some clean up

src/diffusers/models/resnet.py

@@ -175,6 +175,7 @@ class Downsample(nn.Module):
 
 
 # unet.py, unet_grad_tts.py, unet_ldm.py, unet_glide.py, unet_score_vde.py
+# => All 2D-Resnets are included here now!
 class ResnetBlock(nn.Module):
     def __init__(
         self,
@@ -317,9 +318,6 @@ class ResnetBlock(nn.Module):
             num_groups = min(in_ch // 4, 32)
             num_groups_out = min(out_ch // 4, 32)
             temb_dim = temb_channels
-            #            output_scale_factor = np.sqrt(2.0)
-            #            non_linearity = "silu"
-            #            use_nin_shortcut = in_channels != out_channels or use_nin_shortcut = True
 
             self.GroupNorm_0 = nn.GroupNorm(num_groups=num_groups, num_channels=in_ch, eps=eps)
             self.up = up
@@ -337,13 +335,9 @@ class ResnetBlock(nn.Module):
                 # 1x1 convolution with DDPM initialization.
                 self.Conv_2 = conv2d(in_ch, out_ch, kernel_size=1, padding=0)
 
-            #            self.skip_rescale = skip_rescale
             self.in_ch = in_ch
             self.out_ch = out_ch
 
-            # TODO(Patrick) - move to main init
-            self.is_overwritten = False
-
     def set_weights_grad_tts(self):
         self.conv1.weight.data = self.block1.block[0].weight.data
         self.conv1.bias.data = self.block1.block[0].bias.data