Allow padding in ImageStitch node to be white. (Comfy-Org#8631)

Author: comfyanonymous

comfy_extras/nodes_images.py

@@ -304,10 +304,23 @@ def stitch(
                 image2.movedim(-1, 1), target_w, target_h, "lanczos", "disabled"
             ).movedim(1, -1)
 
+        color_map = {
+            "white": 1.0,
+            "black": 0.0,
+            "red": (1.0, 0.0, 0.0),
+            "green": (0.0, 1.0, 0.0),
+            "blue": (0.0, 0.0, 1.0),
+        }
+
+        color_val = color_map[spacing_color]
+
         # When not matching sizes, pad to align non-concat dimensions
         if not match_image_size:
             h1, w1 = image1.shape[1:3]
             h2, w2 = image2.shape[1:3]
+            pad_value = 0.0
+            if not isinstance(color_val, tuple):
+                pad_value = color_val
 
             if direction in ["left", "right"]:
                 # For horizontal concat, pad heights to match
@@ -316,23 +329,23 @@ def stitch(
                     if h1 < target_h:
                         pad_h = target_h - h1
                         pad_top, pad_bottom = pad_h // 2, pad_h - pad_h // 2
-                        image1 = torch.nn.functional.pad(image1, (0, 0, 0, 0, pad_top, pad_bottom), mode='constant', value=0.0)
+                        image1 = torch.nn.functional.pad(image1, (0, 0, 0, 0, pad_top, pad_bottom), mode='constant', value=pad_value)
                     if h2 < target_h:
                         pad_h = target_h - h2
                         pad_top, pad_bottom = pad_h // 2, pad_h - pad_h // 2
-                        image2 = torch.nn.functional.pad(image2, (0, 0, 0, 0, pad_top, pad_bottom), mode='constant', value=0.0)
+                        image2 = torch.nn.functional.pad(image2, (0, 0, 0, 0, pad_top, pad_bottom), mode='constant', value=pad_value)
             else:  # up, down
                 # For vertical concat, pad widths to match
                 if w1 != w2:
                     target_w = max(w1, w2)
                     if w1 < target_w:
                         pad_w = target_w - w1
                         pad_left, pad_right = pad_w // 2, pad_w - pad_w // 2
-                        image1 = torch.nn.functional.pad(image1, (0, 0, pad_left, pad_right), mode='constant', value=0.0)
+                        image1 = torch.nn.functional.pad(image1, (0, 0, pad_left, pad_right), mode='constant', value=pad_value)
                     if w2 < target_w:
                         pad_w = target_w - w2
                         pad_left, pad_right = pad_w // 2, pad_w - pad_w // 2
-                        image2 = torch.nn.functional.pad(image2, (0, 0, pad_left, pad_right), mode='constant', value=0.0)
+                        image2 = torch.nn.functional.pad(image2, (0, 0, pad_left, pad_right), mode='constant', value=pad_value)
 
         # Ensure same number of channels
         if image1.shape[-1] != image2.shape[-1]:
@@ -366,15 +379,6 @@ def stitch(
         if spacing_width > 0:
             spacing_width = spacing_width + (spacing_width % 2)  # Ensure even
 
-            color_map = {
-                "white": 1.0,
-                "black": 0.0,
-                "red": (1.0, 0.0, 0.0),
-                "green": (0.0, 1.0, 0.0),
-                "blue": (0.0, 0.0, 1.0),
-            }
-            color_val = color_map[spacing_color]
-
             if direction in ["left", "right"]:
                 spacing_shape = (
                     image1.shape[0],