diff --git a/src/diffusers/models/resnet.py b/src/diffusers/models/resnet.py
index 9a7eaa2ecd..49c1564253 100644
--- a/src/diffusers/models/resnet.py
+++ b/src/diffusers/models/resnet.py
@@ -150,81 +150,6 @@ class Downsample(nn.Module):
             return self.op(x)
 
 
-# class UNetUpsample(nn.Module):
-#    def __init__(self, in_channels, with_conv):
-#        super().__init__()
-#        self.with_conv = with_conv
-#        if self.with_conv:
-#            self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
-#
-#    def forward(self, x):
-#        x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
-#        if self.with_conv:
-#            x = self.conv(x)
-#        return x
-#
-#
-# class GlideUpsample(nn.Module):
-#    """
-# An upsampling layer with an optional convolution. # # :param channels: channels in the inputs and outputs. :param #
-use_conv: a bool determining if a convolution is # applied. :param dims: determines if the signal is 1D, 2D, or 3D. If
-# 3D, then # upsampling occurs in the inner-two dimensions. #"""
-#
-#    def __init__(self, channels, use_conv, dims=2, out_channels=None):
-#        super().__init__()
-#        self.channels = channels
-#        self.out_channels = out_channels or channels
-#        self.use_conv = use_conv
-#        self.dims = dims
-#        if use_conv:
-#            self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=1)
-#
-#    def forward(self, x):
-#        assert x.shape[1] == self.channels
-#        if self.dims == 3:
-#            x = F.interpolate(x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest")
-#        else:
-#            x = F.interpolate(x, scale_factor=2, mode="nearest")
-#        if self.use_conv:
-#            x = self.conv(x)
-#        return x
-#
-#
-# class LDMUpsample(nn.Module):
-#    """
-# An upsampling layer with an optional convolution. :param channels: channels in the inputs and outputs. :param # #
-use_conv: a bool determining if a convolution is applied. :param dims: determines if the signal is 1D, 2D, or 3D. # If
-# 3D, then # upsampling occurs in the inner-two dimensions. #"""
-#
-#    def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
-#        super().__init__()
-#        self.channels = channels
-#        self.out_channels = out_channels or channels
-#        self.use_conv = use_conv
-#        self.dims = dims
-#        if use_conv:
-#            self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding)
-#
-#    def forward(self, x):
-#        assert x.shape[1] == self.channels
-#        if self.dims == 3:
-#            x = F.interpolate(x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest")
-#        else:
-#            x = F.interpolate(x, scale_factor=2, mode="nearest")
-#        if self.use_conv:
-#            x = self.conv(x)
-#        return x
-#
-#
-# class GradTTSUpsample(torch.nn.Module):
-#    def __init__(self, dim):
-#        super(Upsample, self).__init__()
-#        self.conv = torch.nn.ConvTranspose2d(dim, dim, 4, 2, 1)
-#
-#    def forward(self, x):
-#        return self.conv(x)
-#
-#
 # TODO (patil-suraj): needs test
 # class Upsample1d(nn.Module):
 #    def __init__(self, dim):