diff --git a/mmagic/models/editors/controlnet/controlnet.py b/mmagic/models/editors/controlnet/controlnet.py
index dfce2c44ed..be23afcc07 100644
--- a/mmagic/models/editors/controlnet/controlnet.py
+++ b/mmagic/models/editors/controlnet/controlnet.py
@@ -334,7 +334,7 @@ def prepare_control(image: Tuple[Image.Image, List[Image.Image], Tensor,
         Args:
             image (Tuple[Image.Image, List[Image.Image], Tensor, List[Tensor]]):  # noqa
                 The input image for control.
-            batch_size (int): The batch size of the control. The control will
+            batch_size (int): The number of the prompt. The control will
                 be repeated for `batch_size` times.
             num_images_per_prompt (int): The number images generate for one
                 prompt.
@@ -364,8 +364,11 @@ def prepare_control(image: Tuple[Image.Image, List[Image.Image], Tensor,
         image_batch_size = image.shape[0]
 
         if image_batch_size == 1:
-            repeat_by = batch_size
+            repeat_by = batch_size * num_images_per_prompt
         else:
+            assert image_batch_size == batch_size, (
+                'The number of Control condition must be 1 or equal to the '
+                'number of prompt.')
             # image batch size is the same as prompt batch size
             repeat_by = num_images_per_prompt
 
diff --git a/tests/test_models/test_editors/test_controlnet/test_controlnet.py b/tests/test_models/test_editors/test_controlnet/test_controlnet.py
index 0263a7528e..d29bc1ebcf 100644
--- a/tests/test_models/test_editors/test_controlnet/test_controlnet.py
+++ b/tests/test_models/test_editors/test_controlnet/test_controlnet.py
@@ -92,24 +92,49 @@ def test_init_weights(self):
 
     def test_infer(self):
         control_sd = self.control_sd
-        control = torch.ones([1, 3, 64, 64])
 
-        def mock_encode_prompt(*args, **kwargs):
-            return torch.randn(2, 5, 16)  # 2 for cfg
+        def mock_encode_prompt(prompt, do_classifier_free_guidance,
+                               num_images_per_prompt, *args, **kwargs):
+            batch_size = len(prompt) if isinstance(prompt, list) else 1
+            batch_size *= num_images_per_prompt
+            if do_classifier_free_guidance:
+                batch_size *= 2
+            return torch.randn(batch_size, 5, 16)  # 2 for cfg
 
         encode_prompt = control_sd._encode_prompt
         control_sd._encode_prompt = mock_encode_prompt
 
+        # one prompt, one control, repeat 1 time
+        self._test_infer(control_sd, 1, 1, 1, 1)
+
+        # two prompt, two control, repeat 1 time
+        # NOTE: skip this due to memory limit
+        # self._test_infer(control_sd, 2, 2, 1, 2)
+
+        # one prompt, one control, repeat 2 times
+        # NOTE: skip this due to memory limit
+        # self._test_infer(control_sd, 1, 1, 2, 2)
+
+        # two prompt, two control, repeat 2 times
+        # NOTE: skip this due to memory limit
+        # self._test_infer(control_sd, 2, 2, 2, 4)
+
+        control_sd._encode_prompt = encode_prompt
+
+    def _test_infer(self, control_sd, num_prompt, num_control, num_repeat,
+                    tar_shape):
+        prompt = ''
+        control = torch.ones([1, 3, 64, 64])
+
         result = control_sd.infer(
-            'an insect robot preparing a delicious meal',
-            control=control,
+            [prompt] * num_prompt,
+            control=[control] * num_control,
             height=64,
             width=64,
+            num_images_per_prompt=num_repeat,
             num_inference_steps=1,
             return_type='numpy')
-        assert result['samples'].shape == (1, 3, 64, 64)
-
-        control_sd._encode_prompt = encode_prompt
+        assert result['samples'].shape == (tar_shape, 3, 64, 64)
 
     def test_val_step(self):
         control_sd = self.control_sd
@@ -126,13 +151,20 @@ def test_val_step(self):
         def mock_encode_prompt(*args, **kwargs):
             return torch.randn(2, 5, 16)  # 2 for cfg
 
+        def mock_infer(*args, **kwargs):
+            return dict(samples=torch.randn(2, 3, 64, 64))
+
         encode_prompt = control_sd._encode_prompt
         control_sd._encode_prompt = mock_encode_prompt
 
+        infer = control_sd.infer
+        control_sd.infer = mock_infer
+
         # control_sd.text_encoder = mock_text_encoder()
         output = control_sd.val_step(data)
         assert len(output) == 1
         control_sd._encode_prompt = encode_prompt
+        control_sd.infer = infer
 
     def test_test_step(self):
         control_sd = self.control_sd
@@ -149,13 +181,20 @@ def test_test_step(self):
         def mock_encode_prompt(*args, **kwargs):
             return torch.randn(2, 5, 16)  # 2 for cfg
 
+        def mock_infer(*args, **kwargs):
+            return dict(samples=torch.randn(2, 3, 64, 64))
+
         encode_prompt = control_sd._encode_prompt
         control_sd._encode_prompt = mock_encode_prompt
 
+        infer = control_sd.infer
+        control_sd.infer = mock_infer
+
         # control_sd.text_encoder = mock_text_encoder()
         output = control_sd.test_step(data)
         assert len(output) == 1
         control_sd._encode_prompt = encode_prompt
+        control_sd.infer = infer
 
     def test_train_step(self):
         control_sd = self.control_sd