diff --git a/core/compose.py b/core/compose.py
index 3d516f3..89a6588 100644
--- a/core/compose.py
+++ b/core/compose.py
@@ -40,7 +40,7 @@ from Jovimetrix.sup.image.channel import EnumPixelSwizzle, channel_merge, \
     channel_solid
 
 from Jovimetrix.sup.image.compose import EnumAdjustOP, EnumBlendType, \
-    EnumOrientation, image_levels, image_split, image_stack, image_blend, \
+    EnumOrientation, image_by_size, image_levels, image_split, image_stack, image_blend, \
     image_crop, image_crop_center, image_crop_polygonal
 
 from Jovimetrix.sup.image.mapping import EnumProjection, remap_fisheye, \
@@ -262,17 +262,32 @@ Combine two input images using various blending modes, such as normal, screen, m
             if flip:
                 pA, pB = pB, pA
 
+            width, height = MIN_IMAGE_SIZE, MIN_IMAGE_SIZE
             if pA is None:
-                pA = channel_solid(MIN_IMAGE_SIZE, MIN_IMAGE_SIZE, matte, chan=EnumImageType.BGRA)
+                if pB is None:
+                    if mask is None:
+                        images.append(img)
+                        pbar.update_absolute(idx)
+                        continue
+                    else:
+                        height, width = mask.shape[:2]
+                else:
+                    height, width = pB.shape[:2]
+            else:
+                height, width = pA.shape[:2]
+
+            if pA is None:
+                pA = channel_solid(width, height, matte, chan=EnumImageType.BGRA)
             else:
                 pA = tensor2cv(pA)
                 matted = pixel_eval(matte, EnumImageType.BGRA)
                 pA = image_matte(pA, matted)
 
             if pB is None:
-                pB = channel_solid(MIN_IMAGE_SIZE, MIN_IMAGE_SIZE, matte, chan=EnumImageType.BGRA)
+                pB = channel_solid(width, height, matte, chan=EnumImageType.BGRA)
             else:
                 pB = tensor2cv(pB)
+                print(pB.shape)
 
             if mask is not None:
                 mask = tensor2cv(mask)
@@ -283,7 +298,9 @@ Combine two input images using various blending modes, such as normal, screen, m
             img = image_blend(pA, pB, mask, func, alpha)
 
             if mode != EnumScaleMode.MATTE:
-                img = image_scalefit(img, *wihi, mode, sample)
+                # or mode != EnumScaleMode.RESIZE_MATTE:
+                width, height = wihi
+                img = image_scalefit(img, width, height, mode, sample)
 
             img = cv2tensor_full(img, matte)
             images.append(img)
diff --git a/core/create.py b/core/create.py
index 8a08374..400864f 100644
--- a/core/create.py
+++ b/core/create.py
@@ -137,16 +137,10 @@ Create n-sided polygons. These shapes can be customized by adjusting parameters
             back = matte[:3]
 
             match shape:
-                case EnumShapes.RECTANGLE:
+                case EnumShapes.RECTANGLE | EnumShapes.SQUARE:
                     pA = shape_quad(width, height, sizeX, sizeY, fill, back)
 
-                case EnumShapes.SQUARE:
-                    pA = shape_quad(width, height, sizeX, sizeX, fill, back)
-
-                case EnumShapes.ELLIPSE:
-                    pA = shape_ellipse(width, height, sizeX, sizeY, fill, back)
-
-                case EnumShapes.CIRCLE:
+                case EnumShapes.ELLIPSE | EnumShapes.CIRCLE:
                     pA = shape_ellipse(width, height, sizeX, sizeY, fill, back)
 
                 case EnumShapes.POLYGON:
diff --git a/sup/image/adjust.py b/sup/image/adjust.py
index 118652b..b67ea29 100644
--- a/sup/image/adjust.py
+++ b/sup/image/adjust.py
@@ -60,6 +60,7 @@ class EnumScaleMode(Enum):
     FIT = 10
     ASPECT = 30
     ASPECT_SHORT = 35
+    RESIZE_MATTE = 40
 
 class EnumThreshold(Enum):
     BINARY = cv2.THRESH_BINARY
@@ -167,7 +168,7 @@ def image_flatten(image: List[TYPE_IMAGE], width:int=None, height:int=None,
 
     current = np.zeros((height, width, 4), dtype=np.uint8)
     for x in image:
-        if mode != EnumScaleMode.MATTE:
+        if mode != EnumScaleMode.MATTE and mode != EnumScaleMode.RESIZE_MATTE:
             x = image_scalefit(x, width, height, mode, sample)
         x = image_matte(x, (0,0,0,0), width, height)
         x = image_scalefit(x, width, height, EnumScaleMode.CROP, sample)
@@ -363,7 +364,7 @@ def image_scalefit(image: TYPE_IMAGE, width: int, height:int,
                 matte:TYPE_PIXEL=(0,0,0,0)) -> TYPE_IMAGE:
 
     match mode:
-        case EnumScaleMode.MATTE:
+        case EnumScaleMode.MATTE | EnumScaleMode.RESIZE_MATTE:
             image = image_matte(image, matte, width, height)
 
         case EnumScaleMode.ASPECT: