0.51

images_merger_horizontal.py

@@ -0,0 +1,56 @@
+import torch
+import numpy as np
+from PIL import Image
+
+class MergeImagesHorizontally:
+    @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[2] for img in images)  # Sum of widths
+        max_height = max(img.shape[1] for img in images)   # Maximum height
+        
+        # Create a new tensor for the combined image
+        combined_image = torch.zeros((images[0].shape[0], max_height, total_width, 3), dtype=images[0].dtype, device=images[0].device)
+        
+        # Paste images side by side
+        current_x = 0
+        for img in images:
+            b, h, w, c = img.shape
+            combined_image[:, :h, current_x:current_x+w, :] = img
+            
+            # Blend the edge pixels if it's not the last image
+            # if current_x + w < total_width:
+            #     combined_image[:, :h, current_x+w-1:current_x+w+1, :] = torch.mean(
+            #         torch.stack([
+            #             combined_image[:, :h, current_x+w-1:current_x+w, :],
+            #             combined_image[:, :h, current_x+w:current_x+w+1, :]
+            #         ]), dim=0
+            #     )
+            
+            current_x += w
+        
+        return (combined_image,)