From 557edbae16975b718672c70a50f1d35e59fc4c01 Mon Sep 17 00:00:00 2001
From: kijai <40791699+kijai@users.noreply.github.com>
Date: Tue, 9 Dec 2025 23:49:26 +0200
Subject: [PATCH] SplineEditor: Return only first spline length when using
 multiple splines

---
 nodes/curve_nodes.py | 31 ++++++++++++++++++-------------
 1 file changed, 18 insertions(+), 13 deletions(-)

diff --git a/nodes/curve_nodes.py b/nodes/curve_nodes.py
index 053d8c6..ceea047 100644
--- a/nodes/curve_nodes.py
+++ b/nodes/curve_nodes.py
@@ -253,7 +253,7 @@ output types:
         # Handle nested list structure if present
         all_normalized = []
         all_normalized_y_values = []
-        
+
         # Check if we have a nested list structure
         if isinstance(coordinates, list) and len(coordinates) > 0 and isinstance(coordinates[0], list):
             # Process each list of coordinates in the nested structure
@@ -261,12 +261,14 @@ output types:
         else:
             # If not nested, treat as a single list of coordinates
             coordinate_sets = [coordinates]
-        
+
+        first_spline = coordinate_sets[0] if coordinate_sets else []
+
         # Process each set of coordinates
         for coord_set in coordinate_sets:
             normalized = []
             normalized_y_values = []
-            
+
             for coord in coord_set:
                 coord['x'] = int(round(coord['x']))
                 coord['y'] = int(round(coord['y']))
@@ -274,10 +276,10 @@ output types:
                 norm_y = (1.0 - (coord['y'] / mask_height) - 0.0) * (max_value - min_value) + min_value
                 normalized_y_values.append(norm_y)
                 normalized.append({'x':norm_x, 'y':norm_y})
-            
+
             all_normalized.extend(normalized)
             all_normalized_y_values.extend(normalized_y_values)
-        
+
         # Use the combined normalized values for output
         if float_output_type == 'list':
             out_floats = all_normalized_y_values * repeat_output
@@ -289,18 +291,21 @@ output types:
             out_floats = pd.Series(all_normalized_y_values * repeat_output),
         elif float_output_type == 'tensor':
             out_floats = torch.tensor(all_normalized_y_values * repeat_output, dtype=torch.float32)
-        
+
         # Create a color map for grayscale intensities
         color_map = lambda y: torch.full((mask_height, mask_width, 3), y, dtype=torch.float32)
 
-        # Create image tensors for each normalized y value
-        mask_tensors = [color_map(y) for y in all_normalized_y_values]
+        # Create a color map for grayscale intensities (from first spline only)
+        color_map = lambda y: torch.full((mask_height, mask_width, 3), y, dtype=torch.float32)
+        mask_tensors = [color_map(y) for y in normalized_y_values]
         masks_out = torch.stack(mask_tensors)
         masks_out = masks_out.repeat(repeat_output, 1, 1, 1)
         masks_out = masks_out.mean(dim=-1)
-        
+
+        single_spline_count = len(first_spline)
+
         if bg_image is None:
-            return (masks_out, json.dumps(coordinates if len(coordinates) > 1 else coordinates[0]), out_floats, len(out_floats), json.dumps(all_normalized))
+            return (masks_out, json.dumps(coordinates if len(coordinates) > 1 else coordinates[0]), out_floats, single_spline_count, json.dumps(all_normalized))
         else:
             transform = transforms.ToPILImage()
             image = transform(bg_image[0].permute(2, 0, 1))
@@ -310,12 +315,12 @@ output types:
             # Encode the image bytes to a Base64 string
             img_bytes = buffered.getvalue()
             img_base64 = base64.b64encode(img_bytes).decode('utf-8')
-            
+
             return {
                 "ui": {"bg_image": [img_base64]},
-                "result": (masks_out, json.dumps(coordinates if len(coordinates) > 1 else coordinates[0]), out_floats, len(out_floats), json.dumps(all_normalized))
+                "result": (masks_out, json.dumps(coordinates if len(coordinates) > 1 else coordinates[0]), out_floats, single_spline_count, json.dumps(all_normalized))
             }
-     
+
 
 class CreateShapeMaskOnPath: