diff --git a/tests/e2e/test_diffusion.py b/tests/e2e/test_diffusion.py
index 1fb78f3..d6528c6 100644
--- a/tests/e2e/test_diffusion.py
+++ b/tests/e2e/test_diffusion.py
@@ -864,7 +864,7 @@ def test_diffusion_refonly(
     prompt = "Chicken"
     clip_text_embedding = sd15.compute_clip_text_embedding(prompt)
 
-    sai = ReferenceOnlyControlAdapter(sd15.unet).inject()
+    refonly_adapter = ReferenceOnlyControlAdapter(sd15.unet).inject()
 
     guide = sd15.lda.encode_image(condition_image_refonly)
     guide = torch.cat((guide, guide))
@@ -875,7 +875,7 @@ def test_diffusion_refonly(
     for step in sd15.steps:
         noise = torch.randn(2, 4, 64, 64, device=test_device)
         noised_guide = sd15.scheduler.add_noise(guide, noise, step)
-        sai.set_controlnet_condition(noised_guide)
+        refonly_adapter.set_controlnet_condition(noised_guide)
         x = sd15(
             x,
             step=step,
@@ -903,7 +903,7 @@ def test_diffusion_inpainting_refonly(
     prompt = ""  # unconditional
     clip_text_embedding = sd15.compute_clip_text_embedding(prompt)
 
-    sai = ReferenceOnlyControlAdapter(sd15.unet).inject()
+    refonly_adapter = ReferenceOnlyControlAdapter(sd15.unet).inject()
 
     sd15.set_num_inference_steps(n_steps)
     sd15.set_inpainting_conditions(target_image_inpainting_refonly, mask_image_inpainting_refonly)
@@ -921,7 +921,7 @@ def test_diffusion_inpainting_refonly(
         # inpaint variation models")
         noised_guide = torch.cat([noised_guide, torch.zeros_like(noised_guide)[:, 0:1, :, :], guide], dim=1)
 
-        sai.set_controlnet_condition(noised_guide)
+        refonly_adapter.set_controlnet_condition(noised_guide)
         x = sd15(
             x,
             step=step,
diff --git a/tests/foundationals/latent_diffusion/test_reference_only_control.py b/tests/foundationals/latent_diffusion/test_reference_only_control.py
index 580fdb0..95201bd 100644
--- a/tests/foundationals/latent_diffusion/test_reference_only_control.py
+++ b/tests/foundationals/latent_diffusion/test_reference_only_control.py
@@ -13,9 +13,9 @@ from refiners.foundationals.latent_diffusion.cross_attention import CrossAttenti
 
 
 @torch.no_grad()
-def test_sai_inject_eject() -> None:
+def test_refonly_inject_eject() -> None:
     unet = SD1UNet(in_channels=9)
-    sai = ReferenceOnlyControlAdapter(unet)
+    adapter = ReferenceOnlyControlAdapter(unet)
 
     nb_cross_attention_blocks = len(list(unet.walk(CrossAttentionBlock)))
     assert nb_cross_attention_blocks > 0
@@ -26,21 +26,21 @@ def test_sai_inject_eject() -> None:
     assert len(list(unet.walk(SelfAttentionInjectionAdapter))) == 0
 
     with pytest.raises(AssertionError) as exc:
-        sai.eject()
+        adapter.eject()
     assert "not the first element" in str(exc.value)
 
-    sai.inject()
+    adapter.inject()
 
-    assert unet.parent == sai
+    assert unet.parent == adapter
     assert len(list(unet.walk(SelfAttentionInjectionPassthrough))) == 1
     assert len(list(unet.walk(SaveLayerNormAdapter))) == nb_cross_attention_blocks
     assert len(list(unet.walk(SelfAttentionInjectionAdapter))) == nb_cross_attention_blocks
 
     with pytest.raises(AssertionError) as exc:
-        sai.inject()
+        adapter.inject()
     assert "already injected" in str(exc.value)
 
-    sai.eject()
+    adapter.eject()
 
     assert unet.parent is None
     assert len(list(unet.walk(SelfAttentionInjectionPassthrough))) == 0