diff --git a/python/tvm/relay/frontend/onnx.py b/python/tvm/relay/frontend/onnx.py
index 13609704ccb7..9934d4f13269 100644
--- a/python/tvm/relay/frontend/onnx.py
+++ b/python/tvm/relay/frontend/onnx.py
@@ -4510,14 +4510,19 @@ def _impl_v1(cls, inputs, attr, params):
         # Add constants from both branches to parent graph.
         graph_scope._params.update(then_graph._params)
         graph_scope._nodes.update(then_graph._nodes)
+        graph_scope._params.update(else_graph._params)
+        graph_scope._nodes.update(else_graph._nodes)
+
         then_free_vars = analysis.free_vars(then_expr)
         for var in then_free_vars:
             graph_scope._nodes.update({var.name_hint: var})
-        graph_scope._params.update(else_graph._params)
-        graph_scope._nodes.update(else_graph._nodes)
+            if var.name_hint in graph_scope._inputs:
+                graph_scope._inputs.update({var.name_hint: var})
         else_free_vars = analysis.free_vars(else_expr)
         for var in else_free_vars:
             graph_scope._nodes.update({var.name_hint: var})
+            if var.name_hint in graph_scope._inputs:
+                graph_scope._inputs.update({var.name_hint: var})
 
         # Sometimes pytorch to onnx will insert silly if statements that produce dynamic ranks.
         # Often these dont contribute anything. If we see a dynamic rank output, try to unify
diff --git a/tests/python/frontend/onnx/test_forward.py b/tests/python/frontend/onnx/test_forward.py
index 216732343028..b9f2d14b7888 100644
--- a/tests/python/frontend/onnx/test_forward.py
+++ b/tests/python/frontend/onnx/test_forward.py
@@ -5147,6 +5147,101 @@ def append_constant_nodes(nodes, outputs, expected, name):
     verify_if(cond_array=True, num_outputs=2)
 
 
+@tvm.testing.parametrize_targets
+def test_graph_input_use_in_if(target, dev):
+    """test_graph_input_use_in_if"""
+
+    def verify_if(num_nested, cond):
+        # return "graph input" if cond is True, else return constant(-1).
+
+        input_tensor = helper.make_tensor_value_info("graph_input", TensorProto.FLOAT, [1])
+        output_tensor = helper.make_tensor_value_info("graph_output", TensorProto.FLOAT, [1])
+        constant_node = make_constant_node("const_val", TensorProto.FLOAT, [1], [-1])
+        cond_tensor = helper.make_tensor_value_info("cond", TensorProto.BOOL, [1])
+        inner_if_node = None
+        for i in range(num_nested):
+            identity_node = helper.make_node(
+                "Identity",
+                inputs=["const_val"],
+                outputs=[f"const{i}"],
+                name=f"depth{i}'th else identity",
+            )
+            else_branch = helper.make_graph(
+                [identity_node],
+                f"else{i}_body",
+                inputs=[],
+                outputs=[helper.make_tensor_value_info(f"const{i}", TensorProto.FLOAT, [1])],
+            )
+            out_name = f"if_output{i}" if i != (num_nested - 1) else "graph_output"
+
+            if i == 0:
+                identity_node = helper.make_node(
+                    "Identity",
+                    inputs=["graph_input"],
+                    outputs=[f"input_identity{i}"],
+                    name=f"depth{i}'th then identity",
+                )
+                then_branch = helper.make_graph(
+                    [identity_node],
+                    f"then{i}_body",
+                    inputs=[],
+                    outputs=[
+                        helper.make_tensor_value_info(f"input_identity{i}", TensorProto.FLOAT, [1])
+                    ],
+                )
+                if_node = helper.make_node(
+                    "If",
+                    inputs=["cond"],
+                    outputs=[out_name],
+                    then_branch=then_branch,
+                    else_branch=else_branch,
+                    name=f"depth{i}'s If node",
+                )
+                inner_if_node = if_node
+            else:
+                then_branch = helper.make_graph(
+                    [inner_if_node],
+                    f"then{i}_body",
+                    inputs=[],
+                    outputs=[
+                        helper.make_tensor_value_info(f"if_output{i-1}", TensorProto.FLOAT, [1])
+                    ],
+                )
+                if_node = helper.make_node(
+                    "If",
+                    inputs=["cond"],
+                    outputs=[out_name],
+                    then_branch=then_branch,
+                    else_branch=else_branch,
+                    name=f"depth{i}'s If node",
+                )
+                inner_if_node = if_node
+        graph_nodes = [constant_node, inner_if_node]
+        graph = helper.make_graph(
+            graph_nodes,
+            "input_use_in_if_test",
+            inputs=[input_tensor, cond_tensor],
+            outputs=[output_tensor],
+        )
+        model = helper.make_model(graph, producer_name="input_use_in_if_test")
+
+        verify_with_ort_with_inputs(
+            model,
+            [np.array([3.0], dtype="float32"), np.array([cond])],
+            dtype="float32",
+            use_vm=True,
+            opset=14,
+            target=target,
+            dev=dev,
+        )
+
+    # Confirm that if works with cond as an array or scalar.
+    verify_if(num_nested=1, cond=True)
+    verify_if(num_nested=1, cond=False)
+    verify_if(num_nested=2, cond=True)
+    verify_if(num_nested=2, cond=False)
+
+
 @tvm.testing.parametrize_targets
 def test_size(target, dev):
     """test_size"""