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

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pre-commit-ci[bot] 2026-07-10 17:45:26 +00:00
commit 0d378bc496
5 changed files with 36 additions and 35 deletions
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3
scripts/video_speedmem_bench.py
View file

@ -614,8 +614,7 @@ def _timed_video(
threshold = cache_threshold,
mode = auto_cache_mode(family),
family = family,
quality = normalize_cache_quality(cache_quality)
or auto_cache_quality(family),
quality = normalize_cache_quality(cache_quality) or auto_cache_quality(family),
expert = expert,
logger = logger,
)

5
studio/backend/core/inference/diffusion_cache.py
View file

@ -319,6 +319,7 @@ def _magcache_ratio_key(family: Optional[str], expert: Optional[str]) -> str:
return fam
return f"{fam}::{exp}"
# Families whose AUTO step-cache decision engages MagCache instead of FBCache. On
# HunyuanVideo-1.5 FBCache free-runs (no skip cap, no error budget) and derails the
# trajectory (LPIPS 0.54 + a luma shift at its default threshold), while MagCache holds
@ -667,9 +668,7 @@ def apply_step_cache(
# the schedule, so the expert runs ~len(ratios) * steps / 50 forwards.
num_steps = int(steps)
if len(ratios) != _MAGCACHE_CALIBRATION_STEPS:
num_steps = max(
1, round(len(ratios) * int(steps) / _MAGCACHE_CALIBRATION_STEPS)
)
num_steps = max(1, round(len(ratios) * int(steps) / _MAGCACHE_CALIBRATION_STEPS))
config: Any = MagCacheConfig(
threshold = thr,
max_skip_steps = mag_skip,

47
studio/backend/tests/test_diffusion_cache.py
View file

@ -631,8 +631,7 @@ def test_magcache_ratio_key_primary_and_expert():
assert _magcache_ratio_key("wan2.2-t2v-a14b", None) == "wan2.2-t2v-a14b"
assert _magcache_ratio_key("wan2.2-t2v-a14b", "transformer") == "wan2.2-t2v-a14b"
assert (
_magcache_ratio_key("Wan2.2-T2V-A14B", "transformer_2")
== "wan2.2-t2v-a14b::transformer_2"
_magcache_ratio_key("Wan2.2-T2V-A14B", "transformer_2") == "wan2.2-t2v-a14b::transformer_2"
)
@ -643,12 +642,13 @@ def test_magcache_expert_resolves_its_own_curve(monkeypatch):
primary_curve = tuple([1.0] * 15)
expert_curve = tuple([0.99] * 35)
monkeypatch.setitem(dc_mod._MAGCACHE_FAMILY_RATIOS, "fam-moe", primary_curve)
monkeypatch.setitem(
dc_mod._MAGCACHE_FAMILY_RATIOS, "fam-moe::transformer_2", expert_curve
)
monkeypatch.setitem(dc_mod._MAGCACHE_FAMILY_RATIOS, "fam-moe::transformer_2", expert_curve)
t = _MixinTransformer()
engaged = apply_step_cache(
_pipe(t), mode = "magcache", family = "fam-moe", steps = 50,
_pipe(t),
mode = "magcache",
family = "fam-moe",
steps = 50,
expert = "transformer_2",
)
assert engaged == TC_MAGCACHE
@ -664,20 +664,24 @@ def test_magcache_expert_subcurve_scales_step_count(monkeypatch):
from core.inference import diffusion_cache as dc_mod
expert_curve = tuple([0.99] * 35)
monkeypatch.setitem(
dc_mod._MAGCACHE_FAMILY_RATIOS, "fam-moe::transformer_2", expert_curve
)
monkeypatch.setitem(dc_mod._MAGCACHE_FAMILY_RATIOS, "fam-moe::transformer_2", expert_curve)
t = _MixinTransformer()
apply_step_cache(
_pipe(t), mode = "magcache", family = "fam-moe", steps = 30,
_pipe(t),
mode = "magcache",
family = "fam-moe",
steps = 30,
expert = "transformer_2",
)
assert t.enabled_with.num_inference_steps == round(35 * 30 / _MAGCACHE_CALIBRATION_STEPS)
# At the calibration step count itself the sub-curve maps 1:1.
t2 = _MixinTransformer()
apply_step_cache(
_pipe(t2), mode = "magcache", family = "fam-moe",
steps = _MAGCACHE_CALIBRATION_STEPS, expert = "transformer_2",
_pipe(t2),
mode = "magcache",
family = "fam-moe",
steps = _MAGCACHE_CALIBRATION_STEPS,
expert = "transformer_2",
)
assert t2.enabled_with.num_inference_steps == 35
@ -688,7 +692,10 @@ def test_magcache_full_curve_keeps_requested_steps(monkeypatch):
_stub_diffusers_with_magcache(monkeypatch)
t = _MixinTransformer()
apply_step_cache(
_pipe(t), mode = "magcache", family = "wan2.2-ti2v-5b", steps = 30,
_pipe(t),
mode = "magcache",
family = "wan2.2-ti2v-5b",
steps = 30,
expert = "transformer",
)
assert t.enabled_with.num_inference_steps == 30
@ -705,7 +712,10 @@ def test_magcache_expert_without_curve_runs_uncached(monkeypatch):
t = _MixinTransformer()
assert (
apply_step_cache(
_pipe(t), mode = "magcache", family = "fam-moe", steps = 50,
_pipe(t),
mode = "magcache",
family = "fam-moe",
steps = 50,
expert = "transformer_2",
)
is None
@ -718,12 +728,13 @@ def test_toggle_threads_expert_through(monkeypatch):
from core.inference import diffusion_cache as dc_mod
expert_curve = tuple([0.98] * 35)
monkeypatch.setitem(
dc_mod._MAGCACHE_FAMILY_RATIOS, "fam-moe::transformer_2", expert_curve
)
monkeypatch.setitem(dc_mod._MAGCACHE_FAMILY_RATIOS, "fam-moe::transformer_2", expert_curve)
t = _ToggleTransformer()
mode = maybe_toggle_step_cache(
_pipe(t), steps = 50, mode = TC_MAGCACHE, family = "fam-moe",
_pipe(t),
steps = 50,
mode = TC_MAGCACHE,
family = "fam-moe",
expert = "transformer_2",
)
assert mode == TC_MAGCACHE

8
studio/backend/tests/test_diffusion_cfg_parallel.py
View file

@ -475,9 +475,7 @@ def test_install_failure_after_cudnn_patch_restores_it(monkeypatch):
"_install_threadsafe_cudnn_attention",
lambda logger = None: (calls.append("install"), True)[1],
)
monkeypatch.setattr(
cp, "_restore_threadsafe_cudnn_attention", lambda: calls.append("restore")
)
monkeypatch.setattr(cp, "_restore_threadsafe_cudnn_attention", lambda: calls.append("restore"))
class _LoadableDiT(_FakeDiT):
@classmethod
@ -492,9 +490,7 @@ def test_install_failure_after_cudnn_patch_restores_it(monkeypatch):
pipe = _CtxPipe(_LoadableDiT())
pipe.guider = None # raises AFTER the patch install
proxy, reason = _gate(
monkeypatch, pipe, _fam(), speed_active = False, attention_backend = None
)
proxy, reason = _gate(monkeypatch, pipe, _fam(), speed_active = False, attention_backend = None)
assert proxy is None and reason == "replica install failed"
assert calls == ["install", "restore"]

8
studio/backend/tests/test_diffusion_precision.py
View file

@ -603,8 +603,7 @@ def test_select_te_auto_resolves_dense_for_wan_a14b_but_not_wan_5b(monkeypatch):
assert select_te_quant_scheme(_target(), "auto", family = "wan2.2-t2v-a14b") is None
assert select_te_quant_scheme(_target(), "auto", family = "Wan2.2-T2V-A14B") is None
assert (
select_te_quant_scheme(_target(), "auto", family = "wan2.2-ti2v-5b")
== TE_QUANT_FP8_DYNAMIC
select_te_quant_scheme(_target(), "auto", family = "wan2.2-ti2v-5b") == TE_QUANT_FP8_DYNAMIC
)
# The auto-dense table steers only the DEFAULT; an explicit request stays verbatim.
assert (
@ -642,10 +641,7 @@ def test_quantize_explicit_fp8_dynamic_refused_for_ltx2(monkeypatch):
calls: list = []
monkeypatch.setattr(dp, "_cast_fp8_dynamic", lambda enc, tgt: calls.append(enc))
pipe = types.SimpleNamespace(text_encoder = object())
assert (
quantize_text_encoders(pipe, _target(), mode = "fp8_dynamic", family = "ltx-2")
is None
)
assert quantize_text_encoders(pipe, _target(), mode = "fp8_dynamic", family = "ltx-2") is None
assert calls == []