diff --git a/libs/spandrel/spandrel/architectures/HAT/arch/HAT.py b/libs/spandrel/spandrel/architectures/HAT/arch/HAT.py
index 1febea39..51982b00 100644
--- a/libs/spandrel/spandrel/architectures/HAT/arch/HAT.py
+++ b/libs/spandrel/spandrel/architectures/HAT/arch/HAT.py
@@ -376,46 +376,6 @@ def forward(self, x, x_size, rpi_sa, attn_mask):
         return x
 
 
-class PatchMerging(nn.Module):
-    r"""Patch Merging Layer.
-
-    Args:
-        input_resolution (tuple[int]): Resolution of input feature.
-        dim (int): Number of input channels.
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-    """
-
-    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.input_resolution = input_resolution
-        self.dim = dim
-        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
-        self.norm = norm_layer(4 * dim)
-
-    def forward(self, x):
-        """
-        x: b, h*w, c
-        """
-        h, w = self.input_resolution
-        b, seq_len, c = x.shape
-        assert seq_len == h * w, "input feature has wrong size"
-        assert h % 2 == 0 and w % 2 == 0, f"x size ({h}*{w}) are not even."
-
-        x = x.view(b, h, w, c)
-
-        x0 = x[:, 0::2, 0::2, :]  # b h/2 w/2 c
-        x1 = x[:, 1::2, 0::2, :]  # b h/2 w/2 c
-        x2 = x[:, 0::2, 1::2, :]  # b h/2 w/2 c
-        x3 = x[:, 1::2, 1::2, :]  # b h/2 w/2 c
-        x = torch.cat([x0, x1, x2, x3], -1)  # b h/2 w/2 4*c
-        x = x.view(b, -1, 4 * c)  # b h/2*w/2 4*c
-
-        x = self.norm(x)
-        x = self.reduction(x)
-
-        return x
-
-
 class OCAB(nn.Module):
     # overlapping cross-attention block
 
@@ -1132,14 +1092,6 @@ def calculate_mask(self, x_size):
 
         return attn_mask
 
-    @torch.jit.ignore  # type: ignore
-    def no_weight_decay(self):
-        return {"absolute_pos_embed"}
-
-    @torch.jit.ignore  # type: ignore
-    def no_weight_decay_keywords(self):
-        return {"relative_position_bias_table"}
-
     def check_image_size(self, x):
         return pad_to_multiple(x, self.window_size, mode="reflect")