Renamed x -> hidden_states in resnet.py

src/diffusers/models/resnet.py

@@ -34,21 +34,21 @@ def __init__(self, channels, use_conv=False, use_conv_transpose=False, out_chann
         else:
             self.Conv2d_0 = conv
 
-    def forward(self, x):
-        assert x.shape[1] == self.channels
+    def forward(self, hidden_states):
+        assert hidden_states.shape[1] == self.channels
         if self.use_conv_transpose:
-            return self.conv(x)
+            return self.conv(hidden_states)
 
-        x = F.interpolate(x, scale_factor=2.0, mode="nearest")
+        hidden_states = F.interpolate(hidden_states, scale_factor=2.0, mode="nearest")
 
         # TODO(Suraj, Patrick) - clean up after weight dicts are correctly renamed
         if self.use_conv:
             if self.name == "conv":
-                x = self.conv(x)
+                hidden_states = self.conv(hidden_states)
             else:
-                x = self.Conv2d_0(x)
+                hidden_states = self.Conv2d_0(hidden_states)
 
-        return x
+        return hidden_states
 
 
 class Downsample2D(nn.Module):
@@ -84,16 +84,16 @@ def __init__(self, channels, use_conv=False, out_channels=None, padding=1, name=
         else:
             self.conv = conv
 
-    def forward(self, x):
-        assert x.shape[1] == self.channels
+    def forward(self, hidden_states):
+        assert hidden_states.shape[1] == self.channels
         if self.use_conv and self.padding == 0:
             pad = (0, 1, 0, 1)
-            x = F.pad(x, pad, mode="constant", value=0)
+            hidden_states = F.pad(hidden_states, pad, mode="constant", value=0)
 
-        assert x.shape[1] == self.channels
-        x = self.conv(x)
+        assert hidden_states.shape[1] == self.channels
+        hidden_states = self.conv(hidden_states)
 
-        return x
+        return hidden_states
 
 
 class FirUpsample2D(nn.Module):
@@ -174,12 +174,12 @@ def _upsample_2d(self, x, weight=None, kernel=None, factor=2, gain=1):
 
         return x
 
-    def forward(self, x):
+    def forward(self, hidden_states):
         if self.use_conv:
-            height = self._upsample_2d(x, self.Conv2d_0.weight, kernel=self.fir_kernel)
+            height = self._upsample_2d(hidden_states, self.Conv2d_0.weight, kernel=self.fir_kernel)
             height = height + self.Conv2d_0.bias.reshape(1, -1, 1, 1)
         else:
-            height = self._upsample_2d(x, kernel=self.fir_kernel, factor=2)
+            height = self._upsample_2d(hidden_states, kernel=self.fir_kernel, factor=2)
 
         return height
 
@@ -236,14 +236,14 @@ def _downsample_2d(self, x, weight=None, kernel=None, factor=2, gain=1):
 
         return x
 
-    def forward(self, x):
+    def forward(self, hidden_states):
         if self.use_conv:
-            x = self._downsample_2d(x, weight=self.Conv2d_0.weight, kernel=self.fir_kernel)
-            x = x + self.Conv2d_0.bias.reshape(1, -1, 1, 1)
+            hidden_states = self._downsample_2d(hidden_states, weight=self.Conv2d_0.weight, kernel=self.fir_kernel)
+            hidden_states = hidden_states + self.Conv2d_0.bias.reshape(1, -1, 1, 1)
         else:
-            x = self._downsample_2d(x, kernel=self.fir_kernel, factor=2)
+            hidden_states = self._downsample_2d(hidden_states, kernel=self.fir_kernel, factor=2)
 
-        return x
+        return hidden_states
 
 
 class ResnetBlock2D(nn.Module):