Add nodes for tile splitting and merging. The main motivation for these nodes is for use in tiled upscaling workflows.

invokeai/backend/tiles/utils.py

@@ -0,0 +1,36 @@
+from typing import Optional
+
+import numpy as np
+from pydantic import BaseModel, Field
+
+
+class TBLR(BaseModel):
+    top: int
+    bottom: int
+    left: int
+    right: int
+
+
+class Tile(BaseModel):
+    coords: TBLR = Field(description="The coordinates of this tile relative to its parent image.")
+    overlap: TBLR = Field(description="The amount of overlap with adjacent tiles on each side of this tile.")
+
+
+def paste(dst_image: np.ndarray, src_image: np.ndarray, box: TBLR, mask: Optional[np.ndarray] = None):
+    """Paste a source image into a destination image.
+
+    Args:
+        dst_image (torch.Tensor): The destination image to paste into. Shape: (H, W, C).
+        src_image (torch.Tensor): The source image to paste. Shape: (H, W, C). H and W must be compatible with 'box'.
+        box (TBLR): Box defining the region in the 'dst_image' where 'src_image' will be pasted.
+        mask (Optional[torch.Tensor]): A mask that defines the blending between 'src_image' and 'dst_image'.
+            Range: [0.0, 1.0], Shape: (H, W). The output is calculate per-pixel according to
+            `src * mask + dst * (1 - mask)`.
+    """
+
+    if mask is None:
+        dst_image[box.top : box.bottom, box.left : box.right] = src_image
+    else:
+        mask = np.expand_dims(mask, -1)
+        dst_image_box = dst_image[box.top : box.bottom, box.left : box.right]
+        dst_image[box.top : box.bottom, box.left : box.right] = src_image * mask + dst_image_box * (1.0 - mask)