diff --git a/studio/backend/core/inference/diffusion_cfg_parallel.py b/studio/backend/core/inference/diffusion_cfg_parallel.py
index 7873643794..114231968a 100644
--- a/studio/backend/core/inference/diffusion_cfg_parallel.py
+++ b/studio/backend/core/inference/diffusion_cfg_parallel.py
@@ -459,7 +459,6 @@ def _device_identity(idx: int) -> Optional[tuple]:
     props cannot be queried (a stubbed/old torch): identity is then treated as
     unknown and the check stays best-effort rather than blocking the engage."""
     import torch
-
     try:
         return (
             str(torch.cuda.get_device_name(idx)),
diff --git a/studio/backend/core/inference/diffusion_precision.py b/studio/backend/core/inference/diffusion_precision.py
index ebbba22e1a..0cdf1588c6 100644
--- a/studio/backend/core/inference/diffusion_precision.py
+++ b/studio/backend/core/inference/diffusion_precision.py
@@ -368,7 +368,6 @@ def quantize_text_encoders(
             # offload's Module.to() hard-crashes on). Fail the load for that; a clean
             # miss (nothing swapped, e.g. layerwise fp8) stays best-effort dense.
             from .diffusion_transformer_quant import raise_if_partially_quantized
-
             raise_if_partially_quantized(encoder, what = f"text_encoder_quant {mode}:{attr}", exc = exc)
             _warn(logger, f"{mode}:{attr}", exc)
     return mode if cast else None
diff --git a/studio/backend/core/inference/video.py b/studio/backend/core/inference/video.py
index 87fc720647..c83f4b9793 100644
--- a/studio/backend/core/inference/video.py
+++ b/studio/backend/core/inference/video.py
@@ -473,11 +473,7 @@ def _views_for(pipe: Any, fam: VideoFamily) -> tuple[Any, ...]:
 
 
 def _step_cache_all_or_none(
-    pipe: Any,
-    fam: VideoFamily,
-    engage_fn: Any,
-    *,
-    logger: Any,
+    pipe: Any, fam: VideoFamily, engage_fn: Any, *, logger: Any
 ) -> tuple[Optional[str], Optional[str]]:
     """Run ``engage_fn(view, expert_name)`` (apply_step_cache or the auto toggle) over
     every expert and enforce ALL-OR-NONE, mirroring the transactional quant loop: on a
@@ -1514,6 +1510,7 @@ class VideoBackend:
             cache_request = (
                 auto_cache_mode(fam.name) if default_cache_steps >= FBCACHE_MIN_STEPS else None
             )
+
         # Each expert view passes the pipe attribute it exposes as ``transformer`` (the
         # expert-view iteration contract): a dual-expert MoE's second view passes
         # expert="transformer_2" so MagCache resolves THAT expert's calibrated curve --
diff --git a/studio/backend/tests/test_diffusion_transformer_quant.py b/studio/backend/tests/test_diffusion_transformer_quant.py
index 10e4bba73e..116aab841c 100644
--- a/studio/backend/tests/test_diffusion_transformer_quant.py
+++ b/studio/backend/tests/test_diffusion_transformer_quant.py
@@ -779,7 +779,11 @@ def test_quantize_transformer_partial_failure_raises(monkeypatch):
     monkeypatch.setattr(tq, "_make_quant_config", lambda scheme, fast_accum = None: "cfg")
     tqz = types.ModuleType("torchao.quantization")
 
-    def _convert_one_then_boom(module, config, filter_fn = None):
+    def _convert_one_then_boom(
+        module,
+        config,
+        filter_fn = None,
+    ):
         module._swapped = True  # the in-place swap of the first submodule
         raise RuntimeError("OOM mid-conversion")
 
@@ -798,7 +802,11 @@ def test_quantize_transformer_clean_failure_still_falls_back_dense(monkeypatch):
     monkeypatch.setattr(tq, "_make_quant_config", lambda scheme, fast_accum = None: "cfg")
     tqz = types.ModuleType("torchao.quantization")
 
-    def _boom(module, config, filter_fn = None):
+    def _boom(
+        module,
+        config,
+        filter_fn = None,
+    ):
         raise RuntimeError("failed before any swap")
 
     tqz.quantize_ = _boom
diff --git a/studio/backend/tests/test_video_backend.py b/studio/backend/tests/test_video_backend.py
index 084c47fa23..617e17b07c 100644
--- a/studio/backend/tests/test_video_backend.py
+++ b/studio/backend/tests/test_video_backend.py
@@ -2101,7 +2101,8 @@ def test_step_cache_all_or_none_rolls_back_second_expert_failure(monkeypatch):
     monkeypatch.setattr(
         video,
         "_disengage_step_cache",
-        lambda transformer, *, reason, logger = None: disengaged.append((transformer, reason)) or True,
+        lambda transformer, *, reason, logger = None: disengaged.append((transformer, reason))
+        or True,
     )
     calls: list = []
 
@@ -2128,7 +2129,8 @@ def test_step_cache_all_or_none_rolls_back_first_expert_failure(monkeypatch):
         lambda transformer, *, reason, logger = None: disengaged.append(transformer) or True,
     )
     mode, reason = video._step_cache_all_or_none(
-        pipe, fam,
+        pipe,
+        fam,
         lambda view, expert_name: "magcache" if expert_name == "transformer_2" else None,
         logger = None,
     )