From 8c3554e38c6c0963e3623080c7bbe896b201d650 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Fri, 26 Jun 2026 08:01:43 +0000
Subject: [PATCH] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 studio/backend/core/inference/diffusion.py    |  4 +-
 .../inference/diffusion_transformer_quant.py  | 38 +++++++++++++-----
 .../tests/test_diffusion_transformer_quant.py | 40 +++++++++++--------
 3 files changed, 53 insertions(+), 29 deletions(-)

diff --git a/studio/backend/core/inference/diffusion.py b/studio/backend/core/inference/diffusion.py
index 076a296124..65e7ce8faa 100644
--- a/studio/backend/core/inference/diffusion.py
+++ b/studio/backend/core/inference/diffusion.py
@@ -623,9 +623,7 @@ class DiffusionBackend:
             pipe_kwargs["token"] = hf_token
         pipe = pipeline_cls.from_pretrained(base, **pipe_kwargs)
         pipe.to(device)
-        scheme = quantize_transformer(
-            pipe, target, mode = mode, fast_accum = fast_accum, logger = logger
-        )
+        scheme = quantize_transformer(pipe, target, mode = mode, fast_accum = fast_accum, logger = logger)
         if scheme is None:
             raise RuntimeError("transformer quant unsupported for this device/scheme")
         return pipe, scheme
diff --git a/studio/backend/core/inference/diffusion_transformer_quant.py b/studio/backend/core/inference/diffusion_transformer_quant.py
index c77af06f7b..f3709c6abf 100644
--- a/studio/backend/core/inference/diffusion_transformer_quant.py
+++ b/studio/backend/core/inference/diffusion_transformer_quant.py
@@ -63,13 +63,35 @@ _SMOKE_CACHE: dict[tuple[str, str], bool] = {}
 # torch.cuda.get_device_name(), so the workstation "A4000" is not mistaken for the
 # data-center "A40". Anything not here -- GeForce, workstation RTX, or an unknown name --
 # is treated as consumer-class (FP32-accumulate halved). See developer.nvidia.com/cuda/gpus.
-_DATACENTER_GPU_TOKENS = frozenset({
-    "B200", "B100", "GB200", "GB300", "GB10",          # Blackwell data center
-    "H200", "H100", "H800", "H20",                      # Hopper data center
-    "A100", "A800", "A30", "A40", "A16", "A10", "A2",  # Ampere data center
-    "L40", "L40S", "L4", "L20", "L2",                   # Ada data center
-    "V100", "P100", "P40", "T4",                        # legacy data center
-})
+_DATACENTER_GPU_TOKENS = frozenset(
+    {
+        "B200",
+        "B100",
+        "GB200",
+        "GB300",
+        "GB10",  # Blackwell data center
+        "H200",
+        "H100",
+        "H800",
+        "H20",  # Hopper data center
+        "A100",
+        "A800",
+        "A30",
+        "A40",
+        "A16",
+        "A10",
+        "A2",  # Ampere data center
+        "L40",
+        "L40S",
+        "L4",
+        "L20",
+        "L2",  # Ada data center
+        "V100",
+        "P100",
+        "P40",
+        "T4",  # legacy data center
+    }
+)
 
 
 def _is_consumer_gpu(device: Any = None) -> bool:
@@ -85,7 +107,6 @@ def _is_consumer_gpu(device: Any = None) -> bool:
         import re
 
         import torch
-
         name = torch.cuda.get_device_name(device).upper()
     except Exception:  # noqa: BLE001 — no torch / no device -> assume consumer
         return True
@@ -224,7 +245,6 @@ def _make_quant_config(scheme: str, fast_accum: Optional[bool] = None) -> Any:
         # quant noise floor (measured 0 non-finite even on Z-Image's ~1e6 activations).
         try:
             from torchao.float8 import Float8MMConfig
-
             return Float8DynamicActivationFloat8WeightConfig(
                 mm_config = Float8MMConfig(use_fast_accum = _resolve_fast_accum(fast_accum))
             )
diff --git a/studio/backend/tests/test_diffusion_transformer_quant.py b/studio/backend/tests/test_diffusion_transformer_quant.py
index 3ee17bc816..bb327367b9 100644
--- a/studio/backend/tests/test_diffusion_transformer_quant.py
+++ b/studio/backend/tests/test_diffusion_transformer_quant.py
@@ -212,31 +212,37 @@ def test_smoke_probe_caches_and_tolerates_failure(monkeypatch):
 
 def _stub_device_name(monkeypatch, name):
     torch = types.ModuleType("torch")
-    torch.cuda = types.SimpleNamespace(get_device_name=lambda device=None: name)
+    torch.cuda = types.SimpleNamespace(get_device_name = lambda device = None: name)
     monkeypatch.setitem(sys.modules, "torch", torch)
 
 
-@pytest.mark.parametrize("name", [
-    "NVIDIA GeForce RTX 5090",
-    "NVIDIA GeForce RTX 4090",
-    "NVIDIA RTX A4000",            # workstation: A4000 token, NOT the data-center A40
-    "NVIDIA RTX 6000 Ada Generation",
-    "NVIDIA Some Future Card 9000",  # unknown -> default consumer (fast accum is free on DC)
-])
+@pytest.mark.parametrize(
+    "name",
+    [
+        "NVIDIA GeForce RTX 5090",
+        "NVIDIA GeForce RTX 4090",
+        "NVIDIA RTX A4000",  # workstation: A4000 token, NOT the data-center A40
+        "NVIDIA RTX 6000 Ada Generation",
+        "NVIDIA Some Future Card 9000",  # unknown -> default consumer (fast accum is free on DC)
+    ],
+)
 def test_is_consumer_gpu_true(monkeypatch, name):
     _stub_device_name(monkeypatch, name)
     assert tq._is_consumer_gpu() is True
 
 
-@pytest.mark.parametrize("name", [
-    "NVIDIA B200",
-    "NVIDIA H100 80GB HBM3",
-    "NVIDIA A100-SXM4-80GB",
-    "NVIDIA A40",                  # data-center Ampere (distinct token from RTX A4000)
-    "NVIDIA L40S",
-    "NVIDIA L4",
-    "Tesla V100-SXM2-16GB",
-])
+@pytest.mark.parametrize(
+    "name",
+    [
+        "NVIDIA B200",
+        "NVIDIA H100 80GB HBM3",
+        "NVIDIA A100-SXM4-80GB",
+        "NVIDIA A40",  # data-center Ampere (distinct token from RTX A4000)
+        "NVIDIA L40S",
+        "NVIDIA L4",
+        "Tesla V100-SXM2-16GB",
+    ],
+)
 def test_is_consumer_gpu_false_for_datacenter(monkeypatch, name):
     _stub_device_name(monkeypatch, name)
     assert tq._is_consumer_gpu() is False