mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-03-22 05:32:12 -03:00
4bff17aa1a
- Expanded the prompt.json file with new configurations for KSampler, CheckpointLoaderSimple, and various CLIPTextEncode nodes. - Introduced additional Lora management features, including a new Lora Stacker and improved trigger word handling. - Enhanced the loras_widget.js to log the generated prompt when saving recipes directly, aiding in debugging and user feedback. - Improved overall structure and organization of the prompt configurations for better maintainability.
106 lines
4.3 KiB
Python
106 lines
4.3 KiB
Python
#!/usr/bin/env python3
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"""Script to verify the workflow structure and save the output to a file"""
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import json
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import os
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from pathlib import Path
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# Get project path
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project_path = Path(__file__).parent.parent.parent
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refs_path = project_path / "refs"
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prompt_path = refs_path / "prompt.json"
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output_path = refs_path / "output.json"
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test_output_path = refs_path / "test_output.txt"
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# Load the workflow JSON
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with open(prompt_path, 'r', encoding='utf-8') as f:
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workflow_json = json.load(f)
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# Load the expected output
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with open(output_path, 'r', encoding='utf-8') as f:
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expected_output = json.load(f)
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# Open the output file
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with open(test_output_path, 'w', encoding='utf-8') as f:
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f.write(f"Loading workflow from {prompt_path}\n")
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f.write(f"Expected output from {output_path}\n\n")
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f.write("Expected output:\n")
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f.write(json.dumps(expected_output, indent=2) + "\n\n")
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# Manually extract important parameters
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sampler_node_id = "3"
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sampler_node = workflow_json.get(sampler_node_id, {})
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f.write("Sampler node:\n")
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f.write(json.dumps(sampler_node, indent=2) + "\n\n")
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# Extract seed, steps, cfg
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seed = sampler_node.get("inputs", {}).get("seed")
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steps = sampler_node.get("inputs", {}).get("steps")
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cfg = sampler_node.get("inputs", {}).get("cfg")
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f.write(f"Extracted parameters:\n")
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f.write(f"seed: {seed}\n")
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f.write(f"steps: {steps}\n")
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f.write(f"cfg_scale: {cfg}\n\n")
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# Extract positive prompt - this requires following node references
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positive_ref = sampler_node.get("inputs", {}).get("positive", [])
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if isinstance(positive_ref, list) and len(positive_ref) == 2:
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positive_node_id, slot_index = positive_ref
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positive_node = workflow_json.get(positive_node_id, {})
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f.write(f"Positive node ({positive_node_id}):\n")
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f.write(json.dumps(positive_node, indent=2) + "\n\n")
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# Follow the reference to the text value
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text_ref = positive_node.get("inputs", {}).get("text", [])
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if isinstance(text_ref, list) and len(text_ref) == 2:
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text_node_id, slot_index = text_ref
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text_node = workflow_json.get(text_node_id, {})
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f.write(f"Text node ({text_node_id}):\n")
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f.write(json.dumps(text_node, indent=2) + "\n\n")
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# If the text node is a JoinStrings node, follow its inputs
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if text_node.get("class_type") == "JoinStrings":
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string1_ref = text_node.get("inputs", {}).get("string1", [])
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string2_ref = text_node.get("inputs", {}).get("string2", [])
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if isinstance(string1_ref, list) and len(string1_ref) == 2:
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string1_node_id, slot_index = string1_ref
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string1_node = workflow_json.get(string1_node_id, {})
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f.write(f"String1 node ({string1_node_id}):\n")
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f.write(json.dumps(string1_node, indent=2) + "\n\n")
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if isinstance(string2_ref, list) and len(string2_ref) == 2:
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string2_node_id, slot_index = string2_ref
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string2_node = workflow_json.get(string2_node_id, {})
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f.write(f"String2 node ({string2_node_id}):\n")
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f.write(json.dumps(string2_node, indent=2) + "\n\n")
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# Extract negative prompt
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negative_ref = sampler_node.get("inputs", {}).get("negative", [])
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if isinstance(negative_ref, list) and len(negative_ref) == 2:
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negative_node_id, slot_index = negative_ref
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negative_node = workflow_json.get(negative_node_id, {})
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f.write(f"Negative node ({negative_node_id}):\n")
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f.write(json.dumps(negative_node, indent=2) + "\n\n")
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# Extract LoRA information
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lora_nodes = []
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for node_id, node_data in workflow_json.items():
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if node_data.get("class_type") in ["Lora Loader (LoraManager)", "Lora Stacker (LoraManager)"]:
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lora_nodes.append((node_id, node_data))
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f.write(f"LoRA nodes ({len(lora_nodes)}):\n")
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for node_id, node_data in lora_nodes:
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f.write(f"\nLoRA node {node_id}:\n")
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f.write(json.dumps(node_data, indent=2) + "\n")
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f.write("\nTest completed.\n")
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print(f"Test output written to {test_output_path}") |