[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

studio/backend/core/inference/video.py

@@ -92,6 +92,7 @@ from .video_families import (
     supported_video_family_names,
 )
 from utils.hardware import clear_gpu_cache
+
 # Shared with the image backend so both pin every loader call to the same live cache root.
 from core.inference.diffusion import hub_cache_dir
 
@@ -751,9 +752,7 @@ class VideoBackend:
             if gguf_filename and not Path(repo_id).expanduser().exists():
                 info = api.model_info(repo_id, files_metadata = True)
                 size = sum(
-                    int(s.size or 0)
-                    for s in (info.siblings or [])
-                    if s.rfilename == gguf_filename
+                    int(s.size or 0) for s in (info.siblings or []) if s.rfilename == gguf_filename
                 )
                 total += size
                 entries.append(

studio/backend/core/training/diffusion_dit_trainer.py

@@ -1682,10 +1682,7 @@ def _train_dit(cfg, spec, pairs, rng, device, weight_dtype, on_event, _check_sto
             # on embeddings and latent stats produced by the other model -- incompatible
             # shapes at best, silently wrong conditioning at worst.
             from .diffusion_train_extras import source_revision  # noqa: PLC0415
-
-            namespace = (
-                f"{spec.family}_{cfg.base_model}_{source_revision(cfg.base_model)}"
-            )
+            namespace = f"{spec.family}_{cfg.base_model}_{source_revision(cfg.base_model)}"
             pcache = PersistentConditioningCache(cfg.cond_cache_dir, namespace, cfg.resolution)
         except Exception as exc:  # noqa: BLE001 -- the cache is an optimisation, never fatal
             _emit(on_event, "warning", message = f"conditioning cache disabled: {exc}")

studio/backend/core/training/diffusion_train_extras.py

@@ -183,7 +183,6 @@ def source_revision(ref: Any) -> str:
     not load them.
     """
     import os  # noqa: PLC0415 — keep the module import list light for the subprocess
-
     try:
         name = str(ref or "").strip()
         if not name:
@@ -193,7 +192,8 @@ def source_revision(ref: Any) -> str:
             roots = [name]
             with os.scandir(name) as it:
                 roots += [
-                    e.path for e in it
+                    e.path
+                    for e in it
                     if e.is_dir() and e.name.startswith(("text_encoder", "tokenizer"))
                 ]
             for root in roots:

studio/backend/hub/services/models/downloads.py

@@ -164,9 +164,7 @@ async def download_model_response(body: DownloadModelRequest, hf_token: Optional
                 detail = "scope_id and gguf_variant are mutually exclusive.",
             )
         if not scoped_files:
-            raise HTTPException(
-                status_code = 400, detail = "scope_id requires a non-empty files list."
-            )
+            raise HTTPException(status_code = 400, detail = "scope_id requires a non-empty files list.")
         if not _is_valid_gguf_variant(scope_variant):
             raise HTTPException(status_code = 400, detail = f"Invalid scope_id: {body.scope_id!r}")
         variant = scope_variant

studio/backend/hub/workers/hf_download.py

@@ -690,8 +690,7 @@ def _download_scoped_snapshot(
         info = _model_info_with_retry(repo_id, hf_token)
     except Exception as e:
         print(
-            f"metadata unavailable for scoped download of {repo_id} "
-            f"({type(e).__name__}: {e})",
+            f"metadata unavailable for scoped download of {repo_id} " f"({type(e).__name__}: {e})",
             file = sys.stderr,
         )
         info = None
@@ -709,6 +708,7 @@ def _download_scoped_snapshot(
             for s in siblings
         ]
         from hub.utils.snapshot_filters import blob_hashes_for_siblings
+
         blob_hashes = blob_hashes_for_siblings(siblings)
         download_manifest.write_manifest("model", repo_id, scope, expected_files, mode)
 
@@ -821,7 +821,6 @@ def main() -> None:
     scoped_files: list[str] = []
     if args.files_json:
         import json
-
         try:
             with open(args.files_json, encoding = "utf-8") as handle:
                 scoped_files = [str(f) for f in json.load(handle)]

studio/backend/tests/test_diffusion_backend.py

@@ -3755,11 +3755,20 @@ def test_generate_keeps_a_scalar_negative_prompt_off_the_list_paths(fake_runtime
 class _TracingPipe(_CountingPipe):
     """Appends ``("call", n)`` to a shared trace so resets can be interleaved with forwards."""
 
-    def __init__(self, trace, max_images = None):
+    def __init__(
+        self,
+        trace,
+        max_images = None,
+    ):
         super().__init__(max_images = max_images)
         self.trace = trace
 
-    def __call__(self, *, prompt = None, **kwargs):
+    def __call__(
+        self,
+        *,
+        prompt = None,
+        **kwargs,
+    ):
         n = kwargs.get("num_images_per_prompt", 1)
         if isinstance(prompt, list):
             n *= len(prompt)
@@ -3776,9 +3785,7 @@ def test_generate_resets_the_step_cache_before_an_oom_retry(fake_runtime, tmp_pa
     backend = _load_zimage_backend(tmp_path)
     trace: list = []
     pipe = _TracingPipe(trace, max_images = 2)
-    pipe.transformer = types.SimpleNamespace(
-        _reset_stateful_cache = lambda: trace.append(("reset",))
-    )
+    pipe.transformer = types.SimpleNamespace(_reset_stateful_cache = lambda: trace.append(("reset",)))
     object.__setattr__(backend._state, "pipe", pipe)
     object.__setattr__(backend._state, "transformer_cache", "fbcache")
     out = backend.generate(prompt = "p", seeds = [1, 2, 3, 4])
@@ -3799,13 +3806,13 @@ def test_generate_resets_the_step_cache_before_every_chunk(fake_runtime, tmp_pat
     backend = _load_zimage_backend(tmp_path)
     trace: list = []
     pipe = _TracingPipe(trace)
-    pipe.transformer = types.SimpleNamespace(
-        _reset_stateful_cache = lambda: trace.append(("reset",))
-    )
+    pipe.transformer = types.SimpleNamespace(_reset_stateful_cache = lambda: trace.append(("reset",)))
     object.__setattr__(backend._state, "pipe", pipe)
     object.__setattr__(backend._state, "transformer_cache", "fbcache")
     backend.generate(prompt = "p", seeds = [1, 2, 3], batch_size = 2)
     assert trace == [("reset",), ("call", 2), ("reset",), ("call", 1)]
+
+
 class _FakeSibling:
     def __init__(self, rfilename, size):
         self.rfilename = rfilename
@@ -3817,7 +3824,7 @@ class _FakeInfo:
         self.siblings = siblings
 
 
-GB = 1024 ** 3
+GB = 1024**3
 # A FLUX-shaped base repo: the packaged root single and the transformer shards are what a
 # plain snapshot_download would drag in and the loader never opens.
 _FLUX_BASE_SIBLINGS = [
@@ -3834,8 +3841,14 @@ _FLUX_BASE_SIBLINGS = [
 
 def _fake_hf_api(monkeypatch, repos):
     """Point HfApi.model_info at a canned sibling list per repo id."""
+
     class _Api:
-        def model_info(self, repo_id, files_metadata = False, token = None):
+        def model_info(
+            self,
+            repo_id,
+            files_metadata = False,
+            token = None,
+        ):
             return _FakeInfo(repos[repo_id])
 
     monkeypatch.setattr("huggingface_hub.HfApi", lambda *a, **k: _Api())

studio/backend/tests/test_diffusion_dit_trainer.py

@@ -170,15 +170,14 @@ def test_every_train_base_is_deployable_as_an_inference_pipeline():
     # it -- which is what happened to both FLUX.2 families, trusted for training only.
     from core.inference.diffusion import _is_trusted_diffusion_repo
     from core.inference.diffusion_families import _FAMILIES
-
     for fam in _FAMILIES:
         if not fam.trainable:
             continue
         for base in fam.train_base_repos:
             deploy_base = fam.deploy_base_repo or base
-            assert _is_trusted_diffusion_repo(deploy_base), (
-                f"{fam.name}: deploy base {deploy_base!r} is not loadable for inference"
-            )
+            assert _is_trusted_diffusion_repo(
+                deploy_base
+            ), f"{fam.name}: deploy base {deploy_base!r} is not loadable for inference"
 
 
 def test_gated_access_requires_token():

studio/backend/tests/test_diffusion_prequant.py

@@ -413,7 +413,6 @@ def test_load_exclude_tokens_need_the_recorded_family(monkeypatch, tmp_path):
     # and accept only the family-aware set. Pins the offline builder
     # (scripts/build_prequant_checkpoint.py) to exclude_tokens_for_scheme(scheme, fam.name).
     from core.inference.diffusion_transformer_quant import exclude_tokens_for_scheme
-
     for family in ("qwen-image", "qwen-image-edit"):
         family_less = _good_ckpt(scheme = "int8")
         family_less["metadata"]["family"] = family

studio/backend/tests/test_hf_cache_settings.py

@@ -319,6 +319,6 @@ def test_diffusion_loader_calls_pin_the_cache_dir():
                 if call not in line:
                     continue
                 window = "\n".join(source.splitlines()[index - 1 : index + 8])
-                assert "cache_dir" in window or "kwargs" in window, (
-                    f"{rel}:{index} calls {call} without a pinned cache_dir"
-                )
+                assert (
+                    "cache_dir" in window or "kwargs" in window
+                ), f"{rel}:{index} calls {call} without a pinned cache_dir"

studio/backend/tests/test_scoped_download_job.py

@@ -115,9 +115,7 @@ def test_scoped_files_survive_into_the_registry(monkeypatch):
     monkeypatch.setattr(dl, "resolve_cached_repo_id_case", lambda repo, **k: repo)
     monkeypatch.setattr(dl, "scoped_file_blob_hashes", lambda *a, **k: frozenset())
     monkeypatch.setattr(dl._registry, "claim", _spy_claim)
-    monkeypatch.setattr(
-        download_lifecycle, "launch_worker", lambda *a, **k: "running"
-    )
+    monkeypatch.setattr(download_lifecycle, "launch_worker", lambda *a, **k: "running")
 
     asyncio.run(dl.download_model_response(_request()))
     assert captured["scoped_files"] == FILES

tests/studio/test_model_picker_contracts.py

@@ -585,9 +585,7 @@ def test_variant_expander_forwards_the_gguf_filename():
     by filename and cannot map a quant label back to one, so without it every hub
     GGUF pick on Images/Video fell through to a silent return and nothing loaded."""
     src = _read("features/model-picker/components/model-selector/pickers.tsx")
-    handler = re.search(
-        r"const handleVariantClick = useCallback\(.*?\n  \);", src, re.S
-    )
+    handler = re.search(r"const handleVariantClick = useCallback\(.*?\n  \);", src, re.S)
     assert handler, "handleVariantClick not found"
     assert "ggufFilename: filename," in handler.group(0)
     # The call site has to actually pass it through.
@@ -648,9 +646,7 @@ def test_local_model_sections_respect_the_task_filter():
     for memo in ("sortedLmStudio", "sortedLocalDir", "sortedCustomFolderModels"):
         block = re.search(rf"const {memo} = useMemo\(.*?\n  \);", src, re.S)
         assert block, f"{memo} not found"
-        assert "passesTaskGate(m.task" in block.group(0), (
-            f"{memo} does not apply the task gate"
-        )
+        assert "passesTaskGate(m.task" in block.group(0), f"{memo} does not apply the task gate"
 
 
 def test_chat_picker_routes_diffusion_picks_to_their_page():