diff --git a/monai/transforms/croppad/functional.py b/monai/transforms/croppad/functional.py
index fc086eb9a8..cefd42ca58 100644
--- a/monai/transforms/croppad/functional.py
+++ b/monai/transforms/croppad/functional.py
@@ -15,6 +15,8 @@
 
 from __future__ import annotations
 
+import warnings
+
 import numpy as np
 import torch
 from torch.nn.functional import pad as pad_pt
@@ -29,7 +31,12 @@
 
 
 def _np_pad(img: torch.Tensor, pad_width: list[tuple[int, int]], mode: str, **kwargs) -> torch.Tensor:
-    img_np = img.detach().cpu().numpy() if isinstance(img, torch.Tensor) else img
+    if isinstance(img, torch.Tensor):
+        if img.is_cuda:
+            warnings.warn(f"Padding: moving img {img.shape} from cuda to cpu for dtype={img.dtype} mode={mode}.")
+        img_np = img.detach().cpu().numpy()
+    else:
+        img_np = img
     mode = convert_pad_mode(dst=img_np, mode=mode).value
     if mode == "constant" and "value" in kwargs:
         kwargs["constant_values"] = kwargs.pop("value")
@@ -40,9 +47,15 @@ def _np_pad(img: torch.Tensor, pad_width: list[tuple[int, int]], mode: str, **kw
 
 
 def _pt_pad(img: torch.Tensor, pad_width: list[tuple[int, int]], mode: str, **kwargs) -> torch.Tensor:
+    mode = convert_pad_mode(dst=img, mode=mode).value
+    if mode == "constant" and "constant_values" in kwargs:
+        _kwargs = kwargs.copy()
+        _kwargs["value"] = _kwargs.pop("constant_values")
+    else:
+        _kwargs = kwargs
     pt_pad_width = [val for sublist in pad_width[1:] for val in sublist[::-1]][::-1]
     # torch.pad expects `[B, C, H, W, [D]]` shape
-    return pad_pt(img.unsqueeze(0), pt_pad_width, mode=mode, **kwargs).squeeze(0)
+    return pad_pt(img.unsqueeze(0), pt_pad_width, mode=mode, **_kwargs).squeeze(0)
 
 
 def pad_nd(img: torch.Tensor, to_pad: list[tuple[int, int]], mode: str, **kwargs):
@@ -68,14 +81,14 @@ def pad_nd(img: torch.Tensor, to_pad: list[tuple[int, int]], mode: str, **kwargs
     mode = convert_pad_mode(dst=img, mode=mode).value
     try:
         _pad = (
-            _pt_pad
-            if mode in {"reflect", "replicate"} and img.dtype not in {torch.int16, torch.int64, torch.bool, torch.uint8}
-            else _np_pad
+            _np_pad
+            if mode in {"reflect", "replicate"} and img.dtype in {torch.int16, torch.int64, torch.bool, torch.uint8}
+            else _pt_pad
         )
         return _pad(img, pad_width=to_pad, mode=mode, **kwargs)
     except (ValueError, TypeError, RuntimeError) as err:
         if isinstance(err, NotImplementedError) or any(
-            k in str(err) for k in ("supported", "unexpected keyword", "implemented")
+            k in str(err) for k in ("supported", "unexpected keyword", "implemented", "value")
         ):
             return _np_pad(img, pad_width=to_pad, mode=mode, **kwargs)
         raise ValueError(f"{img.shape} {to_pad} {mode} {kwargs} {img.dtype} {img.device}") from err
diff --git a/monai/transforms/utils.py b/monai/transforms/utils.py
index a8db6818bc..d3c8eb606f 100644
--- a/monai/transforms/utils.py
+++ b/monai/transforms/utils.py
@@ -1628,13 +1628,13 @@ def convert_pad_mode(dst: NdarrayOrTensor, mode: str | None):
     if isinstance(dst, torch.Tensor):
         if mode == "wrap":
             mode = "circular"
-        if mode == "edge":
+        elif mode == "edge":
             mode = "replicate"
         return look_up_option(mode, PytorchPadMode)
     if isinstance(dst, np.ndarray):
         if mode == "circular":
             mode = "wrap"
-        if mode == "replicate":
+        elif mode == "replicate":
             mode = "edge"
         return look_up_option(mode, NumpyPadMode)
     raise ValueError(f"unsupported data type: {type(dst)}.")