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images_merger_vertical.py

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+import torch
+import numpy as np
+from PIL import Image
+
+class MergeImagesVertically:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image1": ("IMAGE",),
+                "image2": ("IMAGE",),
+            },
+            "optional": {
+                "image3": ("IMAGE",),
+                "image4": ("IMAGE",),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "combine_images"
+
+    CATEGORY = "Bjornulf"
+
+    def combine_images(self, image1, image2, image3=None, image4=None):
+        # Collect all provided images
+        images = [image1, image2]
+        if image3 is not None:
+            images.append(image3)
+        if image4 is not None:
+            images.append(image4)
+        
+        # Calculate the total width and maximum height
+        total_width = sum(img.shape[1] for img in images)
+        max_height = max(img.shape[2] for img in images)
+        
+        # Create a new tensor for the combined image
+        combined_image = torch.zeros((images[0].shape[0], total_width, max_height, 3), dtype=images[0].dtype, device=images[0].device)
+        
+        # Paste images side by side
+        current_x = 0
+        for img in images:
+            w, h = img.shape[1:3]
+            combined_image[:, current_x:current_x+w, :h, :] = img[:, :, :, :]
+            current_x += w
+        
+        return (combined_image,)