Fix: Auto-detect mode based on input priority

Changes:
- Priority 1: If folder_path is valid → use folder random selection
- Priority 2: If image_data is available → use file picker image
- Works regardless of folder_mode setting

Benefits:
- folder_path can be maintained while using the node
- No need to manually toggle folder_mode
- Auto-detects best mode based on available input
- Better error messages explaining what's needed

This allows seamless workflow:
- Keep folder path in field
- Node automatically picks random from folder
- Can still use file picker if folder_path is empty

random_image_picker.py

@@ -131,23 +131,18 @@ class RandomImagePicker:
         Load image based on mode settings.
         
         Args:
-            folder_path: Folder path for random selection (folder mode)
-            folder_mode: True for folder mode, False for single file mode
+            folder_path: Folder path for random selection
+            folder_mode: True for explicit folder mode, False for auto-detect
             include_subfolders: Whether to scan subfolders
             seed: Random seed for folder mode
-            image_data: Base64 encoded image data from file picker (single mode)
+            image_data: Base64 encoded image data from file picker
             
         Returns:
             Tuple of (image_tensor, width, height)
         """
-        if folder_mode:
-            # Folder mode: pick random image from folder
-            if not folder_path or not os.path.exists(folder_path):
-                raise ValueError(f"Folder path does not exist: {folder_path}")
-            
-            if not os.path.isdir(folder_path):
-                raise ValueError(f"Folder mode requires a directory path: {folder_path}")
-            
+        # Priority 1: Check if folder_path is valid (works for both modes)
+        if folder_path and os.path.exists(folder_path) and os.path.isdir(folder_path):
+            # Use folder mode (random selection)
             image_files = self.get_image_files(folder_path, include_subfolders)
             
             if not image_files:
@@ -157,15 +152,14 @@ class RandomImagePicker:
             random.seed(seed)
             selected_image = random.choice(image_files)
             
+            mode_str = "Folder Mode" if folder_mode else "Auto (from folder_path)"
+            print(f"[Random Image Picker] Mode: {mode_str}")
             print(f"[Random Image Picker] Folder: {folder_path}")
             print(f"[Random Image Picker] Selected: {selected_image}")
             return self.load_image_file(selected_image)
-        else:
-            # Single file mode: use image_data from file picker
-            if not image_data:
-                raise ValueError("Please select an image file using the 📁 Choose File button")
-            
-            # Parse base64 image data
+        
+        # Priority 2: Use file picker data if available
+        if image_data:
             import base64
             import io
             
@@ -191,11 +185,20 @@ class RandomImagePicker:
                 # Convert to torch tensor and add batch dimension
                 img_tensor = torch.from_numpy(img_array)[None,]
                 
-                print(f"[Random Image Picker] Loaded from file picker: {width}x{height}")
+                print(f"[Random Image Picker] Mode: File Picker")
+                print(f"[Random Image Picker] Loaded: {width}x{height}")
                 return (img_tensor, width, height)
                 
             except Exception as e:
                 raise ValueError(f"Failed to load image from file picker: {str(e)}")
+        
+        # No valid input provided
+        raise ValueError(
+            "No valid input provided.\n\n"
+            "Please either:\n"
+            "1. Enter a folder path in 'folder_path' field, OR\n"
+            "2. Use the 📁 Choose File button to select an image"
+        )
 
 
 # Node registration