diff --git a/studio/backend/core/inference/diffusion.py b/studio/backend/core/inference/diffusion.py
index 7e9965eb41..9555b3c38a 100644
--- a/studio/backend/core/inference/diffusion.py
+++ b/studio/backend/core/inference/diffusion.py
@@ -996,7 +996,10 @@ class DiffusionBackend:
                 # as-is, so auto is the DEFAULT. An explicit "none"/"off" pins
                 # GGUF-as-is and an explicit scheme pins that scheme. The overwritten
                 # "auto" still records source=auto in the resolved provenance.
-                if transformer_quant is None or str(transformer_quant).strip().lower() in ("", "auto"):
+                if transformer_quant is None or str(transformer_quant).strip().lower() in (
+                    "",
+                    "auto",
+                ):
                     transformer_quant = TQ_AUTO
 
                 # Default-on fast path: load the DENSE bf16 transformer and torchao-quantise it
diff --git a/studio/backend/core/inference/diffusion_auto_policy.py b/studio/backend/core/inference/diffusion_auto_policy.py
index 1bdd88e5f6..0aa0a51371 100644
--- a/studio/backend/core/inference/diffusion_auto_policy.py
+++ b/studio/backend/core/inference/diffusion_auto_policy.py
@@ -135,6 +135,7 @@ def _hf_cache_free_mib() -> Optional[int]:
     """Free MiB on the filesystem holding the HF model cache (None when unprobeable)."""
     try:
         import shutil
+
         try:
             from huggingface_hub.constants import HF_HUB_CACHE as cache_dir
         except Exception:  # noqa: BLE001 -- hub missing/old: probe the conventional path
diff --git a/studio/backend/models/inference.py b/studio/backend/models/inference.py
index e655817ad5..9a47105532 100644
--- a/studio/backend/models/inference.py
+++ b/studio/backend/models/inference.py
@@ -1750,15 +1750,17 @@ class DiffusionLoadRequest(BaseModel):
         "memory-vs-quality tradeoff (shifts fine detail), not free; "
         "pairs well with balanced mode.",
     )
-    transformer_quant: Optional[Literal["auto", "none", "off", "int8", "fp8", "nvfp4", "mxfp8"]] = Field(
-        None,
-        description = "Transformer compute dtype. UNSET or auto (the default) picks the "
-        "fastest precision the hardware supports: the DENSE bf16 transformer "
-        "is loaded instead of the GGUF and torchao-quantised onto the "
-        "low-precision tensor cores (data-center fp8, consumer/Ampere int8), "
-        "falling back to the GGUF when the device, VRAM or disk cannot take "
-        "it. none/off pins running the GGUF as-is; an explicit scheme forces "
-        "that scheme. Dense path needs CUDA + bf16.",
+    transformer_quant: Optional[Literal["auto", "none", "off", "int8", "fp8", "nvfp4", "mxfp8"]] = (
+        Field(
+            None,
+            description = "Transformer compute dtype. UNSET or auto (the default) picks the "
+            "fastest precision the hardware supports: the DENSE bf16 transformer "
+            "is loaded instead of the GGUF and torchao-quantised onto the "
+            "low-precision tensor cores (data-center fp8, consumer/Ampere int8), "
+            "falling back to the GGUF when the device, VRAM or disk cannot take "
+            "it. none/off pins running the GGUF as-is; an explicit scheme forces "
+            "that scheme. Dense path needs CUDA + bf16.",
+        )
     )
     transformer_quant_fast_accum: Optional[bool] = Field(
         None,
diff --git a/studio/backend/tests/test_diffusion_auto_policy.py b/studio/backend/tests/test_diffusion_auto_policy.py
index d9deadac0e..7190395f5f 100644
--- a/studio/backend/tests/test_diffusion_auto_policy.py
+++ b/studio/backend/tests/test_diffusion_auto_policy.py
@@ -151,9 +151,7 @@ def test_candidate_disk_gate_unprobeable_disk_passes(monkeypatch):
 
     _patch_selector(monkeypatch, scheme = "int8")
     monkeypatch.setattr(ap, "_hf_cache_free_mib", lambda: None)
-    est = resolve_dense_quant_candidate(
-        fam = _fam("z-image"), target = object(), requested = "auto"
-    )
+    est = resolve_dense_quant_candidate(fam = _fam("z-image"), target = object(), requested = "auto")
     assert isinstance(est, DenseQuantEstimate)