Fix #16 app.py

app.py

@@ -13,31 +13,24 @@ from huggingface_hub import hf_hub_download
 class ResBlk(nn.Module):
     def __init__(self, dim_in, dim_out, normalize=False, downsample=False):
         super().__init__()
-        self.conv1 = nn.Conv2d(dim_in, dim_out, 3, 1, 1)
-        self.norm1 = nn.InstanceNorm2d(dim_out, affine=True) if normalize else None
-        self.relu1 = nn.ReLU(inplace=True)
-        self.conv2 = nn.Conv2d(dim_out, dim_out, 3, 1, 1)
-        self.norm2 = nn.InstanceNorm2d(dim_out, affine=True) if normalize else None
-        self.relu2 = nn.ReLU(inplace=True)
+        self.normalize = normalize
         self.downsample = downsample
-        if self.downsample:
-            self.avg_pool = nn.AvgPool2d(2)
+        self.main = nn.Sequential(
+            nn.Conv2d(dim_in, dim_out, 3, 1, 1),
+            nn.InstanceNorm2d(dim_out, affine=True) if normalize else nn.Identity(),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(dim_out, dim_out, 3, 1, 1),
+            nn.InstanceNorm2d(dim_out, affine=True) if normalize else nn.Identity()
+        )
+        self.downsample_layer = nn.AvgPool2d(2) if downsample else nn.Identity()
+        self.skip = nn.Conv2d(dim_in, dim_out, 1, 1, 0, bias=False)
 
     def forward(self, x):
-        residual = x
-        out = self.conv1(x)
-        if self.norm1:
-            out = self.norm1(out)
-        out = self.relu1(out)
-        out = self.conv2(out) # <--- Corrección aquí
-        if self.norm2:
-            out = self.norm2(out)
-        out = self.relu2(out)
-        if self.downsample:
-            out = self.avg_pool(out)
-            residual = self.avg_pool(residual)
-        out = out + residual
-        return out
+        out = self.main(x)
+        out = self.downsample_layer(out)
+        skip = self.skip(x)
+        skip = self.downsample_layer(skip)
+        return (out + skip) / math.sqrt(2)
 
 class AdainResBlk(nn.Module):
     def __init__(self, dim_in, dim_out, style_dim=64, w_hpf=1, upsample=False):