diff --git a/src/refiners/foundationals/latent_diffusion/stable_diffusion_xl/control_lora.py b/src/refiners/foundationals/latent_diffusion/stable_diffusion_xl/control_lora.py
index b9eeccd..8138d63 100644
--- a/src/refiners/foundationals/latent_diffusion/stable_diffusion_xl/control_lora.py
+++ b/src/refiners/foundationals/latent_diffusion/stable_diffusion_xl/control_lora.py
@@ -117,7 +117,7 @@ class ZeroConvolution(Passthrough):
             device: The PyTorch device to use.
             dtype: The PyTorch data type to use.
         """
-        self.scale = scale
+        self._scale = scale
 
         super().__init__(
             Conv2d(
@@ -131,6 +131,15 @@ class ZeroConvolution(Passthrough):
             ResidualAccumulator(n=residual_index),
         )
 
+    @property
+    def scale(self) -> float:
+        return self._scale
+
+    @scale.setter
+    def scale(self, value: float) -> None:
+        self._scale = value
+        self.ensure_find(Multiply).scale = value
+
 
 class ControlLora(Passthrough):
     """ControlLora is a Half-UNet clone of the target UNet,
diff --git a/tests/adapters/test_control_lora.py b/tests/adapters/test_control_lora.py
new file mode 100644
index 0000000..5c7f26c
--- /dev/null
+++ b/tests/adapters/test_control_lora.py
@@ -0,0 +1,34 @@
+import torch
+
+import refiners.fluxion.layers as fl
+from refiners.foundationals.latent_diffusion import ControlLoraAdapter, SDXLUNet
+from refiners.foundationals.latent_diffusion.stable_diffusion_xl.control_lora import ZeroConvolution
+
+
+def test_inject_eject(test_device: torch.device):
+    unet = SDXLUNet(in_channels=4, device=test_device, dtype=torch.float16)
+    initial_repr = repr(unet)
+    adapter = ControlLoraAdapter(name="foo", target=unet)
+    assert repr(unet) == initial_repr
+    adapter.inject()
+    assert repr(unet) != initial_repr
+    adapter.eject()
+    assert repr(unet) == initial_repr
+
+
+def test_scale(test_device: torch.device):
+    unet = SDXLUNet(in_channels=4, device=test_device, dtype=torch.float16)
+    adapter = ControlLoraAdapter(name="foo", target=unet, scale=0.75).inject()
+
+    def predicate(m: fl.Module, p: fl.Chain) -> bool:
+        return isinstance(p, ZeroConvolution) and isinstance(m, fl.Multiply)
+
+    for m, _ in unet.walk(predicate):
+        assert isinstance(m, fl.Multiply)
+        assert m.scale == 0.75
+
+    adapter.scale = 0.42
+    assert adapter.scale == 0.42
+    for m, _ in unet.walk(predicate):
+        assert isinstance(m, fl.Multiply)
+        assert m.scale == 0.42