clean up lots of dead code

src/diffusers/models/unet.py

@@ -34,48 +34,6 @@ def Normalize(in_channels):
     return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
 
 
-# class ResnetBlock(nn.Module):
-#    def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False, dropout, temb_channels=512):
-#        super().__init__()
-#        self.in_channels = in_channels
-#        out_channels = in_channels if out_channels is None else out_channels
-#        self.out_channels = out_channels
-#        self.use_conv_shortcut = conv_shortcut
-#
-#        self.norm1 = Normalize(in_channels)
-#        self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
-#        self.temb_proj = torch.nn.Linear(temb_channels, out_channels)
-#        self.norm2 = Normalize(out_channels)
-#        self.dropout = torch.nn.Dropout(dropout)
-#        self.conv2 = torch.nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
-#        if self.in_channels != self.out_channels:
-#            if self.use_conv_shortcut:
-#                self.conv_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
-#            else:
-#                self.nin_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
-#
-#    def forward(self, x, temb):
-#        h = x
-#        h = self.norm1(h)
-#        h = nonlinearity(h)
-#        h = self.conv1(h)
-#
-#        h = h + self.temb_proj(nonlinearity(temb))[:, :, None, None]
-#
-#        h = self.norm2(h)
-#        h = nonlinearity(h)
-#        h = self.dropout(h)
-#        h = self.conv2(h)
-#
-#        if self.in_channels != self.out_channels:
-#            if self.use_conv_shortcut:
-#                x = self.conv_shortcut(x)
-#            else:
-#                x = self.nin_shortcut(x)
-#
-#        return x + h
-
-
 class UNetModel(ModelMixin, ConfigMixin):
     def __init__(
         self,

src/diffusers/models/unet_glide.py

@@ -29,19 +29,6 @@ def convert_module_to_f32(l):
             l.bias.data = l.bias.data.float()
 
 
-def avg_pool_nd(dims, *args, **kwargs):
-    """
-    Create a 1D, 2D, or 3D average pooling module.
-    """
-    if dims == 1:
-        return nn.AvgPool1d(*args, **kwargs)
-    elif dims == 2:
-        return nn.AvgPool2d(*args, **kwargs)
-    elif dims == 3:
-        return nn.AvgPool3d(*args, **kwargs)
-    raise ValueError(f"unsupported dimensions: {dims}")
-
-
 def conv_nd(dims, *args, **kwargs):
     """
     Create a 1D, 2D, or 3D convolution module.