diff --git a/src/transformers/models/mllama/modeling_mllama.py b/src/transformers/models/mllama/modeling_mllama.py
index 97dd32b7215..c28671c81f8 100644
--- a/src/transformers/models/mllama/modeling_mllama.py
+++ b/src/transformers/models/mllama/modeling_mllama.py
@@ -1699,11 +1699,6 @@ class MllamaModel(MllamaPreTrainedModel):
         if (input_ids is None) ^ (inputs_embeds is not None):
             raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
 
-        if pixel_values is not None and inputs_embeds is not None:
-            raise ValueError(
-                "You cannot specify both pixel_values and inputs_embeds at the same time, and must specify either one"
-            )
-
         if pixel_values is not None and cross_attention_states is not None:
             raise ValueError("`pixel_values` and `cross_attention_states` cannot be provided simultaneously")
 
diff --git a/tests/models/mllama/test_modeling_mllama.py b/tests/models/mllama/test_modeling_mllama.py
index 589cff4c02b..eed367f3aa3 100644
--- a/tests/models/mllama/test_modeling_mllama.py
+++ b/tests/models/mllama/test_modeling_mllama.py
@@ -285,49 +285,6 @@ class MllamaForConditionalGenerationModelTest(ModelTesterMixin, GenerationTester
     def test_config(self):
         self.config_tester.run_common_tests()
 
-    # overwrite inputs_embeds tests because we need to delete "pixel values" for LVLMs
-    def test_inputs_embeds(self):
-        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
-
-        for model_class in self.all_model_classes:
-            model = model_class(config)
-            model.to(torch_device)
-            model.eval()
-
-            inputs = self._prepare_for_class(inputs_dict, model_class)
-
-            input_ids = inputs["input_ids"]
-            del inputs["input_ids"]
-            del inputs["pixel_values"]
-
-            wte = model.get_input_embeddings()
-            inputs["inputs_embeds"] = wte(input_ids)
-
-            with torch.no_grad():
-                model(**inputs)
-
-    # overwrite inputs_embeds tests because we need to delete "pixel values" for LVLMs
-    # while some other models require pixel_values to be present
-    def test_inputs_embeds_matches_input_ids(self):
-        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
-
-        for model_class in self.all_model_classes:
-            model = model_class(config)
-            model.to(torch_device)
-            model.eval()
-
-            inputs = self._prepare_for_class(inputs_dict, model_class)
-            input_ids = inputs["input_ids"]
-            del inputs["input_ids"]
-            del inputs["pixel_values"]
-
-            inputs_embeds = model.get_input_embeddings()(input_ids)
-
-            with torch.no_grad():
-                out_ids = model(input_ids=input_ids, **inputs)[0]
-                out_embeds = model(inputs_embeds=inputs_embeds, **inputs)[0]
-            torch.testing.assert_close(out_embeds, out_ids)
-
     def test_resize_embeddings_results_in_successful_loss(self):
         # resizing embeddings should result in successful loss computation
         config, inputs = self.model_tester.prepare_config_and_inputs_for_common()