Changed variable name from "h" to "hidden_states"

src/diffusers/models/resnet.py

@@ -328,39 +328,39 @@ def __init__(
         if self.use_nin_shortcut:
             self.conv_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
 
-    def forward(self, x, temb, hey=False):
-        h = x
+    def forward(self, x, temb):
+        hidden_states = x
 
         # make sure hidden states is in float32
         # when running in half-precision
-        h = self.norm1(h.float()).type(h.dtype)
-        h = self.nonlinearity(h)
+        hidden_states = self.norm1(hidden_states.float()).type(hidden_states.dtype)
+        hidden_states = self.nonlinearity(hidden_states)
 
         if self.upsample is not None:
             x = self.upsample(x)
-            h = self.upsample(h)
+            hidden_states = self.upsample(hidden_states)
         elif self.downsample is not None:
             x = self.downsample(x)
-            h = self.downsample(h)
+            hidden_states = self.downsample(hidden_states)
 
-        h = self.conv1(h)
+        hidden_states = self.conv1(hidden_states)
 
         if temb is not None:
             temb = self.time_emb_proj(self.nonlinearity(temb))[:, :, None, None]
-            h = h + temb
+            hidden_states = hidden_states + temb
 
         # make sure hidden states is in float32
         # when running in half-precision
-        h = self.norm2(h.float()).type(h.dtype)
-        h = self.nonlinearity(h)
+        hidden_states = self.norm2(hidden_states.float()).type(hidden_states.dtype)
+        hidden_states = self.nonlinearity(hidden_states)
 
-        h = self.dropout(h)
-        h = self.conv2(h)
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.conv2(hidden_states)
 
         if self.conv_shortcut is not None:
             x = self.conv_shortcut(x)
 
-        out = (x + h) / self.output_scale_factor
+        out = (x + hidden_states) / self.output_scale_factor
 
         return out