diff --git a/studio/backend/core/inference/diffusion.py b/studio/backend/core/inference/diffusion.py
index da04a4ceee..b698dd71f4 100644
--- a/studio/backend/core/inference/diffusion.py
+++ b/studio/backend/core/inference/diffusion.py
@@ -2268,7 +2268,9 @@ class DiffusionBackend:
 
     @staticmethod
     def _align_vae_dtype(
-        pipe: Any, denoiser_attr: str = "transformer", vae_quant: Optional[str] = None
+        pipe: Any,
+        denoiser_attr: str = "transformer",
+        vae_quant: Optional[str] = None,
     ) -> None:
         """Cast the VAE to the denoiser's compute dtype before an image-conditioned
         call. The img2img/inpaint pipelines VAE-encode the input image at the text-
diff --git a/studio/backend/core/inference/diffusion_vae_quant.py b/studio/backend/core/inference/diffusion_vae_quant.py
index 92ccd0e985..b0a3cf685c 100644
--- a/studio/backend/core/inference/diffusion_vae_quant.py
+++ b/studio/backend/core/inference/diffusion_vae_quant.py
@@ -269,7 +269,9 @@ def _cast_vae_fp8_dynamic(vae: Any, target: Any) -> None:
         name = fqn.lower() if fqn else ""
         return not any(tok in name for tok in _VAE_KEEP_DENSE_TOKENS)
 
-    quantize_(vae, Float8DynamicActivationFloat8WeightConfig(granularity = PerTensor()), filter_fn = filter_fn)
+    quantize_(
+        vae, Float8DynamicActivationFloat8WeightConfig(granularity = PerTensor()), filter_fn = filter_fn
+    )
 
 
 def _cast_vae_fp8(vae: Any, target: Any) -> None:
diff --git a/studio/backend/tests/test_diffusion_vae_quant.py b/studio/backend/tests/test_diffusion_vae_quant.py
index ba621d6526..a948a8454d 100644
--- a/studio/backend/tests/test_diffusion_vae_quant.py
+++ b/studio/backend/tests/test_diffusion_vae_quant.py
@@ -29,7 +29,12 @@ from core.inference.diffusion_vae_quant import (
 )
 
 
-def _target(*, device = "cuda", dtype = "bfloat16", cc = (10, 0)):
+def _target(
+    *,
+    device = "cuda",
+    dtype = "bfloat16",
+    cc = (10, 0),
+):
     return types.SimpleNamespace(device = device, dtype = dtype, _cc = cc)
 
 
@@ -43,7 +48,12 @@ class _Weight:
         return len(self.shape)
 
 
-def _stub_torch(monkeypatch, *, with_fp8 = True, cc = (10, 0)):
+def _stub_torch(
+    monkeypatch,
+    *,
+    with_fp8 = True,
+    cc = (10, 0),
+):
     torch = types.ModuleType("torch")
     torch.bfloat16 = "bfloat16"
     torch.float16 = "float16"
@@ -164,9 +174,7 @@ def test_select_offload_uses_layerwise_fp8(monkeypatch):
     monkeypatch.setattr(
         vq, "_vae_fp8_dynamic_probe", lambda device: pytest.fail("probe must not run under offload")
     )
-    assert (
-        select_vae_quant_scheme(_target(), "auto", offload_active = True) == VAE_QUANT_FP8
-    )
+    assert select_vae_quant_scheme(_target(), "auto", offload_active = True) == VAE_QUANT_FP8
 
 
 def test_select_force_fp32_stays_dense(monkeypatch):
@@ -258,6 +266,7 @@ def test_fp8_dynamic_conv_filter(monkeypatch):
     assert ff(_mod(nn.Conv2d, (128, 128, 3, 3)), "decoder.conv_norm_out") is False
     assert ff(_mod(nn.Conv2d, (128, 128, 3, 3)), "decoder.norm_out.conv") is False
     assert ff(_mod(nn.Linear, (512, 512)), "decoder.proj_out") is False
+
     # A non-conv/linear module (e.g. a GroupNorm) is excluded outright.
     class _GroupNorm:
         pass