Add ImagePadForOutpaintMasked

nodes.py

@@ -4384,8 +4384,88 @@ RealEstate camera intrinsics and coordinates in a 3D plot.
         else:
             ret_poses = [np.linalg.inv(w2c) for w2c in w2cs]
         ret_poses = [transform_matrix @ x for x in ret_poses]
-        return np.array(ret_poses, dtype=np.float32)           
-   
+        return np.array(ret_poses, dtype=np.float32)
+    
+class ImagePadForOutpaintMasked:
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "left": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
+                "top": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
+                "right": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
+                "bottom": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
+                "feathering": ("INT", {"default": 40, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
+            },
+            "optional": {
+                "mask": ("MASK",),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE", "MASK")
+    FUNCTION = "expand_image"
+
+    CATEGORY = "image"
+
+    def expand_image(self, image, left, top, right, bottom, feathering, mask=None):
+        B, H, W, C = image.size()
+
+        new_image = torch.ones(
+            (B, H + top + bottom, W + left + right, C),
+            dtype=torch.float32,
+        ) * 0.5
+
+        new_image[:, top:top + H, left:left + W, :] = image
+
+        if mask is None:
+            new_mask = torch.ones(
+                (H + top + bottom, W + left + right),
+                dtype=torch.float32,
+            )
+
+            t = torch.zeros(
+            (H, W),
+            dtype=torch.float32
+            )
+        else:
+            # If a mask is provided, pad it to fit the new image size
+            mask = F.pad(mask, (left, right, top, bottom), mode='constant', value=0)
+            mask = 1 - mask
+            t = torch.zeros_like(mask)
+
+        
+        
+        if feathering > 0 and feathering * 2 < H and feathering * 2 < W:
+
+            for i in range(H):
+                for j in range(W):
+                    dt = i if top != 0 else H
+                    db = H - i if bottom != 0 else H
+
+                    dl = j if left != 0 else W
+                    dr = W - j if right != 0 else W
+
+                    d = min(dt, db, dl, dr)
+
+                    if d >= feathering:
+                        continue
+
+                    v = (feathering - d) / feathering
+
+                    if mask is None:
+                        t[i, j] = v * v
+                    else:
+                        t[:, top + i, left + j] = v * v
+        
+        if mask is None:
+            mask = new_mask.squeeze(0)
+            mask[top:top + H, left:left + W] = t
+            mask = mask.unsqueeze(0)
+
+        return (new_image, mask,)
+    
 NODE_CLASS_MAPPINGS = {
     "INTConstant": INTConstant,
     "FloatConstant": FloatConstant,
@@ -4461,7 +4541,8 @@ NODE_CLASS_MAPPINGS = {
     "BboxVisualize": BboxVisualize,
     "StringConstantMultiline": StringConstantMultiline,
     "JoinStrings": JoinStrings,
-    "Sleep": Sleep
+    "Sleep": Sleep,
+    "ImagePadForOutpaintMasked": ImagePadForOutpaintMasked
 }
 NODE_DISPLAY_NAME_MAPPINGS = {
     "INTConstant": "INT Constant",
@@ -4539,4 +4620,5 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "StringConstantMultiline": "StringConstantMultiline",
     "JoinStrings": "JoinStrings",
     "Sleep": "🛌 Sleep 🛌",
+    "ImagePadForOutpaintMasked": "Pad Image For Outpaint Masked",
 }