_comfyui_workflow(
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workflow_path: str,
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output_path: Optional[str] = None
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) -> Dict:
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"""
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Parse a ComfyUI workflow file and extract generation parameters
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Args:
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workflow_path: Path to the workflow JSON file
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output_path: Optional path to save the output JSON
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Returns:
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Dictionary containing extracted parameters
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"""
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return parse_workflow(workflow_path, output_path)
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if __name__ == "__main__":
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# If run directly, use the CLI
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from .cli import main
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main()
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255
py/workflow/mappers.py
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255
py/workflow/mappers.py
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@@ -0,0 +1,255 @@
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"""
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Node mappers for ComfyUI workflow parsing
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"""
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import logging
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from typing import Dict, List, Any, Optional, Union
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logger = logging.getLogger(__name__)
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class NodeMapper:
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"""Base class for node mappers that define how to extract information from a specific node type"""
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def __init__(self, node_type: str, inputs_to_track: List[str]):
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self.node_type = node_type
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self.inputs_to_track = inputs_to_track
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def process(self, node_id: str, node_data: Dict, workflow: Dict, parser: 'WorkflowParser') -> Any: # type: ignore
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"""Process the node and extract relevant information"""
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result = {}
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for input_name in self.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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# Format is [node_id, output_slot]
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ref_node_id, output_slot = input_value
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# Recursively process the referenced node
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ref_value = parser.process_node(str(ref_node_id), workflow)
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result[input_name] = ref_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 any transformations
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return self.transform(result)
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def transform(self, inputs: Dict) -> Any:
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"""Transform the extracted inputs - override in subclasses"""
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return inputs
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class KSamplerMapper(NodeMapper):
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"""Mapper for KSampler nodes"""
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def __init__(self):
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super().__init__(
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node_type="KSampler",
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inputs_to_track=["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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def transform(self, inputs: Dict) -> Dict:
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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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return result
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class EmptyLatentImageMapper(NodeMapper):
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"""Mapper for EmptyLatentImage nodes"""
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def __init__(self):
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super().__init__(
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node_type="EmptyLatentImage",
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inputs_to_track=["width", "height", "batch_size"]
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)
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def transform(self, inputs: Dict) -> Dict:
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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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class EmptySD3LatentImageMapper(NodeMapper):
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"""Mapper for EmptySD3LatentImage nodes"""
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def __init__(self):
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super().__init__(
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node_type="EmptySD3LatentImage",
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inputs_to_track=["width", "height", "batch_size"]
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)
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def transform(self, inputs: Dict) -> Dict:
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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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class CLIPTextEncodeMapper(NodeMapper):
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"""Mapper for CLIPTextEncode nodes"""
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def __init__(self):
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super().__init__(
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node_type="CLIPTextEncode",
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inputs_to_track=["text", "clip"]
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)
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def transform(self, inputs: Dict) -> Any:
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# Simply return the text
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return inputs.get("text", "")
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class LoraLoaderMapper(NodeMapper):
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"""Mapper for LoraLoader nodes"""
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def __init__(self):
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super().__init__(
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node_type="Lora Loader (LoraManager)",
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inputs_to_track=["text", "loras", "lora_stack"]
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)
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def transform(self, inputs: Dict) -> Dict:
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lora_text = inputs.get("text", "")
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lora_stack = inputs.get("lora_stack", [])
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# Process lora_stack if it exists
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stack_text = ""
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if lora_stack:
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# Handle the formatted lora_stack info if available
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stack_loras = []
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for lora_path, strength, _ in lora_stack:
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lora_name = lora_path.split(os.sep)[-1].split('.')[0]
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stack_loras.append(f"<lora:{lora_name}:{strength}>")
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stack_text = " ".join(stack_loras)
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# Combine lora_text and stack_text
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combined_text = lora_text
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if stack_text:
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combined_text = f"{combined_text} {stack_text}" if combined_text else stack_text
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# Format loras with spaces between them
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if combined_text:
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# Replace consecutive closing and opening tags with a space
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combined_text = combined_text.replace("><", "> <")
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return {"loras": combined_text}
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class LoraStackerMapper(NodeMapper):
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"""Mapper for LoraStacker nodes"""
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def __init__(self):
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super().__init__(
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node_type="Lora Stacker (LoraManager)",
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inputs_to_track=["loras", "lora_stack"]
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)
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def transform(self, inputs: Dict) -> Dict:
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# Return the lora_stack information
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return inputs.get("lora_stack", [])
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class JoinStringsMapper(NodeMapper):
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"""Mapper for JoinStrings nodes"""
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def __init__(self):
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super().__init__(
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node_type="JoinStrings",
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inputs_to_track=["string1", "string2", "delimiter"]
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)
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def transform(self, inputs: Dict) -> str:
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string1 = inputs.get("string1", "")
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string2 = inputs.get("string2", "")
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delimiter = inputs.get("delimiter", "")
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return f"{string1}{delimiter}{string2}"
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class StringConstantMapper(NodeMapper):
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"""Mapper for StringConstant and StringConstantMultiline nodes"""
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def __init__(self):
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super().__init__(
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node_type="StringConstantMultiline",
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inputs_to_track=["string"]
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)
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def transform(self, inputs: Dict) -> str:
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return inputs.get("string", "")
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class TriggerWordToggleMapper(NodeMapper):
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"""Mapper for TriggerWordToggle nodes"""
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def __init__(self):
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super().__init__(
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node_type="TriggerWord Toggle (LoraManager)",
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inputs_to_track=["toggle_trigger_words", "orinalMessage", "trigger_words"]
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)
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def transform(self, inputs: Dict) -> str:
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# Get the original message or toggled trigger words
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original_message = inputs.get("orinalMessage", "") or inputs.get("trigger_words", "")
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# Fix double commas to match the reference output format
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if original_message:
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# Replace double commas with single commas
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original_message = original_message.replace(",, ", ", ")
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return original_message
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class FluxGuidanceMapper(NodeMapper):
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"""Mapper for FluxGuidance nodes"""
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def __init__(self):
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super().__init__(
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node_type="FluxGuidance",
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inputs_to_track=["guidance", "conditioning"]
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)
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def transform(self, inputs: Dict) -> Dict:
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result = {}
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# Handle guidance parameter
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if "guidance" in inputs:
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result["guidance"] = inputs["guidance"]
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# Handle conditioning (the prompt text)
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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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# Add import os for LoraLoaderMapper to work properly
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import os
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185
py/workflow/parser.py
Normal file
185
py/workflow/parser.py
Normal file
@@ -0,0 +1,185 @@
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"""
|
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Main workflow parser implementation for ComfyUI
|
||||
"""
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import json
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import logging
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from typing import Dict, List, Any, Optional, Union, Set
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from .mappers import (
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NodeMapper, KSamplerMapper, EmptyLatentImageMapper,
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EmptySD3LatentImageMapper, CLIPTextEncodeMapper,
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LoraLoaderMapper, LoraStackerMapper, JoinStringsMapper,
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StringConstantMapper, TriggerWordToggleMapper, FluxGuidanceMapper
|
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)
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from .utils import (
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load_workflow, save_output, find_node_by_type,
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trace_model_path
|
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)
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|
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logger = logging.getLogger(__name__)
|
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class WorkflowParser:
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"""Parser for ComfyUI workflows"""
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|
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def __init__(self):
|
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"""Initialize the parser with default node mappers"""
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self.node_mappers: Dict[str, NodeMapper] = {}
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self.processed_nodes: Set[str] = set() # Track processed nodes to avoid cycles
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self.register_default_mappers()
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def register_default_mappers(self) -> None:
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"""Register all default node mappers"""
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mappers = [
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KSamplerMapper(),
|
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EmptyLatentImageMapper(),
|
||||
EmptySD3LatentImageMapper(),
|
||||
CLIPTextEncodeMapper(),
|
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LoraLoaderMapper(),
|
||||
LoraStackerMapper(),
|
||||
JoinStringsMapper(),
|
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StringConstantMapper(),
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||||
TriggerWordToggleMapper(),
|
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FluxGuidanceMapper()
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]
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for mapper in mappers:
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self.register_mapper(mapper)
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def register_mapper(self, mapper: NodeMapper) -> None:
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"""Register a node mapper"""
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self.node_mappers[mapper.node_type] = mapper
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def process_node(self, node_id: str, workflow: Dict) -> Any:
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"""Process a single node and extract relevant information"""
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# Check if we've already processed this node to avoid cycles
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if node_id in self.processed_nodes:
|
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return None
|
||||
|
||||
# Mark this node as processed
|
||||
self.processed_nodes.add(node_id)
|
||||
|
||||
if node_id not in workflow:
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return None
|
||||
|
||||
node_data = workflow[node_id]
|
||||
node_type = node_data.get("class_type")
|
||||
|
||||
result = None
|
||||
if node_type in self.node_mappers:
|
||||
mapper = self.node_mappers[node_type]
|
||||
result = mapper.process(node_id, node_data, workflow, self)
|
||||
|
||||
# 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 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
|
||||
self.processed_nodes = set()
|
||||
|
||||
# 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"]
|
||||
|
||||
# Trace the model path to find LoRA Loader nodes
|
||||
lora_node_ids = trace_model_path(workflow, ksampler_node_id)
|
||||
|
||||
# Process each LoRA Loader node
|
||||
lora_texts = []
|
||||
for lora_node_id in lora_node_ids:
|
||||
# Reset the processed nodes tracker for each lora processing
|
||||
self.processed_nodes = set()
|
||||
|
||||
lora_result = self.process_node(lora_node_id, workflow)
|
||||
if lora_result and "loras" in lora_result:
|
||||
lora_texts.append(lora_result["loras"])
|
||||
|
||||
# Combine all LoRA texts
|
||||
if lora_texts:
|
||||
result["loras"] = " ".join(lora_texts)
|
||||
|
||||
# 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)
|
||||
63
py/workflow/test.py
Normal file
63
py/workflow/test.py
Normal file
@@ -0,0 +1,63 @@
|
||||
"""
|
||||
Test script for the ComfyUI workflow parser
|
||||
"""
|
||||
import os
|
||||
import json
|
||||
import logging
|
||||
from .parser import parse_workflow
|
||||
|
||||
logging.basicConfig(
|
||||
level=logging.INFO,
|
||||
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
|
||||
handlers=[logging.StreamHandler()]
|
||||
)
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Configure paths
|
||||
SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
|
||||
ROOT_DIR = os.path.abspath(os.path.join(SCRIPT_DIR, '..', '..'))
|
||||
REFS_DIR = os.path.join(ROOT_DIR, 'refs')
|
||||
OUTPUT_DIR = os.path.join(ROOT_DIR, 'output')
|
||||
|
||||
def test_parse_flux_workflow():
|
||||
"""Test parsing the flux example workflow"""
|
||||
# Ensure output directory exists
|
||||
os.makedirs(OUTPUT_DIR, exist_ok=True)
|
||||
|
||||
# Define input and output paths
|
||||
input_path = os.path.join(REFS_DIR, 'flux_prompt.json')
|
||||
output_path = os.path.join(OUTPUT_DIR, 'parsed_flux_output.json')
|
||||
|
||||
# Parse workflow
|
||||
logger.info(f"Parsing workflow: {input_path}")
|
||||
result = parse_workflow(input_path, output_path)
|
||||
|
||||
# Print result summary
|
||||
logger.info(f"Output saved to: {output_path}")
|
||||
logger.info(f"Parsing completed. Result summary:")
|
||||
logger.info(f" LoRAs: {result.get('loras', '')}")
|
||||
|
||||
gen_params = result.get('gen_params', {})
|
||||
logger.info(f" Prompt: {gen_params.get('prompt', '')[:50]}...")
|
||||
logger.info(f" Steps: {gen_params.get('steps', '')}")
|
||||
logger.info(f" Sampler: {gen_params.get('sampler', '')}")
|
||||
logger.info(f" Size: {gen_params.get('size', '')}")
|
||||
|
||||
# Compare with reference output
|
||||
ref_output_path = os.path.join(REFS_DIR, 'flux_output.json')
|
||||
try:
|
||||
with open(ref_output_path, 'r') as f:
|
||||
ref_output = json.load(f)
|
||||
|
||||
# Simple validation
|
||||
loras_match = result.get('loras', '') == ref_output.get('loras', '')
|
||||
prompt_match = gen_params.get('prompt', '') == ref_output.get('gen_params', {}).get('prompt', '')
|
||||
|
||||
logger.info(f"Validation against reference:")
|
||||
logger.info(f" LoRAs match: {loras_match}")
|
||||
logger.info(f" Prompt match: {prompt_match}")
|
||||
except Exception as e:
|
||||
logger.warning(f"Failed to compare with reference output: {e}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
test_parse_flux_workflow()
|
||||
102
py/workflow/utils.py
Normal file
102
py/workflow/utils.py
Normal file
@@ -0,0 +1,102 @@
|
||||
"""
|
||||
Utility functions for ComfyUI workflow parsing
|
||||
"""
|
||||
import json
|
||||
import os
|
||||
import logging
|
||||
from typing import Dict, List, Any, Optional, Union, Set, Tuple
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
def load_workflow(workflow_path: str) -> Dict:
|
||||
"""Load a workflow from a JSON file"""
|
||||
try:
|
||||
with open(workflow_path, 'r', encoding='utf-8') as f:
|
||||
return json.load(f)
|
||||
except Exception as e:
|
||||
logger.error(f"Error loading workflow from {workflow_path}: {e}")
|
||||
raise
|
||||
|
||||
def save_output(output: Dict, output_path: str) -> None:
|
||||
"""Save the parsed output to a JSON file"""
|
||||
os.makedirs(os.path.dirname(os.path.abspath(output_path)), exist_ok=True)
|
||||
try:
|
||||
with open(output_path, 'w', encoding='utf-8') as f:
|
||||
json.dump(output, f, indent=4)
|
||||
except Exception as e:
|
||||
logger.error(f"Error saving output to {output_path}: {e}")
|
||||
raise
|
||||
|
||||
def find_node_by_type(workflow: Dict, node_type: str) -> Optional[str]:
|
||||
"""Find a node of the specified type in the workflow"""
|
||||
for node_id, node_data in workflow.items():
|
||||
if node_data.get("class_type") == node_type:
|
||||
return node_id
|
||||
return None
|
||||
|
||||
def find_nodes_by_type(workflow: Dict, node_type: str) -> List[str]:
|
||||
"""Find all nodes of the specified type in the workflow"""
|
||||
return [node_id for node_id, node_data in workflow.items()
|
||||
if node_data.get("class_type") == node_type]
|
||||
|
||||
def get_input_node_ids(workflow: Dict, node_id: str) -> Dict[str, Tuple[str, int]]:
|
||||
"""
|
||||
Get the node IDs for all inputs of the given node
|
||||
|
||||
Returns a dictionary mapping input names to (node_id, output_slot) tuples
|
||||
"""
|
||||
result = {}
|
||||
if node_id not in workflow:
|
||||
return result
|
||||
|
||||
node_data = workflow[node_id]
|
||||
for input_name, input_value in node_data.get("inputs", {}).items():
|
||||
# Check if this input is connected to another node
|
||||
if isinstance(input_value, list) and len(input_value) == 2:
|
||||
# Input is connected to another node's output
|
||||
# Format: [node_id, output_slot]
|
||||
ref_node_id, output_slot = input_value
|
||||
result[input_name] = (str(ref_node_id), output_slot)
|
||||
|
||||
return result
|
||||
|
||||
def trace_model_path(workflow: Dict, start_node_id: str,
|
||||
visited: Optional[Set[str]] = None) -> List[str]:
|
||||
"""
|
||||
Trace through the workflow graph following 'model' inputs
|
||||
to find all LoRA Loader nodes that affect the model
|
||||
|
||||
Returns a list of LoRA Loader node IDs
|
||||
"""
|
||||
if visited is None:
|
||||
visited = set()
|
||||
|
||||
# Prevent cycles
|
||||
if start_node_id in visited:
|
||||
return []
|
||||
|
||||
visited.add(start_node_id)
|
||||
|
||||
node_data = workflow.get(start_node_id)
|
||||
if not node_data:
|
||||
return []
|
||||
|
||||
# If this is a LoRA Loader node, add it to the result
|
||||
if node_data.get("class_type") == "Lora Loader (LoraManager)":
|
||||
return [start_node_id]
|
||||
|
||||
# Get all input nodes
|
||||
input_nodes = get_input_node_ids(workflow, start_node_id)
|
||||
|
||||
# Recursively trace the model input if it exists
|
||||
result = []
|
||||
if "model" in input_nodes:
|
||||
model_node_id, _ = input_nodes["model"]
|
||||
result.extend(trace_model_path(workflow, model_node_id, visited))
|
||||
|
||||
# Also trace lora_stack input if it exists
|
||||
if "lora_stack" in input_nodes:
|
||||
lora_stack_node_id, _ = input_nodes["lora_stack"]
|
||||
result.extend(trace_model_path(workflow, lora_stack_node_id, visited))
|
||||
|
||||
return result
|
||||
Reference in New Issue
Block a user