diff --git a/scripts/build_prequant_checkpoint.py b/scripts/build_prequant_checkpoint.py
index 8312e02130..366822de94 100644
--- a/scripts/build_prequant_checkpoint.py
+++ b/scripts/build_prequant_checkpoint.py
@@ -86,7 +86,9 @@ def main(argv = None) -> int:
     quantize_(
         transformer,
         _make_quant_config(scheme),
-        filter_fn = make_filter_fn(args.min_features, exclude_name_tokens = exclude_tokens_for_scheme(scheme)),
+        filter_fn = make_filter_fn(
+            args.min_features, exclude_name_tokens = exclude_tokens_for_scheme(scheme)
+        ),
     )
 
     # Move the state dict to CPU for a portable, GPU-free artifact.
diff --git a/studio/backend/core/inference/diffusion_transformer_quant.py b/studio/backend/core/inference/diffusion_transformer_quant.py
index e9bd1c6ff1..821f33da1b 100644
--- a/studio/backend/core/inference/diffusion_transformer_quant.py
+++ b/studio/backend/core/inference/diffusion_transformer_quant.py
@@ -72,6 +72,7 @@ def exclude_tokens_for_scheme(scheme: str) -> tuple[str, ...]:
     M=1 projections as int8 and crashes at the first denoise step on Flux / Qwen."""
     return _INT8_EXCLUDE_NAME_TOKENS if scheme == TQ_INT8 else ()
 
+
 # Per-architecture preference order for ``auto`` -- best (fastest, in-bar) first, with
 # the lower-precision schemes listed as fallbacks for that arch tier. On Blackwell, fp8
 # leads: measured on a B200, plain fp8 dynamic is both faster AND more accurate than the