Oracle seed-repeat + floor refinement (2026-06-20, data/qwen_in):
- OpenAI floor 0.10 is SEED-ROBUST: 0.05 and 0.075 still detected; 0.10 clean on
seeds 0-4 (5/5) -> a random seed is safe.
- Gemini floor lowered 0.30 -> 0.25 (0.20 still detected, 0.25 clean on both
images). Single-seed (seed 0): the Gemini oracle rate-limits volume seed-repeat,
so pin a seed in prod rather than relying on seed-robustness there.
Re-measured fidelity at the certified floors (controlnet 0.15 vs Qwen 0.25 for
Gemini): faces still favor controlnet (ArcFace 0.546 vs 0.382, lapvar 0.62 vs
0.40); the short-CJK text case is now a TIE (gemini_1 0.037 vs 0.037 -- the earlier
Qwen 0.000 was at 0.30, not the floor). Qwen's text win holds on substantial
Latin/mixed text (OpenAI 0.385 vs 0.241 / 0.341 vs 0.290). Update watermark_profiles
comment, CLAUDE.md, module-internals, known-limitations.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The face fidelity numbers cited an equal-strength compare (both 0.15), but Qwen at
0.15 does NOT clear Gemini SynthID -- so that output is un-scrubbed and the compare
is invalid. Per the methodology rule (compare fidelity only between outputs where
SynthID is removed in BOTH), restate faces at each pipeline's scrub floor
(controlnet 0.15 / Qwen 0.30): ArcFace identity 0.546 vs 0.331, lapvar 0.62 vs 0.40,
face LPIPS 0.09 vs 0.19 -- controlnet still wins faces, conclusion unchanged. Drop
the "equal strength" framing in CLAUDE.md / module-internals / known-limitations.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
data/qwen_in/ground_truth.json is transcribed by vision (PaddleOCR mangled the
stylized Cyrillic), so the text metric scores variants against an accurate
reference instead of noisy OCR-vs-OCR. Re-measured text CER (controlnet vs qwen)
with this ground truth confirms qwen wins text across EN/RU/ZH: openai_1 0.385 vs
0.241, openai_2 0.341 vs 0.290, gemini_1 (ZH) 0.037 vs 0.000 (perfect Chinese even
at the higher 0.30 strength). Faces still favor controlnet. Refresh the numbers in
docs/known-limitations.md to this cleaner methodology.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Add scripts/fidelity_metrics.py: an objective eval harness comparing
watermark-removal outputs against the original (reference) across four groups
-- OCR character error rate (EasyOCR), ArcFace identity cosine (insightface),
face texture (LPIPS + Laplacian-variance ratio), and whole-image LPIPS/SSIM/
PSNR. PEP 723 inline deps so it stays out of the package / uv.lock; metrics
self-gate (faces only where faces, text only where text).
The metrics overturned an eyeball conclusion: at EQUAL strength Qwen beats
controlnet on TEXT (OpenAI typography 0.10: OCR CER 0.25 vs 0.37) but controlnet
beats Qwen on FACES (gemini_3, 18 faces, 0.15 each: Laplacian-variance retention
0.62 vs 0.41, face LPIPS 0.09 vs 0.13 -- Qwen smooths faces MORE; ArcFace
identity ~tied). So Qwen is the better TEXT-preserving remover, not a universal
fidelity win. Correct the earlier "qwen keeps faces faithful where controlnet
plasticizes" claim in CLAUDE.md, module-internals.md, known-limitations.md, README.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
A third diffusion pipeline alongside sdxl/controlnet: Qwen-Image (20B MMDiT,
Apache-2.0 code AND weights) img2img. The scrub still comes from the img2img
strength; Qwen preserves text (incl. CJK) and structure markedly better than
SDXL at the scrub floor, so it over-regenerates real photos far less (directly
targets the controlnet over-regeneration that degrades real uploads).
- watermark_profiles: QWEN_MODEL_ID, normalize_profile accepts "qwen".
- WatermarkRemover: _load_qwen_pipeline (bf16, loads Qwen base unless --model
overridden, clear ImportError if diffusers lacks the class), _run_qwen (no
MPS fallback -- 20B is CUDA/cloud-class), dispatch in _generate_one/preload,
pure _build_qwen_kwargs (true_cfg_scale, not guidance_scale).
- Shared _base_load_kwargs() across all three loaders (dtype + token).
- CLI --pipeline gains "qwen"; invisible_engine threads it through.
- scripts/qwen_scrub_prototype.py: standalone PEP 723 GPU experiment.
Prototype oracle floors (Modal A100-80GB, single seed, controls SynthID-positive,
PENDING seed-repeat cert): OpenAI clears at strength ~0.10, Gemini at ~0.30 (0.20
still detected), with CJK text + faces faithful where controlnet plasticizes. The
Gemini floor is higher than the shared default ladder, so pass an explicit
--strength for Gemini on this pipeline until a Qwen-specific ladder is certified.
The model-running path is CUDA-only (untestable locally); unit tests cover the
pure call-shape (_build_qwen_kwargs) and profile normalization without torch.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Add a lossless alternative to the --max-resolution downscale for large
images that OOM on MPS/GPU: regenerate in overlapping, feather-blended
tiles at native resolution.
- noai/tiling.py: pure plan_tiles (uniform tiles, last flush to edge) +
feather_weights (strictly-positive separable taper -> partition-of-unity
blend) + run_tiled (per-tile generate callable, decoupled from the
pipeline). Unit-tested without the model.
- WatermarkRemover.remove_watermark: refactor _generate into _generate_one
+ a tiled branch that engages only when --tile is set and the long side
exceeds tile_size (ControlNet canny is rebuilt per tile).
- Thread tile/tile_size/tile_overlap through InvisibleEngine and the
invisible/all/batch CLI commands via a shared _tile_options decorator.
Verified end-to-end on the real SDXL pipeline (forced 2x2 tiling on a
1024px sample, MPS): non-degenerate output, no gross seam at tile borders.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Closes a documented coverage gap (P2#9): an AI Software/Make/Artist/ImageDescription
token in an EXIF item (its TIFF bytes live in mdat/idat) survived remove_ai_metadata
because the top-level box stripper and (absent pillow-heif) the PIL EXIF reader can't
reach it. New isobmff.blank_ai_exif_tokens finds EXIF TIFF blocks by their II/MM
byte-order header, validates each with piexif (a coincidental II/MM run in pixels
won't parse as a TIFF IFD, so it's ignored), and overwrites any AI_GENERATOR_TOKENS-
bearing value with same-length spaces -- so box sizes and iloc offsets stay valid and
the coded image is untouched (mirrors blank_ai_xmp_packets; no iinf/iloc surgery, no
exiftool dep). Camera/editor EXIF without an AI token is preserved. Wired into
remove_ai_metadata's ISOBMFF path. Covers the realistic AI-generator-token case; xAI-
signature-in-meta-box-EXIF (Grok is JPEG-only) stays out.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
A 2026-06-14 oracle re-test on the deployed Modal controlnet worker (v0.10.0)
cleared SynthID at OpenAI 0.10 (2 photoreal) and Google 0.15 (2 native
2816x1536, retiring the "native >= 0.30" guess), while a pixel sweep showed the
2026-06-04 cert floors (0.20/0.30) over-regenerated for no efficacy gain
(Google MAE -20% at 0.15). Lowers OPENAI_STRENGTH 0.20->0.10, GEMINI_STRENGTH
and UNKNOWN_STRENGTH 0.30->0.15.
Caveats documented in watermark_profiles.py + docs: removal near this floor is
seed-non-deterministic (a service must pin a verified seed), and the n=2 re-test
did not cover flat-graphic hard cases.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Victor Kuznetsov