Use PyTorch's conventional inplace functions (#7332)

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

tests/pipelines/controlnet/test_controlnet.py

@@ -302,7 +302,7 @@ class StableDiffusionMultiControlNetPipelineFastTests(
 
         def init_weights(m):
             if isinstance(m, torch.nn.Conv2d):
-                torch.nn.init.normal(m.weight)
+                torch.nn.init.normal_(m.weight)
                 m.bias.data.fill_(1.0)
 
         controlnet1 = ControlNetModel(
@@ -519,7 +519,7 @@ class StableDiffusionMultiControlNetOneModelPipelineFastTests(
 
         def init_weights(m):
             if isinstance(m, torch.nn.Conv2d):
-                torch.nn.init.normal(m.weight)
+                torch.nn.init.normal_(m.weight)
                 m.bias.data.fill_(1.0)
 
         controlnet = ControlNetModel(

tests/pipelines/controlnet/test_controlnet_img2img.py

@@ -210,7 +210,7 @@ class StableDiffusionMultiControlNetPipelineFastTests(
 
         def init_weights(m):
             if isinstance(m, torch.nn.Conv2d):
-                torch.nn.init.normal(m.weight)
+                torch.nn.init.normal_(m.weight)
                 m.bias.data.fill_(1.0)
 
         controlnet1 = ControlNetModel(

tests/pipelines/controlnet/test_controlnet_inpaint.py

@@ -273,7 +273,7 @@ class MultiControlNetInpaintPipelineFastTests(
 
         def init_weights(m):
             if isinstance(m, torch.nn.Conv2d):
-                torch.nn.init.normal(m.weight)
+                torch.nn.init.normal_(m.weight)
                 m.bias.data.fill_(1.0)
 
         controlnet1 = ControlNetModel(

tests/pipelines/controlnet/test_controlnet_sdxl.py

@@ -503,7 +503,7 @@ class StableDiffusionXLMultiControlNetPipelineFastTests(
 
         def init_weights(m):
             if isinstance(m, torch.nn.Conv2d):
-                torch.nn.init.normal(m.weight)
+                torch.nn.init.normal_(m.weight)
                 m.bias.data.fill_(1.0)
 
         controlnet1 = ControlNetModel(
@@ -708,7 +708,7 @@ class StableDiffusionXLMultiControlNetOneModelPipelineFastTests(
 
         def init_weights(m):
             if isinstance(m, torch.nn.Conv2d):
-                torch.nn.init.normal(m.weight)
+                torch.nn.init.normal_(m.weight)
                 m.bias.data.fill_(1.0)
 
         controlnet = ControlNetModel(