use same scale setter / getter interface for all controls

src/refiners/foundationals/latent_diffusion/image_prompt.py

@@ -424,10 +424,6 @@ class IPAdapter(Generic[T], fl.Chain, Adapter[T]):
         for cross_attn in self.sub_adapters:
             cross_attn.scale = value
 
-    def set_scale(self, scale: float) -> None:
-        for cross_attn in self.sub_adapters:
-            cross_attn.scale = scale
-
     def set_clip_image_embedding(self, image_embedding: Tensor) -> None:
         """Set the CLIP image embedding context.
 

src/refiners/foundationals/latent_diffusion/stable_diffusion_1/controlnet.py

@@ -174,8 +174,13 @@ class SD1ControlnetAdapter(Chain, Adapter[SD1UNet]):
     def init_context(self) -> Contexts:
         return {"controlnet": {f"condition_{self.name}": None}}
 
-    def set_scale(self, scale: float) -> None:
-        self._controlnet[0].scale = scale
+    @property
+    def scale(self) -> float:
+        return self._controlnet[0].scale
+
+    @scale.setter
+    def scale(self, value: float) -> None:
+        self._controlnet[0].scale = value
 
     def set_controlnet_condition(self, condition: Tensor) -> None:
         self.set_context("controlnet", {f"condition_{self.name}": condition})

src/refiners/foundationals/latent_diffusion/t2i_adapter.py

@@ -204,9 +204,14 @@ class T2IAdapter(Generic[T], fl.Chain, Adapter[T]):
     def set_condition_features(self, features: tuple[Tensor, ...]) -> None:
         self.set_context("t2iadapter", {f"condition_features_{self.name}": features})
 
-    def set_scale(self, scale: float) -> None:
+    @property
+    def scale(self) -> float:
+        return self._features[0].scale
+
+    @scale.setter
+    def scale(self, value: float) -> None:
         for f in self._features:
-            f.scale = scale
+            f.scale = value
 
     def init_context(self) -> Contexts:
         return {"t2iadapter": {f"condition_features_{self.name}": None}}

tests/e2e/test_diffusion.py

@@ -2109,7 +2109,7 @@ def test_t2i_adapter_xl_canny(
     sdxl.set_inference_steps(30)
 
     t2i_adapter = SDXLT2IAdapter(target=sdxl.unet, name=name, weights=load_from_safetensors(weights_path)).inject()
-    t2i_adapter.set_scale(0.8)
+    t2i_adapter.scale = 0.8
 
     condition = image_to_tensor(condition_image.convert("RGB"), device=test_device)
     t2i_adapter.set_condition_features(features=t2i_adapter.compute_condition_features(condition))
@@ -2238,8 +2238,8 @@ def test_hello_world(
     condition = image_to_tensor(condition_image.convert("RGB"), device=sdxl.device, dtype=sdxl.dtype)
     t2i_adapter.set_condition_features(features=t2i_adapter.compute_condition_features(condition))
 
-    ip_adapter.set_scale(0.85)
-    t2i_adapter.set_scale(0.8)
+    ip_adapter.scale = 0.85
+    t2i_adapter.scale = 0.8
     sdxl.set_inference_steps(50, first_step=1)
     sdxl.set_self_attention_guidance(enable=True, scale=0.75)