From 1f196a09fea0bb62308a31b66f1c398ff851959d Mon Sep 17 00:00:00 2001
From: Juan Carrasquilla <68667541+JC-swEng@users.noreply.github.com>
Date: Thu, 1 Sep 2022 04:31:02 -0500
Subject: [PATCH] Changed variable name from "h" to "hidden_states" (#285)

* Changed variable name from "h" to "hidden_states"

Per issue #198 , changed variable name from "h" to "hidden_states" in the forward function only. I am happy to change any other variable names, please advise recommended new names.

* Update src/diffusers/models/resnet.py

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
---
 src/diffusers/models/resnet.py | 26 +++++++++++++-------------
 1 file changed, 13 insertions(+), 13 deletions(-)

diff --git a/src/diffusers/models/resnet.py b/src/diffusers/models/resnet.py
index acce7b574e..50382bcab3 100644
--- a/src/diffusers/models/resnet.py
+++ b/src/diffusers/models/resnet.py
@@ -328,39 +328,39 @@ class ResnetBlock2D(nn.Module):
         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