[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

studio/backend/core/inference/diffusion_attention.py

@@ -225,7 +225,6 @@ def _reset_global_backend_to_native(logger: Any) -> None:
             AttentionBackendName,
             _AttentionBackendRegistry,
         )
-
         _AttentionBackendRegistry.set_active_backend(AttentionBackendName.NATIVE)
     except Exception:  # noqa: BLE001 — best-effort; leave the global as-is on any change
         pass

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:

studio/backend/core/inference/diffusion_speed.py

@@ -249,6 +249,8 @@ def _enable_cudnn_benchmark(logger: Any) -> bool:
     except Exception as exc:  # noqa: BLE001 — optimisation only
         _warn(logger, "cudnn_benchmark", exc)
         return False
+
+
 # The TF32 flag values from before the first max load flipped them, so a later
 # non-max load / unload can put the process back exactly as it found it (rather than
 # forcing a hardcoded default that might clobber another component's choice).

studio/backend/core/inference/diffusion_transformer_quant.py

@@ -313,6 +313,7 @@ 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

studio/backend/tests/test_diffusion_routes.py

@@ -465,6 +465,8 @@ def test_invalid_attention_backend_returns_422(client):
         json = {"model_path": "x/z-image", "gguf_filename": "q.gguf", "attention_backend": "bogus"},
     )
     assert resp.status_code == 422
+
+
 def test_prequant_path_doc_describes_allowlist_not_toggle():
     # The field help must match the code: UNSLOTH_ALLOW_LOCAL_PREQUANT_PATH is a
     # directory allowlist, not a =1 toggle (diffusion_prequant._allowed_prequant_roots