v1.1.4 cut and shift

image_cut_and_shift.py

@@ -0,0 +1,69 @@
+import torch
+import numpy as np
+from PIL import Image
+
+class HorizontalCutAndShift:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),                  # Input image tensor
+                "X": ("INT", {"default": 0, "min": 0, "max": 4096}),  # Cut position
+                "Y": ("INT", {"default": 0, "min": 0, "max": 4096}),  # Upward shift for bottom
+                "Z": ("INT", {"default": 0, "min": 0, "max": 4096}),  # Downward shift for top
+                "fill_color": (["black", "white"],),  # New option for fill color
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)  # Output is an image tensor
+    FUNCTION = "process"       # Processing function name
+    CATEGORY = "image"         # Node category in ComfyUI
+
+    def process(self, image, X, Y, Z, fill_color):
+        # Get image dimensions: batch size, height, width, channels
+        batch, H, W, channels = image.shape
+
+        # Initialize output tensor based on the selected fill color
+        if fill_color == "black":
+            output = torch.zeros_like(image)  # Zeros for black
+        elif fill_color == "white":
+            output = torch.ones_like(image)   # Ones for white
+        else:
+            raise ValueError("Invalid fill_color: must be 'black' or 'white'")
+
+        # Process the bottom part: shift upward by Y pixels
+        bottom_dest_start = max(X - Y, 0)
+        bottom_dest_end = min(H - 1 - Y, H - 1)
+        if bottom_dest_start <= bottom_dest_end:
+            num_rows_bottom = bottom_dest_end - bottom_dest_start + 1
+            bottom_src_start = bottom_dest_start + Y
+            bottom_src_end = bottom_src_start + num_rows_bottom - 1
+            if bottom_src_start < X:
+                offset = X - bottom_src_start
+                bottom_dest_start += offset
+                bottom_src_start = X
+                num_rows_bottom -= offset
+            if bottom_src_end >= H:
+                excess = bottom_src_end - (H - 1)
+                num_rows_bottom -= excess
+                bottom_dest_end = bottom_dest_start + num_rows_bottom - 1
+            if num_rows_bottom > 0:
+                output[:, bottom_dest_start:bottom_dest_end + 1, :, :] = \
+                    image[:, bottom_src_start:bottom_src_end + 1, :, :]
+
+        # Process the top part: shift downward by Z pixels
+        top_dest_start = max(Z, 0)
+        top_dest_end = min(X - 1 + Z, H - 1)
+        if top_dest_start <= top_dest_end:
+            num_rows_top = top_dest_end - top_dest_start + 1
+            top_src_start = top_dest_start - Z
+            top_src_end = top_src_start + num_rows_top - 1
+            if top_src_end >= X:
+                excess = top_src_end - (X - 1)
+                num_rows_top -= excess
+                top_dest_end = top_dest_start + num_rows_top - 1
+            if num_rows_top > 0:
+                output[:, top_dest_start:top_dest_end + 1, :, :] = \
+                    image[:, top_src_start:top_src_end + 1, :, :]
+
+        return (output,)  # Return the output tensor as a tuple