Update prompt configuration and enhance Lora management functionality

- 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.

py/workflow_params/simple_test.py

@@ -0,0 +1,63 @@
+#!/usr/bin/env python3
+"""Simple test script for the workflow parser"""
+
+import json
+import os
+import sys
+from pathlib import Path
+
+# Get project path
+project_path = Path(__file__).parent.parent.parent
+refs_path = project_path / "refs"
+prompt_path = refs_path / "prompt.json"
+output_path = refs_path / "output.json"
+
+print(f"Loading workflow from {prompt_path}")
+print(f"Expected output from {output_path}")
+
+# Load the workflow JSON
+with open(prompt_path, 'r', encoding='utf-8') as f:
+    workflow_json = json.load(f)
+
+# Load the expected output
+with open(output_path, 'r', encoding='utf-8') as f:
+    expected_output = json.load(f)
+
+print("\nExpected output:")
+print(json.dumps(expected_output, indent=2))
+
+# Manually extract important parameters to verify our understanding
+sampler_node_id = "3"
+sampler_node = workflow_json.get(sampler_node_id, {})
+print("\nSampler node:")
+print(json.dumps(sampler_node, indent=2))
+
+# Extract seed, steps, cfg
+seed = sampler_node.get("inputs", {}).get("seed")
+steps = sampler_node.get("inputs", {}).get("steps")
+cfg = sampler_node.get("inputs", {}).get("cfg")
+
+print(f"\nExtracted parameters:")
+print(f"seed: {seed}")
+print(f"steps: {steps}")
+print(f"cfg_scale: {cfg}")
+
+# Extract positive prompt - this requires following node references
+positive_ref = sampler_node.get("inputs", {}).get("positive", [])
+if isinstance(positive_ref, list) and len(positive_ref) == 2:
+    positive_node_id, slot_index = positive_ref
+    positive_node = workflow_json.get(positive_node_id, {})
+    
+    print(f"\nPositive node ({positive_node_id}):")
+    print(json.dumps(positive_node, indent=2))
+    
+    # Follow the reference to the text value
+    text_ref = positive_node.get("inputs", {}).get("text", [])
+    if isinstance(text_ref, list) and len(text_ref) == 2:
+        text_node_id, slot_index = text_ref
+        text_node = workflow_json.get(text_node_id, {})
+        
+        print(f"\nText node ({text_node_id}):")
+        print(json.dumps(text_node, indent=2))
+
+print("\nTest completed.")