feat: rename single-letter vars in resnet.py (#3868)

feat: rename single-letter vars

Author: SauravMaheshkar

src/diffusers/models/resnet.py

@@ -95,9 +95,9 @@ def __init__(self, channels, use_conv=False, out_channels=None, padding=1, name=
             assert self.channels == self.out_channels
             self.conv = nn.AvgPool1d(kernel_size=stride, stride=stride)
 
-    def forward(self, x):
-        assert x.shape[1] == self.channels
-        return self.conv(x)
+    def forward(self, inputs):
+        assert inputs.shape[1] == self.channels
+        return self.conv(inputs)
 
 
 class Upsample2D(nn.Module):
@@ -431,13 +431,13 @@ def __init__(self, pad_mode="reflect"):
         self.pad = kernel_1d.shape[1] // 2 - 1
         self.register_buffer("kernel", kernel_1d.T @ kernel_1d, persistent=False)
 
-    def forward(self, x):
-        x = F.pad(x, (self.pad,) * 4, self.pad_mode)
-        weight = x.new_zeros([x.shape[1], x.shape[1], self.kernel.shape[0], self.kernel.shape[1]])
-        indices = torch.arange(x.shape[1], device=x.device)
-        kernel = self.kernel.to(weight)[None, :].expand(x.shape[1], -1, -1)
+    def forward(self, inputs):
+        inputs = F.pad(inputs, (self.pad,) * 4, self.pad_mode)
+        weight = inputs.new_zeros([inputs.shape[1], inputs.shape[1], self.kernel.shape[0], self.kernel.shape[1]])
+        indices = torch.arange(inputs.shape[1], device=inputs.device)
+        kernel = self.kernel.to(weight)[None, :].expand(inputs.shape[1], -1, -1)
         weight[indices, indices] = kernel
-        return F.conv2d(x, weight, stride=2)
+        return F.conv2d(inputs, weight, stride=2)
 
 
 class KUpsample2D(nn.Module):
@@ -448,13 +448,13 @@ def __init__(self, pad_mode="reflect"):
         self.pad = kernel_1d.shape[1] // 2 - 1
         self.register_buffer("kernel", kernel_1d.T @ kernel_1d, persistent=False)
 
-    def forward(self, x):
-        x = F.pad(x, ((self.pad + 1) // 2,) * 4, self.pad_mode)
-        weight = x.new_zeros([x.shape[1], x.shape[1], self.kernel.shape[0], self.kernel.shape[1]])
-        indices = torch.arange(x.shape[1], device=x.device)
-        kernel = self.kernel.to(weight)[None, :].expand(x.shape[1], -1, -1)
+    def forward(self, inputs):
+        inputs = F.pad(inputs, ((self.pad + 1) // 2,) * 4, self.pad_mode)
+        weight = inputs.new_zeros([inputs.shape[1], inputs.shape[1], self.kernel.shape[0], self.kernel.shape[1]])
+        indices = torch.arange(inputs.shape[1], device=inputs.device)
+        kernel = self.kernel.to(weight)[None, :].expand(inputs.shape[1], -1, -1)
         weight[indices, indices] = kernel
-        return F.conv_transpose2d(x, weight, stride=2, padding=self.pad * 2 + 1)
+        return F.conv_transpose2d(inputs, weight, stride=2, padding=self.pad * 2 + 1)
 
 
 class ResnetBlock2D(nn.Module):
@@ -664,13 +664,13 @@ def __init__(self, inp_channels, out_channels, kernel_size, n_groups=8):
         self.group_norm = nn.GroupNorm(n_groups, out_channels)
         self.mish = nn.Mish()
 
-    def forward(self, x):
-        x = self.conv1d(x)
-        x = rearrange_dims(x)
-        x = self.group_norm(x)
-        x = rearrange_dims(x)
-        x = self.mish(x)
-        return x
+    def forward(self, inputs):
+        intermediate_repr = self.conv1d(inputs)
+        intermediate_repr = rearrange_dims(intermediate_repr)
+        intermediate_repr = self.group_norm(intermediate_repr)
+        intermediate_repr = rearrange_dims(intermediate_repr)
+        output = self.mish(intermediate_repr)
+        return output
 
 
 # unet_rl.py
@@ -687,20 +687,20 @@ def __init__(self, inp_channels, out_channels, embed_dim, kernel_size=5):
             nn.Conv1d(inp_channels, out_channels, 1) if inp_channels != out_channels else nn.Identity()
         )
 
-    def forward(self, x, t):
+    def forward(self, inputs, t):
         """
         Args:
-            x : [ batch_size x inp_channels x horizon ]
+            inputs : [ batch_size x inp_channels x horizon ]
             t : [ batch_size x embed_dim ]
 
         returns:
             out : [ batch_size x out_channels x horizon ]
         """
         t = self.time_emb_act(t)
         t = self.time_emb(t)
-        out = self.conv_in(x) + rearrange_dims(t)
+        out = self.conv_in(inputs) + rearrange_dims(t)
         out = self.conv_out(out)
-        return out + self.residual_conv(x)
+        return out + self.residual_conv(inputs)
 
 
 def upsample_2d(hidden_states, kernel=None, factor=2, gain=1):