up

src/diffusers/models/resnet.py

@@ -207,9 +207,6 @@ class ResBlock(TimestepBlock):
 
         self.updown = up or down
 
-#        if self.updown:
-#            import ipdb; ipdb.set_trace()
-
         if up:
             self.h_upd = Upsample(channels, use_conv=False, dims=dims)
             self.x_upd = Upsample(channels, use_conv=False, dims=dims)
@@ -227,8 +224,10 @@ class ResBlock(TimestepBlock):
             ),
         )
         self.out_layers = nn.Sequential(
-            normalization(self.out_channels, swish=0.0 if use_scale_shift_norm else 1.0),
-            nn.SiLU() if use_scale_shift_norm else nn.Identity(),
+#            normalization(self.out_channels, swish=0.0 if use_scale_shift_norm else 1.0),
+#            nn.SiLU() if use_scale_shift_norm else nn.Identity(),
+            normalization(self.out_channels, swish=0.0),
+            nn.SiLU(),
             nn.Dropout(p=dropout),
             zero_module(conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)),
         )
@@ -322,6 +321,7 @@ class ResBlock(TimestepBlock):
         emb_out = self.emb_layers(emb).type(h.dtype)
         while len(emb_out.shape) < len(h.shape):
             emb_out = emb_out[..., None]
+
         if self.use_scale_shift_norm:
             out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
             scale, shift = torch.chunk(emb_out, 2, dim=1)
@@ -338,35 +338,31 @@ class ResBlock(TimestepBlock):
 
         return result
 
-    def forward_2(self, x, temb, mask=1.0):
+    def forward_2(self, x, temb):
         if self.overwrite and not self.is_overwritten:
             self.set_weights()
             self.is_overwritten = True
 
         h = x
-        if self.pre_norm:
         h = self.norm1(h)
         h = self.nonlinearity(h)
 
         h = self.conv1(h)
 
-        if not self.pre_norm:
-            h = self.norm1(h)
-            h = self.nonlinearity(h)
+        temb = self.temb_proj(self.nonlinearity(temb))[:, :, None, None]
 
-        h = h + self.temb_proj(self.nonlinearity(temb))[:, :, None, None]
+        scale, shift = torch.chunk(temb, 2, dim=1)
+
+        h = self.norm2(h)
+        h = h * scale + shift
 
-        if self.pre_norm:
         h = self.norm2(h)
+
         h = self.nonlinearity(h)
 
         h = self.dropout(h)
         h = self.conv2(h)
 
-        if not self.pre_norm:
-            h = self.norm2(h)
-            h = self.nonlinearity(h)
-
         if self.in_channels != self.out_channels:
             if self.use_conv_shortcut:
                 x = self.conv_shortcut(x)
@@ -376,7 +372,7 @@ class ResBlock(TimestepBlock):
         return x + h
 
 
-# unet.py and unet_grad_tts.py
+# unet.py, unet_grad_tts.py, unet_ldm.py
 class ResnetBlock(nn.Module):
     def __init__(
         self,
@@ -410,6 +406,7 @@ class ResnetBlock(nn.Module):
         self.norm2 = Normalize(out_channels, num_groups=groups, eps=eps)
         self.dropout = torch.nn.Dropout(dropout)
         self.conv2 = torch.nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
+
         if non_linearity == "swish":
             self.nonlinearity = nonlinearity
         elif non_linearity == "mish":