diff --git a/monai/transforms/smooth_field/dictionary.py b/monai/transforms/smooth_field/dictionary.py
index 4eca541fcc..c129d14f32 100644
--- a/monai/transforms/smooth_field/dictionary.py
+++ b/monai/transforms/smooth_field/dictionary.py
@@ -50,7 +50,6 @@ class RandSmoothFieldAdjustContrastd(RandomizableTransform, MapTransform):
         align_corners: if True align the corners when upsampling field
         prob: probability transform is applied
         gamma: (min, max) range for exponential field
-        apply_same_field: if True, apply the same field to each key, otherwise randomize individually
         device: Pytorch device to define field on
     """
 
@@ -66,13 +65,11 @@ def __init__(
         align_corners: Optional[bool] = None,
         prob: float = 0.1,
         gamma: Union[Sequence[float], float] = (0.5, 4.5),
-        apply_same_field: bool = True,
         device: Optional[torch.device] = None,
     ):
         RandomizableTransform.__init__(self, prob)
         MapTransform.__init__(self, keys)
 
-        self.apply_same_field = apply_same_field
         self.mode = ensure_tuple_rep(mode, len(self.keys))
 
         self.trans = RandSmoothFieldAdjustContrast(
@@ -108,9 +105,6 @@ def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Mapping[Hashable
         d = dict(data)
 
         for idx, key in enumerate(self.key_iterator(d)):
-            if not self.apply_same_field:
-                self.randomize()  # new field for every key
-
             self.trans.set_mode(self.mode[idx % len(self.mode)])
             d[key] = self.trans(d[key], False)
 
@@ -134,7 +128,6 @@ class RandSmoothFieldAdjustIntensityd(RandomizableTransform, MapTransform):
         align_corners: if True align the corners when upsampling field
         prob: probability transform is applied
         gamma: (min, max) range of intensity multipliers
-        apply_same_field: if True, apply the same field to each key, otherwise randomize individually
         device: Pytorch device to define field on
     """
 
@@ -150,13 +143,11 @@ def __init__(
         align_corners: Optional[bool] = None,
         prob: float = 0.1,
         gamma: Union[Sequence[float], float] = (0.1, 1.0),
-        apply_same_field: bool = True,
         device: Optional[torch.device] = None,
     ):
         RandomizableTransform.__init__(self, prob)
         MapTransform.__init__(self, keys)
 
-        self.apply_same_field = apply_same_field
         self.mode = ensure_tuple_rep(mode, len(self.keys))
 
         self.trans = RandSmoothFieldAdjustIntensity(
@@ -190,9 +181,6 @@ def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Mapping[Hashable
         d = dict(data)
 
         for idx, key in enumerate(self.key_iterator(d)):
-            if not self.apply_same_field:
-                self.randomize()  # new field for every key
-
             self.trans.set_mode(self.mode[idx % len(self.mode)])
             d[key] = self.trans(d[key], False)
 
@@ -220,7 +208,6 @@ class RandSmoothDeformd(RandomizableTransform, MapTransform):
         grid_mode: interpolation mode used for sampling input using deformation grid
         grid_padding_mode: padding mode used for sampling input using deformation grid
         grid_align_corners: if True align the corners when sampling the deformation grid
-        apply_same_field: if True, apply the same field to each key, otherwise randomize individually
         device: Pytorch device to define field on
     """
 
@@ -240,7 +227,6 @@ def __init__(
         grid_mode: Union[GridSampleModeType, Sequence[GridSampleModeType]] = GridSampleMode.NEAREST,
         grid_padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER,
         grid_align_corners: Optional[bool] = False,
-        apply_same_field: bool = True,
         device: Optional[torch.device] = None,
     ):
         RandomizableTransform.__init__(self, prob)
@@ -248,7 +234,6 @@ def __init__(
 
         self.field_mode = ensure_tuple_rep(field_mode, len(self.keys))
         self.grid_mode = ensure_tuple_rep(grid_mode, len(self.keys))
-        self.apply_same_field = apply_same_field
 
         self.trans = RandSmoothDeform(
             rand_size=rand_size,
@@ -285,9 +270,6 @@ def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Mapping[Hashable
         d = dict(data)
 
         for idx, key in enumerate(self.key_iterator(d)):
-            if not self.apply_same_field:
-                self.randomize()  # new field for every key
-
             self.trans.set_field_mode(self.field_mode[idx % len(self.field_mode)])
             self.trans.set_grid_mode(self.grid_mode[idx % len(self.grid_mode)])