diff --git a/src/diffusers/models/resnet.py b/src/diffusers/models/resnet.py
index dfd26ec19b..46824a85f1 100644
--- a/src/diffusers/models/resnet.py
+++ b/src/diffusers/models/resnet.py
@@ -404,9 +404,6 @@ class ResnetBlock(nn.Module):
         if groups_out is None:
             groups_out = groups
 
-        if use_nin_shortcut is None:
-            use_nin_shortcut = self.in_channels != self.out_channels
-
         if self.pre_norm:
             self.norm1 = Normalize(in_channels, num_groups=groups, eps=eps)
         else:
@@ -439,8 +436,11 @@ class ResnetBlock(nn.Module):
         self.upsample = Upsample(in_channels, use_conv=False, dims=2) if self.up else None
         self.downsample = Downsample(in_channels, use_conv=False, dims=2, padding=1, name="op") if self.down else None
 
-        self.nin_shortcut = None
-        if use_nin_shortcut:
+        self.nin_shortcut = use_nin_shortcut
+        if self.use_nin_shortcut is None:
+            self.use_nin_shortcut = self.in_channels != self.out_channels
+
+        if self.use_nin_shortcut:
             self.nin_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
 
         # TODO(SURAJ, PATRICK): ALL OF THE FOLLOWING OF THE INIT METHOD CAN BE DELETED ONCE WEIGHTS ARE CONVERTED
@@ -613,10 +613,6 @@ class ResnetBlock(nn.Module):
             x = self.downsample(x)
             h = self.downsample(h)
 
-#        if self.up: or self.down:
-#            x = self.x_upd(x)
-#            h = self.h_upd(h)
-#
         h = self.conv1(h)
 
         if not self.pre_norm:
diff --git a/src/diffusers/models/unet_sde_score_estimation.py b/src/diffusers/models/unet_sde_score_estimation.py
index af19ed68a2..c900ae7810 100644
--- a/src/diffusers/models/unet_sde_score_estimation.py
+++ b/src/diffusers/models/unet_sde_score_estimation.py
@@ -29,6 +29,7 @@ from .attention import AttentionBlock
 from .embeddings import GaussianFourierProjection, get_timestep_embedding
 from .resnet import downsample_2d, upfirdn2d, upsample_2d
 from .resnet import ResnetBlockBigGANppNew as ResnetBlockBigGANpp
+from .resnet import ResnetBlock as ResnetNew
 
 
 def _setup_kernel(k):
@@ -346,7 +347,19 @@ class NCSNpp(ModelMixin, ConfigMixin):
             # Residual blocks for this resolution
             for i_block in range(num_res_blocks):
                 out_ch = nf * ch_mult[i_level]
-                modules.append(ResnetBlock(in_ch=in_ch, out_ch=out_ch))
+#                modules.append(ResnetBlock(in_ch=in_ch, out_ch=out_ch))
+                modules.append(
+                    ResnetNew(
+                        in_channels=in_ch,
+                        out_channels=out_ch,
+                        temb_channels=4 * nf,
+                        output_scale_factor=np.sqrt(2.0),
+                        non_linearity="silu",
+                        groups=min(in_ch // 4, 32),
+                        groups_out=min(out_ch // 4, 32),
+                        overwrite_for_score_vde=True,
+                    )
+                )
                 in_ch = out_ch
 
                 if all_resolutions[i_level] in attn_resolutions: