diff --git a/timm/models/swin_transformer_v2.py b/timm/models/swin_transformer_v2.py
index b152b544..e8a5a9cf 100644
--- a/timm/models/swin_transformer_v2.py
+++ b/timm/models/swin_transformer_v2.py
@@ -22,7 +22,7 @@ import torch.utils.checkpoint as checkpoint
 
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
 from timm.layers import PatchEmbed, Mlp, DropPath, to_2tuple, trunc_normal_, _assert, ClassifierHead,\
-    resample_patch_embed, ndgrid
+    resample_patch_embed, ndgrid, get_act_layer, LayerType
 from ._builder import build_model_with_cfg
 from ._features_fx import register_notrace_function
 from ._registry import generate_default_cfgs, register_model, register_model_deprecations
@@ -206,7 +206,7 @@ class SwinTransformerV2Block(nn.Module):
             proj_drop: float = 0.,
             attn_drop: float = 0.,
             drop_path: float = 0.,
-            act_layer: nn.Module = nn.GELU,
+            act_layer: LayerType = "gelu",
             norm_layer: nn.Module = nn.LayerNorm,
             pretrained_window_size: _int_or_tuple_2_t = 0,
     ) -> None:
@@ -235,6 +235,7 @@ class SwinTransformerV2Block(nn.Module):
         self.shift_size: Tuple[int, int] = ss
         self.window_area = self.window_size[0] * self.window_size[1]
         self.mlp_ratio = mlp_ratio
+        act_layer = get_act_layer(act_layer)
 
         self.attn = WindowAttention(
             dim,
@@ -372,6 +373,7 @@ class SwinTransformerV2Stage(nn.Module):
             proj_drop: float = 0.,
             attn_drop: float = 0.,
             drop_path: float = 0.,
+            act_layer: Union[str, Callable] = 'gelu',
             norm_layer: nn.Module = nn.LayerNorm,
             pretrained_window_size: _int_or_tuple_2_t = 0,
             output_nchw: bool = False,
@@ -390,6 +392,7 @@ class SwinTransformerV2Stage(nn.Module):
             proj_drop: Projection dropout rate
             attn_drop: Attention dropout rate.
             drop_path: Stochastic depth rate.
+            act_layer: Activation layer type.
             norm_layer: Normalization layer.
             pretrained_window_size: Local window size in pretraining.
             output_nchw: Output tensors on NCHW format instead of NHWC.
@@ -424,6 +427,7 @@ class SwinTransformerV2Stage(nn.Module):
                 proj_drop=proj_drop,
                 attn_drop=attn_drop,
                 drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
+                act_layer=act_layer,
                 norm_layer=norm_layer,
                 pretrained_window_size=pretrained_window_size,
             )
@@ -471,6 +475,7 @@ class SwinTransformerV2(nn.Module):
             proj_drop_rate: float = 0.,
             attn_drop_rate: float = 0.,
             drop_path_rate: float = 0.1,
+            act_layer: Union[str, Callable] = 'gelu',
             norm_layer: Callable = nn.LayerNorm,
             pretrained_window_sizes: Tuple[int, ...] = (0, 0, 0, 0),
             **kwargs,
@@ -492,6 +497,7 @@ class SwinTransformerV2(nn.Module):
             attn_drop_rate: Attention dropout rate.
             drop_path_rate: Stochastic depth rate.
             norm_layer: Normalization layer.
+            act_layer: Activation layer type.
             patch_norm: If True, add normalization after patch embedding.
             pretrained_window_sizes: Pretrained window sizes of each layer.
             output_fmt: Output tensor format if not None, otherwise output 'NHWC' by default.
@@ -541,6 +547,7 @@ class SwinTransformerV2(nn.Module):
                 proj_drop=proj_drop_rate,
                 attn_drop=attn_drop_rate,
                 drop_path=dpr[i],
+                act_layer=act_layer,
                 norm_layer=norm_layer,
                 pretrained_window_size=pretrained_window_sizes[i],
             )]