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Merge branch 'claude/silly-northcutt-c2bf06': unify C2PA vendor registry + code-health + uv publish
Brings in commit 5cf68a6 (single C2PA_AI_VENDORS registry, erase_lama
grayscale/BGRA support, batch device-cache clearing + --controlnet-scale,
uv publish via OIDC, hatchling pin <1.31). Auto-merged with no conflicts;
ruff/pytest(544)/pyright all clean.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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@@ -90,3 +90,47 @@ class TestLamaBackend:
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pytest.skip("onnxruntime installed; cannot test the unavailable path")
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with pytest.raises(RuntimeError, match="onnxruntime"):
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erase(img, boxes=[(10, 10, 20, 20)], backend="lama")
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class TestLamaChannelHandling:
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"""erase_lama must accept grayscale (2D) and BGRA (4-channel) like erase_cv2.
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The real ONNX model is never loaded -- the session is faked to an identity
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inpaint, so this exercises only the channel promote/split wrapper (the fix for
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LaMa crashing on grayscale and dropping alpha on BGRA).
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"""
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@pytest.fixture
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def _fake_lama(self, monkeypatch: pytest.MonkeyPatch):
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from remove_ai_watermarks import region_eraser
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class _In:
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def __init__(self, name: str, shape: list[int]):
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self.name = name
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self.shape = shape
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class _FakeSession:
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def get_inputs(self):
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return [_In("image", [1, 3, 512, 512]), _In("mask", [1, 1, 512, 512])]
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def run(self, _outputs, feeds):
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# Identity inpaint: echo the image tensor (1,3,size,size) back.
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return [feeds["image"]]
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monkeypatch.setattr(region_eraser, "lama_available", lambda: True)
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monkeypatch.setattr(region_eraser, "_get_lama_session", lambda: _FakeSession())
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@pytest.mark.usefixtures("_fake_lama")
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def test_grayscale_2d_does_not_raise(self):
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gray = np.full((100, 100), 120, np.uint8)
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out = erase(gray, boxes=[(40, 40, 20, 20)], backend="lama")
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assert out.ndim == 2
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assert out.shape == gray.shape
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@pytest.mark.usefixtures("_fake_lama")
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def test_bgra_preserves_alpha(self):
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bgra = np.full((100, 100, 4), 120, np.uint8)
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bgra[..., 3] = 200 # opaque-ish alpha plane
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out = erase(bgra, boxes=[(40, 40, 20, 20)], backend="lama")
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assert out.shape == bgra.shape
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assert np.array_equal(out[..., 3], bgra[..., 3]) # alpha carried through unchanged
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