renamed x to meaningful variable in resnet.py (#677)

* renamed single letter variables

* renamed x to meaningful variable in resnet.py

Hello @patil-suraj can you verify it
Thanks

* Reformatted using black

* renamed x to meaningful variable in resnet.py

Hello @patil-suraj can you verify it
Thanks

* reformatted the files

* modified unboundlocalerror in line 374

* removed referenced before error

* renamed single variable x -> hidden_state, p-> pad_value

Co-authored-by: Nikhil A V <[email protected]>
Co-authored-by: Patrick von Platen <[email protected]>
Co-authored-by: Suraj Patil <[email protected]>

Author: 4 people

src/diffusers/models/resnet.py

@@ -112,7 +112,7 @@ def __init__(self, channels=None, out_channels=None, use_conv=False, fir_kernel=
         self.fir_kernel = fir_kernel
         self.out_channels = out_channels
 
-    def _upsample_2d(self, x, weight=None, kernel=None, factor=2, gain=1):
+    def _upsample_2d(self, hidden_states, weight=None, kernel=None, factor=2, gain=1):
         """Fused `upsample_2d()` followed by `Conv2d()`.
 
         Args:
@@ -151,34 +151,46 @@ def _upsample_2d(self, x, weight=None, kernel=None, factor=2, gain=1):
             convW = weight.shape[3]
             inC = weight.shape[1]
 
-            p = (kernel.shape[0] - factor) - (convW - 1)
+            pad_value = (kernel.shape[0] - factor) - (convW - 1)
 
             stride = (factor, factor)
             # Determine data dimensions.
-            output_shape = ((x.shape[2] - 1) * factor + convH, (x.shape[3] - 1) * factor + convW)
+            output_shape = (
+                (hidden_states.shape[2] - 1) * factor + convH,
+                (hidden_states.shape[3] - 1) * factor + convW,
+            )
             output_padding = (
-                output_shape[0] - (x.shape[2] - 1) * stride[0] - convH,
-                output_shape[1] - (x.shape[3] - 1) * stride[1] - convW,
+                output_shape[0] - (hidden_states.shape[2] - 1) * stride[0] - convH,
+                output_shape[1] - (hidden_states.shape[3] - 1) * stride[1] - convW,
             )
             assert output_padding[0] >= 0 and output_padding[1] >= 0
             inC = weight.shape[1]
-            num_groups = x.shape[1] // inC
+            num_groups = hidden_states.shape[1] // inC
 
             # Transpose weights.
             weight = torch.reshape(weight, (num_groups, -1, inC, convH, convW))
             weight = torch.flip(weight, dims=[3, 4]).permute(0, 2, 1, 3, 4)
             weight = torch.reshape(weight, (num_groups * inC, -1, convH, convW))
 
-            x = F.conv_transpose2d(x, weight, stride=stride, output_padding=output_padding, padding=0)
+            inverse_conv = F.conv_transpose2d(
+                hidden_states, weight, stride=stride, output_padding=output_padding, padding=0
+            )
 
-            x = upfirdn2d_native(x, torch.tensor(kernel, device=x.device), pad=((p + 1) // 2 + factor - 1, p // 2 + 1))
+            output = upfirdn2d_native(
+                inverse_conv,
+                torch.tensor(kernel, device=inverse_conv.device),
+                pad=((pad_value + 1) // 2 + factor - 1, pad_value // 2 + 1),
+            )
         else:
-            p = kernel.shape[0] - factor
-            x = upfirdn2d_native(
-                x, torch.tensor(kernel, device=x.device), up=factor, pad=((p + 1) // 2 + factor - 1, p // 2)
+            pad_value = kernel.shape[0] - factor
+            output = upfirdn2d_native(
+                hidden_states,
+                torch.tensor(kernel, device=hidden_states.device),
+                up=factor,
+                pad=((pad_value + 1) // 2 + factor - 1, pad_value // 2),
             )
 
-        return x
+        return output
 
     def forward(self, hidden_states):
         if self.use_conv:
@@ -200,7 +212,7 @@ def __init__(self, channels=None, out_channels=None, use_conv=False, fir_kernel=
         self.use_conv = use_conv
         self.out_channels = out_channels
 
-    def _downsample_2d(self, x, weight=None, kernel=None, factor=2, gain=1):
+    def _downsample_2d(self, hidden_states, weight=None, kernel=None, factor=2, gain=1):
         """Fused `Conv2d()` followed by `downsample_2d()`.
 
         Args:
@@ -232,20 +244,29 @@ def _downsample_2d(self, x, weight=None, kernel=None, factor=2, gain=1):
 
         if self.use_conv:
             _, _, convH, convW = weight.shape
-            p = (kernel.shape[0] - factor) + (convW - 1)
-            s = [factor, factor]
-            x = upfirdn2d_native(x, torch.tensor(kernel, device=x.device), pad=((p + 1) // 2, p // 2))
-            x = F.conv2d(x, weight, stride=s, padding=0)
+            pad_value = (kernel.shape[0] - factor) + (convW - 1)
+            stride_value = [factor, factor]
+            upfirdn_input = upfirdn2d_native(
+                hidden_states,
+                torch.tensor(kernel, device=hidden_states.device),
+                pad=((pad_value + 1) // 2, pad_value // 2),
+            )
+            hidden_states = F.conv2d(upfirdn_input, weight, stride=stride_value, padding=0)
         else:
-            p = kernel.shape[0] - factor
-            x = upfirdn2d_native(x, torch.tensor(kernel, device=x.device), down=factor, pad=((p + 1) // 2, p // 2))
+            pad_value = kernel.shape[0] - factor
+            hidden_states = upfirdn2d_native(
+                hidden_states,
+                torch.tensor(kernel, device=hidden_states.device),
+                down=factor,
+                pad=((pad_value + 1) // 2, pad_value // 2),
+            )
 
-        return x
+        return hidden_states
 
     def forward(self, hidden_states):
         if self.use_conv:
-            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)
+            downsample_input = self._downsample_2d(hidden_states, weight=self.Conv2d_0.weight, kernel=self.fir_kernel)
+            hidden_states = downsample_input + self.Conv2d_0.bias.reshape(1, -1, 1, 1)
         else:
             hidden_states = self._downsample_2d(hidden_states, kernel=self.fir_kernel, factor=2)
 
@@ -332,17 +353,17 @@ def __init__(
         if self.use_in_shortcut:
             self.conv_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
 
-    def forward(self, x, temb):
-        hidden_states = x
+    def forward(self, input_tensor, temb):
+        hidden_states = input_tensor
 
         hidden_states = self.norm1(hidden_states)
         hidden_states = self.nonlinearity(hidden_states)
 
         if self.upsample is not None:
-            x = self.upsample(x)
+            input_tensor = self.upsample(input_tensor)
             hidden_states = self.upsample(hidden_states)
         elif self.downsample is not None:
-            x = self.downsample(x)
+            input_tensor = self.downsample(input_tensor)
             hidden_states = self.downsample(hidden_states)
 
         hidden_states = self.conv1(hidden_states)
@@ -358,19 +379,19 @@ def forward(self, x, temb):
         hidden_states = self.conv2(hidden_states)
 
         if self.conv_shortcut is not None:
-            x = self.conv_shortcut(x)
+            input_tensor = self.conv_shortcut(input_tensor)
 
-        out = (x + hidden_states) / self.output_scale_factor
+        output_tensor = (input_tensor + hidden_states) / self.output_scale_factor
 
-        return out
+        return output_tensor
 
 
 class Mish(torch.nn.Module):
-    def forward(self, x):
-        return x * torch.tanh(torch.nn.functional.softplus(x))
+    def forward(self, hidden_states):
+        return hidden_states * torch.tanh(torch.nn.functional.softplus(hidden_states))
 
 
-def upsample_2d(x, kernel=None, factor=2, gain=1):
+def upsample_2d(hidden_states, kernel=None, factor=2, gain=1):
     r"""Upsample2D a batch of 2D images with the given filter.
 
     Args:
@@ -397,11 +418,16 @@ def upsample_2d(x, kernel=None, factor=2, gain=1):
     kernel /= torch.sum(kernel)
 
     kernel = kernel * (gain * (factor**2))
-    p = kernel.shape[0] - factor
-    return upfirdn2d_native(x, kernel.to(device=x.device), up=factor, pad=((p + 1) // 2 + factor - 1, p // 2))
+    pad_value = kernel.shape[0] - factor
+    return upfirdn2d_native(
+        hidden_states,
+        kernel.to(device=hidden_states.device),
+        up=factor,
+        pad=((pad_value + 1) // 2 + factor - 1, pad_value // 2),
+    )
 
 
-def downsample_2d(x, kernel=None, factor=2, gain=1):
+def downsample_2d(hidden_states, kernel=None, factor=2, gain=1):
     r"""Downsample2D a batch of 2D images with the given filter.
 
     Args:
@@ -429,8 +455,10 @@ def downsample_2d(x, kernel=None, factor=2, gain=1):
     kernel /= torch.sum(kernel)
 
     kernel = kernel * gain
-    p = kernel.shape[0] - factor
-    return upfirdn2d_native(x, kernel.to(device=x.device), down=factor, pad=((p + 1) // 2, p // 2))
+    pad_value = kernel.shape[0] - factor
+    return upfirdn2d_native(
+        hidden_states, kernel.to(device=hidden_states.device), down=factor, pad=((pad_value + 1) // 2, pad_value // 2)
+    )
 
 
 def upfirdn2d_native(input, kernel, up=1, down=1, pad=(0, 0)):
@@ -441,6 +469,7 @@ def upfirdn2d_native(input, kernel, up=1, down=1, pad=(0, 0)):
 
     _, channel, in_h, in_w = input.shape
     input = input.reshape(-1, in_h, in_w, 1)
+    # Rename this variable (input); it shadows a builtin.sonarlint(python:S5806)
 
     _, in_h, in_w, minor = input.shape
     kernel_h, kernel_w = kernel.shape