Remove the redundant line from the adapter.py file. (#5618)

* I added a new doc string to the class. This is more flexible to understanding other developers what are doing and where it's using.

* Update src/diffusers/models/unet_2d_blocks.py

This changes suggest by maintener.
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* Update src/diffusers/models/unet_2d_blocks.py

Add suggested text
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* Update unet_2d_blocks.py

I changed the Parameter to Args text.

* Update unet_2d_blocks.py

proper indentation set in this file.

* Update unet_2d_blocks.py

a little bit of change in the act_fun argument line.

* I run the black command to reformat style in the code

* Update unet_2d_blocks.py

similar doc-string add to have in the original diffusion repository.

* I removed the dummy variable defined in both the encoder and decoder.

* Now, I run black package to reformat my file

* Remove the redundant line from the adapter.py file.

* Black package using to reformated my file

---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>

src/diffusers/models/adapter.py

@@ -456,9 +456,8 @@ class AdapterResnetBlock(nn.Module):
         This method takes input tensor x and applies a convolutional layer, ReLU activation, and another convolutional
         layer on the input tensor. It returns addition with the input tensor.
         """
-        h = x
-        h = self.block1(h)
-        h = self.act(h)
+
+        h = self.act(self.block1(x))
         h = self.block2(h)
 
         return h + x
@@ -578,9 +577,8 @@ class LightAdapterResnetBlock(nn.Module):
         This function takes input tensor x and processes it through one convolutional layer, ReLU activation, and
         another convolutional layer and adds it to input tensor.
         """
-        h = x
-        h = self.block1(h)
-        h = self.act(h)
+
+        h = self.act(self.block1(x))
         h = self.block2(h)
 
         return h + x