test IP adapter scale setter

tests/adapters/test_ip_adapter.py

@@ -3,8 +3,10 @@ from typing import overload
 import pytest
 import torch
 
+import refiners.fluxion.layers as fl
 from refiners.fluxion.utils import no_grad
 from refiners.foundationals.latent_diffusion import SD1IPAdapter, SD1UNet, SDXLIPAdapter, SDXLUNet
+from refiners.foundationals.latent_diffusion.image_prompt import ImageCrossAttention
 
 
 @overload
@@ -35,3 +37,23 @@ def test_inject_eject(k_unet: type[SD1UNet] | type[SDXLUNet], test_device: torch
     assert repr(unet) != initial_repr
     adapter.eject()
     assert repr(unet) == initial_repr
+
+
+@no_grad()
+@pytest.mark.parametrize("k_unet", [SD1UNet, SDXLUNet])
+def test_scale(k_unet: type[SD1UNet] | type[SDXLUNet], test_device: torch.device):
+    unet = k_unet(in_channels=4, device=test_device, dtype=torch.float16)
+    adapter = new_adapter(unet).inject()
+
+    def predicate(m: fl.Module, p: fl.Chain) -> bool:
+        return isinstance(p, ImageCrossAttention) and isinstance(m, fl.Multiply)
+
+    for m, _ in unet.walk(predicate):
+        assert isinstance(m, fl.Multiply)
+        assert m.scale == 1.0
+
+    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