mirror of
https://github.com/wiltodelta/remove-ai-watermarks.git
synced 2026-07-05 07:57:50 +02:00
Victor Kuznetsov
d5dd24140c
- watermark_remover: _build_qwen_kwargs now passes explicit height/width (via _qwen_target_size, floored to /16). Without it QwenImageImg2ImgPipeline defaults to 1024x1024 and silently squishes non-square inputs, distorting the scene and garbling text. - watermark_profiles: resolve_strength gains a `pipeline` arg + a Qwen strength ladder (_QWEN_VENDOR_STRENGTH, Gemini 0.25), so `--pipeline qwen` gets its certified floor automatically; retires the manual "pass --strength 0.25 for Gemini on qwen" workaround. - fidelity_metrics: replace per-face nearest matching (collided on multi-face images when a variant dropped a face, corrupting the identity metric) with a collision-free one-to-one assignment (assign_faces_one_to_one). lapvar/LPIPS were always bbox-anchored and immune. Regression-guarded by tests/test_fidelity_matching.py. - docs: record the measured outcomes of the qwen-improvement arc. The Qwen ControlNet face-fix is CLOSED (no permissive Qwen detail/tile ControlNet exists; canny carries edges, not skin grain). The `--pipeline auto` router + faces+text mixed dual-pass were prototyped and DROPPED (controlnet wins faces AND display text: abba CER 0.114 vs qwen 0.379). Z-Image-Turbo was tried and dropped (same regeneration limits). qwen stays a manual opt-in; controlnet is the default for everything. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
77 lines
3.0 KiB
Python
77 lines
3.0 KiB
Python
"""Regression test for the one-to-one face matcher in ``scripts/fidelity_metrics.py``.
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The shipped per-face nearest matcher collided on multi-face images (two original faces
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both picking the same variant face when regeneration dropped a face), which inflated/
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corrupted the identity metric. ``assign_faces_one_to_one`` is the collision-free
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replacement. The function is pure (centers + diagonals in, index map out), so it is
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tested here without insightface / the heavy PEP723 env. Caught on the gemini_3 Qwen
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ControlNet experiment, where the original had 18 faces but the regenerated variants had
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17, producing two collisions under the old matcher.
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"""
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from __future__ import annotations
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import importlib.util
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import sys
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from pathlib import Path
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import pytest
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_SCRIPTS = Path(__file__).resolve().parent.parent / "scripts"
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def _load_assign():
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# fidelity_metrics is a standalone PEP723 script, not an installed module; load it by
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# path with scripts/ on sys.path so its `_plain_console` shim import resolves.
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sys.path.insert(0, str(_SCRIPTS))
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try:
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spec = importlib.util.spec_from_file_location("fidelity_metrics", _SCRIPTS / "fidelity_metrics.py")
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assert spec is not None
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assert spec.loader is not None
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mod = importlib.util.module_from_spec(spec)
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sys.modules[spec.name] = mod # @dataclass introspection needs the module registered
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spec.loader.exec_module(mod)
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except ImportError as exc: # cv2/click absent in a bare env -> skip, not fail
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pytest.skip(f"fidelity_metrics import deps missing: {exc}")
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finally:
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sys.path.remove(str(_SCRIPTS))
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return mod.assign_faces_one_to_one
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def test_distinct_faces_match_nearest() -> None:
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assign = _load_assign()
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ref = [(0.0, 0.0), (100.0, 100.0)]
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var = [(2.0, 1.0), (98.0, 102.0)]
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diags = [50.0, 50.0]
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assert assign(ref, var, diags) == {0: 0, 1: 1}
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def test_no_collision_when_variant_drops_a_face() -> None:
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# Two original faces near the SAME single variant face: the old nearest matcher mapped
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# BOTH to index 0; one-to-one must give the nearer ref the match and drop the other.
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assign = _load_assign()
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ref = [(10.0, 10.0), (14.0, 10.0)] # both close to the lone variant
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var = [(12.0, 10.0)]
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diags = [50.0, 50.0]
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matched = assign(ref, var, diags)
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assert sorted(matched.values()) == [0] # variant 0 used at most once
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assert len(matched) == 1
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def test_gate_drops_implausibly_far_match() -> None:
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assign = _load_assign()
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ref = [(0.0, 0.0)]
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var = [(1000.0, 1000.0)] # far beyond 0.6 * diag
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diags = [50.0]
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assert assign(ref, var, diags) == {}
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def test_assignment_is_one_to_one_over_many_faces() -> None:
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assign = _load_assign()
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ref = [(float(i * 100), 0.0) for i in range(18)]
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var = [(float(i * 100) + 3.0, 0.0) for i in range(17)] # one fewer, as in the experiment
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diags = [50.0] * 18
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matched = assign(ref, var, diags)
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assert len(matched) == 17
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assert len(set(matched.values())) == 17 # every variant used at most once
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