diff --git a/graph_weather/models/fengwu_ghr/layers.py b/graph_weather/models/fengwu_ghr/layers.py
index 2d032ab..a9ec895 100644
--- a/graph_weather/models/fengwu_ghr/layers.py
+++ b/graph_weather/models/fengwu_ghr/layers.py
@@ -363,93 +363,3 @@ def forward(self, x):
         x = rearrange(x, "n (b c) -> b n c", b=b, c=c)
 
         return x
-
-class MetaModel(nn.Module):
-    def __init__(
-        self,
-        lat_lons: list,
-        *,
-        image_size,
-        patch_size,
-        depth,
-        heads,
-        mlp_dim,
-        channels,
-        dim_head=64
-    ):
-        super().__init__()
-        self.i_h, self.i_w = pair(image_size)
-
-        self.pos_x = torch.tensor(lat_lons)
-        self.pos_y = torch.cartesian_prod(
-            (torch.arange(-self.i_h / 2, self.i_h / 2, 1) / self.i_h * 180).to(torch.long),
-            (torch.arange(0, self.i_w, 1) / self.i_w * 360).to(torch.long),
-        )
-
-        self.image_meta_model = ImageMetaModel(
-            image_size=image_size,
-            patch_size=patch_size,
-            depth=depth,
-            heads=heads,
-            mlp_dim=mlp_dim,
-            channels=channels,
-            dim_head=dim_head,
-        )
-
-    def forward(self, x):
-        b, n, c = x.shape
-
-        x = rearrange(x, "b n c -> n (b c)")
-        x = knn_interpolate(x, self.pos_x, self.pos_y)
-        x = rearrange(x, "(h w) (b c) -> b c h w", b=b, c=c, h=self.i_h, w=self.i_w)
-        x = self.image_meta_model(x)
-
-        x = rearrange(x, "b c h w -> (h w) (b c)")
-        x = knn_interpolate(x, self.pos_y, self.pos_x)
-        x = rearrange(x, "n (b c) -> b n c", b=b, c=c)
-        return x
-
-
-class WrapperMetaModel(nn.Module):
-    def __init__(
-        self,
-        lat_lons: list,
-        meta_model: MetaModel,
-        scale_factor
-    ):
-        super().__init__()
-        s_h, s_w = pair(scale_factor)
-        self.i_h, self.i_w = meta_model.i_h * s_h, meta_model.i_w * s_w
-        self.pos_x = torch.tensor(lat_lons)
-        self.pos_y = torch.cartesian_prod(
-            (torch.arange(-self.i_h / 2, self.i_h / 2, 1) / self.i_h * 180).to(torch.long),
-            (torch.arange(0, self.i_w, 1) / self.i_w * 360).to(torch.long),
-        )
-
-        self.batcher = Rearrange("b c (h s_h) (w s_w) -> (b s_h s_w) c h w", s_h=s_h, s_w=s_w)
-
-        imm_args = vars(meta_model.image_meta_model)
-        imm_args.update({"res": True, "scale_factor": scale_factor})
-        self.image_meta_model = ImageMetaModel(**imm_args)
-        self.image_meta_model.load_state_dict(
-            meta_model.image_meta_model.state_dict(), strict=False
-        )
-
-        self.debatcher = Rearrange("(b s_h s_w) c h w -> b c (h s_h) (w s_w)", s_h=s_h, s_w=s_w)
-
-    def forward(self, x):
-        b, n, c = x.shape
-
-        x = rearrange(x, "b n c -> n (b c)")
-        x = knn_interpolate(x, self.pos_x, self.pos_y)
-        x = rearrange(x, "(h w) (b c) -> b c h w", b=b, c=c, h=self.i_h, w=self.i_w)
-
-        x = self.batcher(x)
-        x = self.image_meta_model(x)
-        x = self.debatcher(x)
-
-        x = rearrange(x, "b c h w -> (h w) (b c)")
-        x = knn_interpolate(x, self.pos_y, self.pos_x)
-        x = rearrange(x, "n (b c) -> b n c", b=b, c=c)
-
-        return x