diff --git a/scripts/fp8_overflow_check.py b/scripts/fp8_overflow_check.py
index 09caf9064d..157b9cce30 100644
--- a/scripts/fp8_overflow_check.py
+++ b/scripts/fp8_overflow_check.py
@@ -22,9 +22,11 @@ PROMPT = "A cinematic photograph of a red fox in a snowy forest at dawn, highly
 
 def _load_dense():
     import torch, diffusers
+
     t = diffusers.ZImageTransformer2DModel.from_pretrained(
-        BASE, subfolder="transformer", torch_dtype=torch.bfloat16)
-    pipe = diffusers.ZImagePipeline.from_pretrained(BASE, torch_dtype=torch.bfloat16, transformer=t)
+        BASE, subfolder = "transformer", torch_dtype = torch.bfloat16
+    )
+    pipe = diffusers.ZImagePipeline.from_pretrained(BASE, torch_dtype = torch.bfloat16, transformer = t)
     pipe.to("cuda")
     return pipe
 
@@ -37,13 +39,16 @@ def _run(fast_accum, steps, res, seed, mf):
 
     pipe = _load_dense()
 
-    def filt(mod, fqn=""):
-        return (isinstance(mod, nn.Linear)
-                and mod.in_features >= mf and mod.out_features >= mf)
+    def filt(mod, fqn = ""):
+        return isinstance(mod, nn.Linear) and mod.in_features >= mf and mod.out_features >= mf
 
-    quantize_(pipe.transformer,
-              Float8DynamicActivationFloat8WeightConfig(mm_config=Float8MMConfig(use_fast_accum=fast_accum)),
-              filter_fn=filt)
+    quantize_(
+        pipe.transformer,
+        Float8DynamicActivationFloat8WeightConfig(
+            mm_config = Float8MMConfig(use_fast_accum = fast_accum)
+        ),
+        filter_fn = filt,
+    )
 
     stats = {"max_abs": 0.0, "nonfinite": 0, "hooked": 0}
 
@@ -66,10 +71,17 @@ def _run(fast_accum, steps, res, seed, mf):
             m.register_forward_hook(hook)
             stats["hooked"] += 1
 
-    g = torch.Generator(device="cuda").manual_seed(seed)
-    img = pipe(prompt=PROMPT, width=res, height=res, num_inference_steps=steps,
-               guidance_scale=0.0, generator=g).images[0]
+    g = torch.Generator(device = "cuda").manual_seed(seed)
+    img = pipe(
+        prompt = PROMPT,
+        width = res,
+        height = res,
+        num_inference_steps = steps,
+        guidance_scale = 0.0,
+        generator = g,
+    ).images[0]
     import numpy as np
+
     arr = np.array(img)
     img_finite = bool(np.isfinite(arr).all())
     del pipe
@@ -77,21 +89,24 @@ def _run(fast_accum, steps, res, seed, mf):
     return stats, img_finite
 
 
-def main(argv=None) -> int:
+def main(argv = None) -> int:
     p = argparse.ArgumentParser()
-    p.add_argument("--steps", type=int, default=4)
-    p.add_argument("--res", type=int, default=512)
-    p.add_argument("--seed", type=int, default=42)
-    p.add_argument("--min-feat", type=int, default=512)
+    p.add_argument("--steps", type = int, default = 4)
+    p.add_argument("--res", type = int, default = 512)
+    p.add_argument("--seed", type = int, default = 42)
+    p.add_argument("--min-feat", type = int, default = 512)
     args = p.parse_args(argv)
 
-    print(f"== fp8 overflow check (Z-Image dense, {args.res}px, {args.steps} steps) ==", flush=True)
+    print(f"== fp8 overflow check (Z-Image dense, {args.res}px, {args.steps} steps) ==", flush = True)
     for fast in (True, False):
         stats, img_finite = _run(fast, args.steps, args.res, args.seed, args.min_feat)
-        print(f"  fast_accum={str(fast):5s}  hooked_linears={stats['hooked']:3d}  "
-              f"max|linear_out|={stats['max_abs']:.1f}  nonfinite_elems={stats['nonfinite']}  "
-              f"image_all_finite={img_finite}", flush=True)
-    print("FP8-OVERFLOW-CHECK-DONE", flush=True)
+        print(
+            f"  fast_accum={str(fast):5s}  hooked_linears={stats['hooked']:3d}  "
+            f"max|linear_out|={stats['max_abs']:.1f}  nonfinite_elems={stats['nonfinite']}  "
+            f"image_all_finite={img_finite}",
+            flush = True,
+        )
+    print("FP8-OVERFLOW-CHECK-DONE", flush = True)
     return 0