modify ip_adapter's ImageCrossAttention scale getter and setter

this new version makes it robust in case mulitple Mulitply-s are inside the Chain (e.g. if the Linear layers are LoRA-ified)

src/refiners/foundationals/latent_diffusion/image_prompt.py

@@ -235,7 +235,7 @@ def init_context(self) -> Contexts:
 
 class ImageCrossAttention(fl.Chain):
     def __init__(self, text_cross_attention: fl.Attention, scale: float = 1.0) -> None:
-        self._scale = scale
+        self._multiply = [fl.Multiply(scale)]
         super().__init__(
             fl.Distribute(
                 fl.Identity(),
@@ -263,17 +263,20 @@ def __init__(self, text_cross_attention: fl.Attention, scale: float = 1.0) -> No
             ScaledDotProductAttention(
                 num_heads=text_cross_attention.num_heads, is_causal=text_cross_attention.is_causal
             ),
-            fl.Multiply(self.scale),
+            self.multiply,
         )
 
+    @property
+    def multiply(self) -> fl.Multiply:
+        return self._multiply[0]
+
     @property
     def scale(self) -> float:
-        return self._scale
+        return self.multiply.scale
 
     @scale.setter
     def scale(self, value: float) -> None:
-        self._scale = value
-        self.ensure_find(fl.Multiply).scale = value
+        self.multiply.scale = value
 
 
 class CrossAttentionAdapter(fl.Chain, Adapter[fl.Attention]):
@@ -335,7 +338,6 @@ def scale(self) -> float:
 
     @scale.setter
     def scale(self, value: float) -> None:
-        self._scale = value
         self.image_cross_attention.scale = value
 
     def load_weights(self, key_tensor: Tensor, value_tensor: Tensor) -> None: