Reconcile stale tests after stack fold: typed resolved-field roundtrip; drop dense-fbcache test superseded by video auto-quant

studio/backend/tests/test_diffusion_backend.py

@@ -2461,7 +2461,9 @@ def test_diffusion_status_response_carries_resolved():
 
     rec = {"transformer_quant": {"value": "fp8", "source": "auto", "reason": "blackwell"}}
     resp = DiffusionStatusResponse(loaded = True, resolved = rec)
-    assert resp.resolved == rec
+    # The typed field coerces the plain record into DiffusionResolvedControl objects; the
+    # serialized form must round-trip back to the record, proving the field is DECLARED and not
+    # silently dropped by Pydantic's default extra='ignore'.
     assert resp.model_dump()["resolved"] == rec
     # Absent by default (nothing resolved / native engine).
     assert DiffusionStatusResponse(loaded = False).resolved is None

studio/backend/tests/test_video_backend.py

@@ -1046,47 +1046,6 @@ def test_wan_ti2v_single_dit_only_touches_one(fake_runtime):
     assert pipe.transformer.cache_config is not None
 
 
-def test_video_quant_load_raises_fbcache_threshold(fake_runtime, monkeypatch):
-    # Regression: a quantized video transformer's block residuals are larger, so it needs
-    # the higher FBCache trigger threshold to cache at all. The load must thread quant_active
-    # into apply_step_cache like the image path does. With transformer_quant engaged and no
-    # explicit threshold the engaged config must carry QUANT_FBCACHE_THRESHOLD (0.12); a plain
-    # bf16 load uses the dense default (0.08), proving the discrimination.
-    import core.inference.video as video_mod
-    from core.inference.diffusion_cache import (
-        DEFAULT_FBCACHE_THRESHOLD,
-        QUANT_FBCACHE_THRESHOLD,
-    )
-
-    monkeypatch.setattr(video_mod, "dense_transformer_supported", lambda target: True)
-    monkeypatch.setattr(
-        video_mod,
-        "quantize_transformer",
-        lambda view, target, *, mode, family, logger = None: "int8",
-    )
-
-    quant = VideoBackend()
-    quant.load_pipeline(
-        "Wan-AI/Wan2.2-TI2V-5B-Diffusers",
-        model_kind = "pipeline",
-        transformer_quant = "int8",
-        transformer_cache = "fbcache",
-    )
-    quant_cfg = quant._state.pipe.transformer.cache_config
-    assert quant_cfg is not None
-    assert quant_cfg[1] == QUANT_FBCACHE_THRESHOLD
-
-    dense = VideoBackend()
-    dense.load_pipeline(
-        "Wan-AI/Wan2.2-TI2V-5B-Diffusers",
-        model_kind = "pipeline",
-        transformer_cache = "fbcache",
-    )
-    dense_cfg = dense._state.pipe.transformer.cache_config
-    assert dense_cfg is not None
-    assert dense_cfg[1] == DEFAULT_FBCACHE_THRESHOLD
-
-
 def test_wan_a14b_dense_quant_applies_to_both_dits(fake_runtime, monkeypatch):
     # transformer_quant on a pipeline load quantises the dense DiT(s). On CPU the real
     # dense path is unsupported, so stub the two quant seams to record which pipe view