[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

tests/inferers/test_controlnet_inferers.py

@@ -587,7 +587,7 @@ def test_sampler_conditioned(self, model_params, controlnet_params, input_shape)
         controlnet.eval()
         mask = torch.randn(input_shape).to(device)
         noise = torch.randn(input_shape).to(device)
-        
+
         # DDIM
         scheduler = DDIMScheduler(num_train_timesteps=1000)
         inferer = ControlNetDiffusionInferer(scheduler=scheduler)
@@ -759,7 +759,7 @@ def test_prediction_shape(
             inferer = ControlNetLatentDiffusionInferer(scheduler=scheduler, scale_factor=1.0)
             scheduler.set_timesteps(num_inference_steps=10)
             timesteps = torch.randint(0, scheduler.num_train_timesteps, (input_shape[0],), device=input.device).long()
-    
+
             if dm_model_type == "SPADEDiffusionModelUNet":
                 input_shape_seg = list(input_shape)
                 if "label_nc" in stage_2_params.keys():

tests/networks/schedulers/test_scheduler_rflow.py

@@ -54,7 +54,7 @@ def test_add_noise(self, input_param, input_shape, expected_shape):
 
     @parameterized.expand(TEST_CASES)
     def test_step_shape(self, input_param, input_shape, expected_shape):
-        scheduler = RFlowScheduler(**input_param)        
+        scheduler = RFlowScheduler(**input_param)
         model_output = torch.randn(input_shape)
         sample = torch.randn(input_shape)
         scheduler.set_timesteps(num_inference_steps=100, input_img_size_numel=torch.numel(sample[0,0,...]))
@@ -64,7 +64,7 @@ def test_step_shape(self, input_param, input_shape, expected_shape):
 
     @parameterized.expand(TEST_FULl_LOOP)
     def test_full_timestep_loop(self, input_param, input_shape, expected_output):
-        scheduler = RFlowScheduler(**input_param)        
+        scheduler = RFlowScheduler(**input_param)
         torch.manual_seed(42)
         model_output = torch.randn(input_shape)
         sample = torch.randn(input_shape)