make sure in_channels is not accessed directly

Author: patrickvonplaten

tests/fixtures/custom_pipeline/pipeline.py

@@ -73,7 +73,7 @@ def __call__(
 
         # Sample gaussian noise to begin loop
         image = torch.randn(
-            (batch_size, self.unet.in_channels, self.unet.sample_size, self.unet.sample_size),
+            (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size),
             generator=generator,
         )
         image = image.to(self.device)

tests/fixtures/custom_pipeline/what_ever.py

@@ -73,7 +73,7 @@ def __call__(
 
         # Sample gaussian noise to begin loop
         image = torch.randn(
-            (batch_size, self.unet.in_channels, self.unet.sample_size, self.unet.sample_size),
+            (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size),
             generator=generator,
         )
         image = image.to(self.device)

tests/models/test_models_unet_1d.py

@@ -116,7 +116,7 @@ def test_output_pretrained(self):
         if torch.cuda.is_available():
             torch.cuda.manual_seed_all(0)
 
-        num_features = model.in_channels
+        num_features = model.config.in_channels
         seq_len = 16
         noise = torch.randn((1, seq_len, num_features)).permute(
             0, 2, 1
@@ -264,7 +264,7 @@ def test_output_pretrained(self):
         if torch.cuda.is_available():
             torch.cuda.manual_seed_all(0)
 
-        num_features = value_function.in_channels
+        num_features = value_function.config.in_channels
         seq_len = 14
         noise = torch.randn((1, seq_len, num_features)).permute(
             0, 2, 1