diff --git a/src/diffusers/models/resnet.py b/src/diffusers/models/resnet.py
index ae6754b1d8..e55b83e962 100644
--- a/src/diffusers/models/resnet.py
+++ b/src/diffusers/models/resnet.py
@@ -957,19 +957,6 @@ def downsample_2d(x, k=None, factor=2, gain=1):
     return upfirdn2d(x, torch.tensor(k, device=x.device), down=factor, pad=((p + 1) // 2, p // 2))
 
 
-def naive_upsample_2d(x, factor=2):
-    _N, C, H, W = x.shape
-    x = torch.reshape(x, (-1, C, H, 1, W, 1))
-    x = x.repeat(1, 1, 1, factor, 1, factor)
-    return torch.reshape(x, (-1, C, H * factor, W * factor))
-
-
-def naive_downsample_2d(x, factor=2):
-    _N, C, H, W = x.shape
-    x = torch.reshape(x, (-1, C, H // factor, factor, W // factor, factor))
-    return torch.mean(x, dim=(3, 5))
-
-
 class NIN(nn.Module):
     def __init__(self, in_dim, num_units, init_scale=0.1):
         super().__init__()
diff --git a/src/diffusers/models/unet_sde_score_estimation.py b/src/diffusers/models/unet_sde_score_estimation.py
index 508bac141b..dda144457f 100644
--- a/src/diffusers/models/unet_sde_score_estimation.py
+++ b/src/diffusers/models/unet_sde_score_estimation.py
@@ -123,19 +123,6 @@ class Conv2d(nn.Module):
         return x
 
 
-def naive_upsample_2d(x, factor=2):
-    _N, C, H, W = x.shape
-    x = torch.reshape(x, (-1, C, H, 1, W, 1))
-    x = x.repeat(1, 1, 1, factor, 1, factor)
-    return torch.reshape(x, (-1, C, H * factor, W * factor))
-
-
-def naive_downsample_2d(x, factor=2):
-    _N, C, H, W = x.shape
-    x = torch.reshape(x, (-1, C, H // factor, factor, W // factor, factor))
-    return torch.mean(x, dim=(3, 5))
-
-
 def upsample_conv_2d(x, w, k=None, factor=2, gain=1):
     """Fused `upsample_2d()` followed by `tf.nn.conv2d()`.