Add simple tile nodes

__init__.py

@@ -0,0 +1,3 @@
+from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
+
+__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']

nodes.py

@@ -0,0 +1,106 @@
+import sys
+import os
+
+import torch
+
+sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy"))
+
+
+IMAGE_SIZE = 1472
+TILE_SIZE = 768
+
+# Splits an image in four tiles and returns them as a list
+class TileSplit:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":{
+            "image": ("IMAGE", ),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "split"
+    CATEGORY = "utils"
+
+    def split(self, image):
+
+        height, width = IMAGE_SIZE, IMAGE_SIZE
+
+        tile_height, tile_width = TILE_SIZE, TILE_SIZE
+        overlap = 64
+
+        tiles = []
+        for y in range(0, height-tile_height+1, tile_height-overlap):
+            for x in range(0, width-tile_width+1, tile_width-overlap):
+                tile = image[:, y:y+tile_height, x:x+tile_width, :]
+                tiles.append(tile)
+
+        # Convert tiles list to a tensor if needed
+        tiles_tensor = torch.stack(tiles).squeeze(1)
+
+
+
+        return  [tiles_tensor]
+
+class TileMerge:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":{
+            "images": ("IMAGE", ),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "blend_tiles"
+    CATEGORY = "utils"
+    def blend_tiles(self, images):
+        tiles = images
+        overlap = 64
+        tile_height = TILE_SIZE
+        tile_width = TILE_SIZE
+        # original_shape  (1, 3072, 2048, 3)
+        original_shape = (1, IMAGE_SIZE, IMAGE_SIZE, 3)
+
+
+
+        batch, height, width, channels = original_shape
+        output = torch.zeros(original_shape, dtype=tiles.dtype)
+        count = torch.zeros(original_shape, dtype=tiles.dtype)
+        
+        idx = 0
+        for y in range(0, height-tile_height+1, tile_height-overlap):
+            for x in range(0, width-tile_width+1, tile_width-overlap):
+                tile = tiles[idx]
+                
+                # Create a weight matrix for blending
+                weight_matrix = torch.ones((tile_height, tile_width, channels))
+                for i in range(overlap):
+                    weight = float(i) / overlap
+                    weight_matrix[i, :, :] *= weight  # Top rows
+                    weight_matrix[-(i + 1), :, :] *= weight  # Bottom rows
+                    weight_matrix[:, i, :] *= weight  # Left columns
+                    weight_matrix[:, -(i + 1), :] *= weight  # Right columns
+                
+                # Update the output and count tensors
+                output[:, y:y+tile_height, x:x+tile_width, :] += tile * weight_matrix
+                count[:, y:y+tile_height, x:x+tile_width, :] += weight_matrix
+                
+                idx += 1
+        
+        # Blend the output
+        output /= count
+        
+        return [output]
+
+
+
+
+NODE_CLASS_MAPPINGS = {
+    "TileSplit": TileSplit,
+    "TileMerge": TileMerge,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "TileSplit": "TileSplit",
+    "TileMerge": "TileMerge",
+}