diff --git a/torchvision/transforms/v2/functional/_geometry.py b/torchvision/transforms/v2/functional/_geometry.py
index 1c9ce3f6df0..6bfdf43fed6 100644
--- a/torchvision/transforms/v2/functional/_geometry.py
+++ b/torchvision/transforms/v2/functional/_geometry.py
@@ -451,6 +451,12 @@ def _parallelogram_to_bounding_boxes(parallelogram: torch.Tensor) -> torch.Tenso
         torch.Tensor: Tensor of same shape as input containing the rectangle coordinates.
                      The output maintains the same dtype as the input.
     """
+    dtype = parallelogram.dtype
+    acceptable_dtypes = [torch.float32, torch.float64]
+    need_cast = dtype not in acceptable_dtypes
+    if need_cast:
+        # Up-case to avoid overflow for square operations
+        parallelogram = parallelogram.to(torch.float32)
     out_boxes = parallelogram.clone()
 
     # Calculate parallelogram diagonal vectors
@@ -489,6 +495,10 @@ def _parallelogram_to_bounding_boxes(parallelogram: torch.Tensor) -> torch.Tenso
     out_boxes[..., 1] = torch.where(~mask, parallelogram[..., 3] + delta_y, parallelogram[..., 1])
     out_boxes[..., 4] = torch.where(~mask, parallelogram[..., 6] + delta_x, parallelogram[..., 4])
     out_boxes[..., 5] = torch.where(~mask, parallelogram[..., 7] - delta_y, parallelogram[..., 5])
+
+    if need_cast:
+        out_boxes = out_boxes.to(dtype)
+
     return out_boxes
 
 
diff --git a/torchvision/transforms/v2/functional/_meta.py b/torchvision/transforms/v2/functional/_meta.py
index 6256a288203..4568b39ab59 100644
--- a/torchvision/transforms/v2/functional/_meta.py
+++ b/torchvision/transforms/v2/functional/_meta.py
@@ -194,11 +194,6 @@ def _cxcywhr_to_xywhr(cxcywhr: torch.Tensor, inplace: bool) -> torch.Tensor:
     if not inplace:
         cxcywhr = cxcywhr.clone()
 
-    dtype = cxcywhr.dtype
-    need_cast = not cxcywhr.is_floating_point()
-    if need_cast:
-        cxcywhr = cxcywhr.float()
-
     half_wh = cxcywhr[..., 2:-1].div(-2, rounding_mode=None if cxcywhr.is_floating_point() else "floor").abs_()
     r_rad = cxcywhr[..., 4].mul(torch.pi).div(180.0)
     cos, sin = r_rad.cos(), r_rad.sin()
@@ -207,10 +202,6 @@ def _cxcywhr_to_xywhr(cxcywhr: torch.Tensor, inplace: bool) -> torch.Tensor:
     # (cy + width / 2 * sin - height / 2 * cos) = y1
     cxcywhr[..., 1].add_(half_wh[..., 0].mul(sin)).sub_(half_wh[..., 1].mul(cos))
 
-    if need_cast:
-        cxcywhr.round_()
-        cxcywhr = cxcywhr.to(dtype)
-
     return cxcywhr
 
 
@@ -218,11 +209,6 @@ def _xywhr_to_cxcywhr(xywhr: torch.Tensor, inplace: bool) -> torch.Tensor:
     if not inplace:
         xywhr = xywhr.clone()
 
-    dtype = xywhr.dtype
-    need_cast = not xywhr.is_floating_point()
-    if need_cast:
-        xywhr = xywhr.float()
-
     half_wh = xywhr[..., 2:-1].div(-2, rounding_mode=None if xywhr.is_floating_point() else "floor").abs_()
     r_rad = xywhr[..., 4].mul(torch.pi).div(180.0)
     cos, sin = r_rad.cos(), r_rad.sin()
@@ -231,10 +217,6 @@ def _xywhr_to_cxcywhr(xywhr: torch.Tensor, inplace: bool) -> torch.Tensor:
     # (y1 - width / 2 * sin + height / 2 * cos) = cy
     xywhr[..., 1].sub_(half_wh[..., 0].mul(sin)).add_(half_wh[..., 1].mul(cos))
 
-    if need_cast:
-        xywhr.round_()
-        xywhr = xywhr.to(dtype)
-
     return xywhr
 
 
@@ -243,11 +225,6 @@ def _xywhr_to_xyxyxyxy(xywhr: torch.Tensor, inplace: bool) -> torch.Tensor:
     if not inplace:
         xywhr = xywhr.clone()
 
-    dtype = xywhr.dtype
-    need_cast = not xywhr.is_floating_point()
-    if need_cast:
-        xywhr = xywhr.float()
-
     wh = xywhr[..., 2:-1]
     r_rad = xywhr[..., 4].mul(torch.pi).div(180.0)
     cos, sin = r_rad.cos(), r_rad.sin()
@@ -265,10 +242,6 @@ def _xywhr_to_xyxyxyxy(xywhr: torch.Tensor, inplace: bool) -> torch.Tensor:
     # y1 + h * cos = y4
     xywhr[..., 7].add_(wh[..., 1].mul(cos))
 
-    if need_cast:
-        xywhr.round_()
-        xywhr = xywhr.to(dtype)
-
     return xywhr
 
 
@@ -278,9 +251,11 @@ def _xyxyxyxy_to_xywhr(xyxyxyxy: torch.Tensor, inplace: bool) -> torch.Tensor:
         xyxyxyxy = xyxyxyxy.clone()
 
     dtype = xyxyxyxy.dtype
-    need_cast = not xyxyxyxy.is_floating_point()
+    acceptable_dtypes = [torch.float32, torch.float64]  # Ensure consistency between CPU and GPU.
+    need_cast = dtype not in acceptable_dtypes
     if need_cast:
-        xyxyxyxy = xyxyxyxy.float()
+        # Up-case to avoid overflow for square operations
+        xyxyxyxy = xyxyxyxy.to(torch.float32)
 
     r_rad = torch.atan2(xyxyxyxy[..., 1].sub(xyxyxyxy[..., 3]), xyxyxyxy[..., 2].sub(xyxyxyxy[..., 0]))
     # x1, y1, (x2 - x1), (y2 - y1), (x3 - x2), (y3 - y2) x4, y4
@@ -293,7 +268,6 @@ def _xyxyxyxy_to_xywhr(xyxyxyxy: torch.Tensor, inplace: bool) -> torch.Tensor:
     xyxyxyxy[..., 4] = r_rad.div_(torch.pi).mul_(180.0)
 
     if need_cast:
-        xyxyxyxy.round_()
         xyxyxyxy = xyxyxyxy.to(dtype)
 
     return xyxyxyxy[..., :5]