diff --git a/hls4ml/backends/fpga/passes/clone.py b/hls4ml/backends/fpga/passes/clone.py
index 40462f1175..f425855c3b 100644
--- a/hls4ml/backends/fpga/passes/clone.py
+++ b/hls4ml/backends/fpga/passes/clone.py
@@ -20,21 +20,19 @@ def initialize(self):
 class CloneFunctionTemplate(FunctionCallTemplate):
     def __init__(self):
         super().__init__(Clone, include_header=clone_include_list)
-        self.template = None  # to be filled once number of clones known
 
     def format(self, node):
         params = self._default_function_params(node)
         for i, _output in enumerate(node.outputs):
             params['output' + str(i + 1)] = node.variables[node.outputs[i]].name
 
-        if self.template is None:
-            self.template = (
-                'nnet::clone_stream<{input_t}, {output_t}, {size}>({input}, '
-                + ', '.join(['{output' + str(i + 1) + '}' for i in range(len(node.outputs))])
-                + ');'
-            )
+        template = (
+            'nnet::clone_stream<{input_t}, {output_t}, {size}>({input}, '
+            + ', '.join(['{output' + str(i + 1) + '}' for i in range(len(node.outputs))])
+            + ');'
+        )
 
-        return self.template.format(**params)
+        return template.format(**params)
 
 
 def register_clone(backend):
diff --git a/test/pytest/test_stream_multi_clone.py b/test/pytest/test_stream_multi_clone.py
new file mode 100644
index 0000000000..64b4779bfe
--- /dev/null
+++ b/test/pytest/test_stream_multi_clone.py
@@ -0,0 +1,59 @@
+import os
+import random
+from pathlib import Path
+
+import numpy as np
+import pytest
+import tensorflow as tf
+from keras.layers import Add, Dense
+from tensorflow import keras
+
+from hls4ml.converters import convert_from_keras_model
+
+test_root_path = Path(__file__).parent
+
+
+@pytest.fixture(scope='module')
+def model():
+    seed = 42
+    os.environ['RANDOM_SEED'] = f'{seed}'
+    np.random.seed(seed)
+    tf.random.set_seed(seed)
+    tf.get_logger().setLevel('ERROR')
+    random.seed(seed)
+
+    inp = keras.Input(shape=(10,))
+    x = Dense(10)(inp)
+    y = Dense(10)(inp)
+    z = Dense(10)(inp)
+    xy = Add()([x, y])  # 5
+    xy = Add()([xy, y])  # 5
+    out = Add()([xy, z])  # 5
+    model = keras.Model(inp, out)
+    return model
+
+
+@pytest.fixture(scope='module')
+def data():
+    rng = np.random.RandomState(42)
+    X = rng.normal(0, 1, (1000, 10))
+    X = np.clip(X, -16, 15)
+    return X
+
+
+@pytest.mark.parametrize('backend', ['Vivado', 'Quartus', 'Vitis'])
+def test_multi_clone(model, data, backend: str):
+    output_dir = str(test_root_path / f'hls4mlprj_stream_multi_clone_{backend}')
+    hls_config = {'Model': {'Precision': 'fixed<32,5>', 'ReuseFactor': 1}}
+    model_hls = convert_from_keras_model(
+        model,
+        backend=backend,
+        output_dir=output_dir,
+        hls_config=hls_config,
+        io_type='io_stream',  # clone only happens with stream io.
+    )
+    model_hls.compile()
+    r_hls = model_hls.predict(data)
+    r_keras = model(data).numpy()
+
+    assert np.allclose(r_hls, r_keras, atol=1e-5, rtol=0)