diff --git a/studio/backend/core/inference/diffusion_precision.py b/studio/backend/core/inference/diffusion_precision.py
index 68036aae8d..030b6da7f2 100644
--- a/studio/backend/core/inference/diffusion_precision.py
+++ b/studio/backend/core/inference/diffusion_precision.py
@@ -120,7 +120,10 @@ def _cast_fp8(encoder: Any, target: Any) -> None:
     # gets cast to fp8 and, sharing one tensor, drags the embedding to fp8 with it. The
     # embedding then emits fp8 activations that crash the first RMSNorm. Skip the tied
     # projection so the shared tensor stays dense (lm_head is unused for prompt encoding).
-    get_out, get_in = getattr(encoder, "get_output_embeddings", None), getattr(encoder, "get_input_embeddings", None)
+    get_out, get_in = (
+        getattr(encoder, "get_output_embeddings", None),
+        getattr(encoder, "get_input_embeddings", None),
+    )
     out_emb = get_out() if callable(get_out) else None
     in_emb = get_in() if callable(get_in) else None
     if out_emb is not None and in_emb is not None and out_emb.weight is in_emb.weight:
diff --git a/studio/backend/core/inference/diffusion_transformer_quant.py b/studio/backend/core/inference/diffusion_transformer_quant.py
index 6e1341983f..7cb1074425 100644
--- a/studio/backend/core/inference/diffusion_transformer_quant.py
+++ b/studio/backend/core/inference/diffusion_transformer_quant.py
@@ -257,6 +257,7 @@ def _make_quant_config(scheme: str, fast_accum: Optional[bool] = None) -> Any:
             return Float8DynamicActivationFloat8WeightConfig()
     if scheme == TQ_NVFP4:
         from torchao.prototype.mx_formats import NVFP4DynamicActivationNVFP4WeightConfig
+
         # Select the CUTLASS FP4 path, not the default Triton kernel: torchao defaults
         # use_triton_kernel=True, which needs MSLK installed. On a Blackwell box with the
         # CUTLASS FP4 extension but no MSLK, the default would make the smoke probe fail