small fix

src/diffusers/models/resnet.py

@@ -237,12 +237,12 @@ class ResnetBlock(nn.Module):
         elif non_linearity == "silu":
             self.nonlinearity = nn.SiLU()
 
-#        if up:
-#            self.h_upd = Upsample(in_channels, use_conv=False, dims=2)
-#            self.x_upd = Upsample(in_channels, use_conv=False, dims=2)
-#        elif down:
-#            self.h_upd = Downsample(in_channels, use_conv=False, dims=2, padding=1, name="op")
-#            self.x_upd = Downsample(in_channels, use_conv=False, dims=2, padding=1, name="op")
+        #        if up:
+        #            self.h_upd = Upsample(in_channels, use_conv=False, dims=2)
+        #            self.x_upd = Upsample(in_channels, use_conv=False, dims=2)
+        #        elif down:
+        #            self.h_upd = Downsample(in_channels, use_conv=False, dims=2, padding=1, name="op")
+        #            self.x_upd = Downsample(in_channels, use_conv=False, dims=2, padding=1, name="op")
 
         self.upsample = self.downsample = None
         if self.up and kernel == "fir":
@@ -318,9 +318,9 @@ 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
+            #            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
@@ -338,7 +338,7 @@ 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.skip_rescale = skip_rescale
             self.in_ch = in_ch
             self.out_ch = out_ch
 

src/diffusers/models/unet_sde_score_estimation.py

@@ -27,8 +27,7 @@ from ..configuration_utils import ConfigMixin
 from ..modeling_utils import ModelMixin
 from .attention import AttentionBlock
 from .embeddings import GaussianFourierProjection, get_timestep_embedding
-from .resnet import downsample_2d, upfirdn2d, upsample_2d, Downsample, Upsample
-from .resnet import ResnetBlock
+from .resnet import Downsample, ResnetBlock, Upsample, downsample_2d, upfirdn2d, upsample_2d
 
 
 def _setup_kernel(k):
@@ -277,8 +276,6 @@ class NCSNpp(ModelMixin, ConfigMixin):
             skip_rescale=skip_rescale,
             continuous=continuous,
         )
-        self.act = act = nn.SiLU()
-
         self.nf = nf
         self.num_res_blocks = num_res_blocks
         self.attn_resolutions = attn_resolutions
@@ -421,9 +418,10 @@ class NCSNpp(ModelMixin, ConfigMixin):
         for i_level in reversed(range(self.num_resolutions)):
             for i_block in range(num_res_blocks + 1):
                 out_ch = nf * ch_mult[i_level]
+                in_ch = in_ch + hs_c.pop()
                 modules.append(
                     ResnetBlock(
-                        in_channels=in_ch + hs_c.pop(),
+                        in_channels=in_ch,
                         out_channels=out_ch,
                         temb_channels=4 * nf,
                         output_scale_factor=np.sqrt(2.0),