diff --git a/mmagic/models/editors/consistency_models/consistencymodel_modules.py b/mmagic/models/editors/consistency_models/consistencymodel_modules.py
index 486f37299..70376cdd9 100644
--- a/mmagic/models/editors/consistency_models/consistencymodel_modules.py
+++ b/mmagic/models/editors/consistency_models/consistencymodel_modules.py
@@ -235,13 +235,13 @@ def __init__(
             self.convert_to_fp16()
 
     def convert_to_fp16(self):
-        """Convert the torso of the model to float16."""
+        """Convert the tensor of the model to float16."""
         self.input_blocks.apply(convert_module_to_f16)
         self.middle_block.apply(convert_module_to_f16)
         self.output_blocks.apply(convert_module_to_f16)
 
     def convert_to_fp32(self):
-        """Convert the torso of the model to float32."""
+        """Convert the tensor of the model to float32."""
         self.input_blocks.apply(convert_module_to_f32)
         self.middle_block.apply(convert_module_to_f32)
         self.output_blocks.apply(convert_module_to_f32)
diff --git a/tests/test_models/test_editors/test_consistency_models/test_consistency_model_utils.py b/tests/test_models/test_editors/test_consistency_models/test_consistency_model_utils.py
index 2c822f3e6..400ec5edc 100644
--- a/tests/test_models/test_editors/test_consistency_models/test_consistency_model_utils.py
+++ b/tests/test_models/test_editors/test_consistency_models/test_consistency_model_utils.py
@@ -69,6 +69,8 @@ def test_karras_sample(self):
             model_kwargs=model_kwargs)
         assert sample.shape == (batch_size, channel_num, image_size,
                                 image_size)
+        unet.convert_to_fp32()
+        unet.convert_to_fp16()
 
     def test_get_generator(self):
         self.assertIsInstance(get_generator('dummy'), DummyGenerator)
@@ -122,6 +124,7 @@ def denoiser(x_t, sigma):
         generator_list = ['dummy', 'determ', 'determ-indiv']
         for generator_str in generator_list:
             generator = get_generator(generator_str, 4, 0)
+            generator.randint(1, 2, (1, 2), dtype=torch.long, device='cpu')
             x_T = generator.randn(*shape, device=device) * sigma_max
             for sample in sample_list:
                 if sample == 'progdist':