[Tests] fix sharding tests (#8764)

fix sharding tests

src/diffusers/models/embeddings.py

@@ -415,9 +415,10 @@ class GaussianFourierProjection(nn.Module):
 
         if set_W_to_weight:
             # to delete later
+            del self.weight
             self.W = nn.Parameter(torch.randn(embedding_size) * scale, requires_grad=False)
-
             self.weight = self.W
+            del self.W
 
     def forward(self, x):
         if self.log:

tests/models/autoencoders/test_models_vae.py

@@ -361,9 +361,10 @@ class ConsistencyDecoderVAETests(ModelTesterMixin, unittest.TestCase):
     forward_requires_fresh_args = True
 
     def inputs_dict(self, seed=None):
-        generator = torch.Generator("cpu")
-        if seed is not None:
-            generator.manual_seed(0)
+        if seed is None:
+            generator = torch.Generator("cpu").manual_seed(0)
+        else:
+            generator = torch.Generator("cpu").manual_seed(seed)
         image = randn_tensor((4, 3, 32, 32), generator=generator, device=torch.device(torch_device))
 
         return {"sample": image, "generator": generator}

tests/models/test_modeling_common.py

@@ -905,11 +905,13 @@ class ModelTesterMixin:
             actual_num_shards = len([file for file in os.listdir(tmp_dir) if file.endswith(".safetensors")])
             self.assertTrue(actual_num_shards == expected_num_shards)
 
-            new_model = self.model_class.from_pretrained(tmp_dir)
+            new_model = self.model_class.from_pretrained(tmp_dir).eval()
             new_model = new_model.to(torch_device)
 
             torch.manual_seed(0)
+            _, inputs_dict = self.prepare_init_args_and_inputs_for_common()
             new_output = new_model(**inputs_dict)
+
             self.assertTrue(torch.allclose(base_output[0], new_output[0], atol=1e-5))
 
     @require_torch_gpu
@@ -940,6 +942,7 @@ class ModelTesterMixin:
             new_model = new_model.to(torch_device)
 
             torch.manual_seed(0)
+            _, inputs_dict = self.prepare_init_args_and_inputs_for_common()
             new_output = new_model(**inputs_dict)
             self.assertTrue(torch.allclose(base_output[0], new_output[0], atol=1e-5))