diff --git a/src/refiners/foundationals/latent_diffusion/image_prompt.py b/src/refiners/foundationals/latent_diffusion/image_prompt.py
index 9dc358d..1734729 100644
--- a/src/refiners/foundationals/latent_diffusion/image_prompt.py
+++ b/src/refiners/foundationals/latent_diffusion/image_prompt.py
@@ -162,6 +162,10 @@ class CrossAttentionAdapter(fl.Chain, Adapter[fl.Attention]):
 
 
 class IPAdapter(Generic[T], fl.Chain, Adapter[T]):
+    # Prevent PyTorch module registration
+    _clip_image_encoder: list[CLIPImageEncoderH]
+    _image_proj: list[ImageProjection]
+
     def __init__(
         self,
         target: T,
@@ -174,13 +178,15 @@ class IPAdapter(Generic[T], fl.Chain, Adapter[T]):
 
         cross_attn_2d = target.ensure_find(CrossAttentionBlock2d)
 
-        self.clip_image_encoder = clip_image_encoder or CLIPImageEncoderH(device=target.device, dtype=target.dtype)
-        self.image_proj = ImageProjection(
-            clip_image_embedding_dim=self.clip_image_encoder.output_dim,
-            clip_text_embedding_dim=cross_attn_2d.context_embedding_dim,
-            device=target.device,
-            dtype=target.dtype,
-        )
+        self._clip_image_encoder = [clip_image_encoder or CLIPImageEncoderH(device=target.device, dtype=target.dtype)]
+        self._image_proj = [
+            ImageProjection(
+                clip_image_embedding_dim=self.clip_image_encoder.output_dim,
+                clip_text_embedding_dim=cross_attn_2d.context_embedding_dim,
+                device=target.device,
+                dtype=target.dtype,
+            )
+        ]
 
         self.sub_adapters = [
             CrossAttentionAdapter(target=cross_attn, scale=scale)
@@ -203,6 +209,14 @@ class IPAdapter(Generic[T], fl.Chain, Adapter[T]):
 
                 cross_attn.load_state_dict(state_dict=cross_attn_state_dict)
 
+    @property
+    def clip_image_encoder(self) -> CLIPImageEncoderH:
+        return self._clip_image_encoder[0]
+
+    @property
+    def image_proj(self) -> ImageProjection:
+        return self._image_proj[0]
+
     def inject(self: "TIPAdapter", parent: fl.Chain | None = None) -> "TIPAdapter":
         for adapter in self.sub_adapters:
             adapter.inject()
diff --git a/tests/e2e/test_diffusion.py b/tests/e2e/test_diffusion.py
index 11ff09f..5cfd424 100644
--- a/tests/e2e/test_diffusion.py
+++ b/tests/e2e/test_diffusion.py
@@ -1125,6 +1125,13 @@ def test_diffusion_ip_adapter_controlnet(
     ip_adapter.clip_image_encoder.load_from_safetensors(image_encoder_weights)
     ip_adapter.inject()
 
+    depth_controlnet = SD1ControlnetAdapter(
+        sd15.unet,
+        name="depth",
+        scale=1.0,
+        weights=load_from_safetensors(depth_cn_weights_path),
+    ).inject()
+
     clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
     clip_image_embedding = ip_adapter.compute_clip_image_embedding(ip_adapter.preprocess_image(input_image))
 
@@ -1138,12 +1145,6 @@ def test_diffusion_ip_adapter_controlnet(
         )
     )
 
-    depth_controlnet = SD1ControlnetAdapter(
-        sd15.unet,
-        name="depth",
-        scale=1.0,
-        weights=load_from_safetensors(depth_cn_weights_path),
-    ).inject()
     depth_cn_condition = image_to_tensor(
         depth_condition_image.convert("RGB"),
         device=test_device,