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/verify_workflow.py

@@ -0,0 +1,106 @@
+#!/usr/bin/env python3
+"""Script to verify the workflow structure and save the output to a file"""
+
+import json
+import os
+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"
+test_output_path = refs_path / "test_output.txt"
+
+# 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)
+
+# Open the output file
+with open(test_output_path, 'w', encoding='utf-8') as f:
+    f.write(f"Loading workflow from {prompt_path}\n")
+    f.write(f"Expected output from {output_path}\n\n")
+    
+    f.write("Expected output:\n")
+    f.write(json.dumps(expected_output, indent=2) + "\n\n")
+    
+    # Manually extract important parameters
+    sampler_node_id = "3"
+    sampler_node = workflow_json.get(sampler_node_id, {})
+    f.write("Sampler node:\n")
+    f.write(json.dumps(sampler_node, indent=2) + "\n\n")
+    
+    # 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")
+    
+    f.write(f"Extracted parameters:\n")
+    f.write(f"seed: {seed}\n")
+    f.write(f"steps: {steps}\n")
+    f.write(f"cfg_scale: {cfg}\n\n")
+    
+    # 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, {})
+        
+        f.write(f"Positive node ({positive_node_id}):\n")
+        f.write(json.dumps(positive_node, indent=2) + "\n\n")
+        
+        # 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, {})
+            
+            f.write(f"Text node ({text_node_id}):\n")
+            f.write(json.dumps(text_node, indent=2) + "\n\n")
+            
+            # If the text node is a JoinStrings node, follow its inputs
+            if text_node.get("class_type") == "JoinStrings":
+                string1_ref = text_node.get("inputs", {}).get("string1", [])
+                string2_ref = text_node.get("inputs", {}).get("string2", [])
+                
+                if isinstance(string1_ref, list) and len(string1_ref) == 2:
+                    string1_node_id, slot_index = string1_ref
+                    string1_node = workflow_json.get(string1_node_id, {})
+                    
+                    f.write(f"String1 node ({string1_node_id}):\n")
+                    f.write(json.dumps(string1_node, indent=2) + "\n\n")
+                
+                if isinstance(string2_ref, list) and len(string2_ref) == 2:
+                    string2_node_id, slot_index = string2_ref
+                    string2_node = workflow_json.get(string2_node_id, {})
+                    
+                    f.write(f"String2 node ({string2_node_id}):\n")
+                    f.write(json.dumps(string2_node, indent=2) + "\n\n")
+    
+    # Extract negative prompt
+    negative_ref = sampler_node.get("inputs", {}).get("negative", [])
+    if isinstance(negative_ref, list) and len(negative_ref) == 2:
+        negative_node_id, slot_index = negative_ref
+        negative_node = workflow_json.get(negative_node_id, {})
+        
+        f.write(f"Negative node ({negative_node_id}):\n")
+        f.write(json.dumps(negative_node, indent=2) + "\n\n")
+    
+    # Extract LoRA information
+    lora_nodes = []
+    for node_id, node_data in workflow_json.items():
+        if node_data.get("class_type") in ["Lora Loader (LoraManager)", "Lora Stacker (LoraManager)"]:
+            lora_nodes.append((node_id, node_data))
+    
+    f.write(f"LoRA nodes ({len(lora_nodes)}):\n")
+    for node_id, node_data in lora_nodes:
+        f.write(f"\nLoRA node {node_id}:\n")
+        f.write(json.dumps(node_data, indent=2) + "\n")
+    
+    f.write("\nTest completed.\n")
+
+print(f"Test output written to {test_output_path}")