Every code-referencing claim in the docs, the README and the rules files was
checked against src/, and each finding was re-derived independently before it
was applied. 35 held, 5 were false positives.
Two of them were code, not text. `InvisibleOptions` promises in its docstring to
mirror `InvisibleEngine`, and two defaults had silently stopped:
`max_resolution=None` reached `_target_size`'s `max_resolution > 0` and raised
`TypeError` on every library call that left the options alone, and
`cpu_offload=True` made a library run slower than the identical CLI run. Both are
fixed, and `TestInvisibleOptionsMirrorTheEngine` compares the two signatures
field by field rather than pinning the two values that happen to be known. A
companion assertion in `TestTargetSize` reads the engine's own declared default,
so a drift on the engine side -- which the mirror check alone would accept,
because both sides would still agree -- fails too.
The user-facing docs: README called `invisible` GPU-optional where it raises
without CUDA, and gave the image `metadata` command `video metadata`'s output
rule, promising the source survives a command that overwrites it. Yuanbao was
missing from the supported-mark list. `veo` was listed among the video policies
that require a run anchor, though its row sets no `anchor_iou`.
`known-limitations` called ControlNet the default profile and contradicted
itself ninety lines below. An unescaped pipe truncated the `hailuo` table row.
The `dev` extra, the CI shape, ffmpeg's role, the sdist boundary and the
strength-curve range were corrected, and `remove_all`/`remove_batch`, the pill
gate, `erase --keep-metadata` and `all`'s CUDA failure mode were documented.
Research notes that described removed modules, extras and flags in the present
tense now say so once in the page banner instead of sentence by sentence, which
covers the whole page rather than the lines that happened to be noticed, and one
fixture is referred to by role rather than by name.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Empirical conclusion from the 2026-06-04 - 2026-06-08 cert sweeps:
every shipped face-restore method (GFPGAN-on-cleaned, PhotoMaker-V2,
InstantID txt2img, InstantID img2img-on-cleaned at three parameter
settings) regenerates the face from an ArcFace embedding via SDXL
diffusion. Output face pixels are diffusion-fresh, which makes the
regenerated face look MORE AI-generated than the cleaned image (gloss,
symmetric pores, SDXL "clean skin" aesthetic) regardless of license.
The cleaned image from the main controlnet 0.20 removal pass is the
LEAST-AI state we can reach without re-introducing SynthID; any restore
on top trades original-look for embedding-driven regeneration. The
fundamental issue is structural: ArcFace encodes "general look" at 512
dimensions, SDXL decodes that into pixels with the inherent SDXL
aesthetic. Stronger identity push (higher strength + IP-Adapter scale)
makes the face closer to the embedding but more AI-looking; weaker push
leaves identity to drift further. No parameter setting recovers original
identity AND looks less AI than cleaned.
Production conclusion: do not ship `--restore-faces` in any monetized
deployment. The extras (`instantid`, `photomaker`) stay in the library
for research / personal use where users explicitly want regeneration.
Documented at every entry point:
- CLAUDE.md: new "Face restore trade-off" bullet + every restore mention
rewritten to "REGENERATES, does NOT recover"; controlnet bullet updated
- README.md: feature bullet + callout + secondary mention all updated
- docs/synthid-robust-identity-research-2026-06-08.md: appended
"Empirical follow-up" section documenting the InstantID sweep phases
(Phase 1 txt2img v1/v2/v3, Phase 2 img2img defaults + stronger params)
- docs/controlnet-removal-pipeline-research.md: updated restore-faces
bullet to reflect the empirical conclusion
- CLI help: `_restore_faces_options` docstring + `--restore-faces` /
`--restore-faces-method` help text all updated
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Visual review of the GFPGAN-on-cleaned output (9-face grid, 1448x1086) showed it
only polished the already-drifted face without restoring identity — useless for the
"restore who is in the photo" intent. Dropping it.
The shipped restore path is now PhotoMaker-V2, which delivers true identity-from-
embedding face regeneration via a CLIP+ArcFace dual encoder. The ArcFace branch
pulls InsightFace antelopev2/buffalo_l model packs at runtime, which InsightFace
releases under a research-only license, so the whole extra is **NON-COMMERCIAL**.
raiw.cc and any monetized deployment must NOT install the `photomaker` extra.
This is called out at every entry point: CLI flag help, module docstring,
pyproject extra block, CLAUDE.md extras bullet, README install snippet.
Changes:
- Deleted `src/remove_ai_watermarks/face_restore.py` and its tests.
- Deleted the `restore` extra (gfpgan/facexlib/basicsr + scipy<1.18 / numba<0.60
pins) and the basicsr setuptools<69 build pin from pyproject.toml.
- Restored `src/remove_ai_watermarks/photomaker_restore.py` (V2 this time:
`TencentARC/PhotoMaker-V2`, `photomaker-v2.bin`, no `pm_version='v1'` override).
- Restored the `photomaker` extra in pyproject with all the upstream-compat
pins (einops, peft, onnxruntime, insightface) and the `allow-direct-references`
hatch metadata block.
- `InvisibleEngine` swapped `_restore_faces` -> `_restore_faces_photomaker`;
`--restore-faces-method` removed (only one method, no choice).
- CLI flag help, CLAUDE.md, README, docs/synthid.md, and
docs/controlnet-removal-pipeline-research.md all updated.
- docs/synthid-robust-identity-research.md status notice rewritten to list both
abandoned commercial-safe attempts (V1 + GFPGAN-on-cleaned) and the
non-commercial trade-off we accepted.
ruff + strict pyright(src/) clean; 578 tests pass (the 9 GFPGAN tests are gone,
the 11 PhotoMaker tests stay green).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
After 7 cascading upstream-compat fixes (insightface dep, peft dep, pm_version,
device, etc.), the PhotoMaker V1 cert sweep still hit a CFG batch-dim mismatch
inside the denoising loop. The upstream PhotoMaker `pipeline.py` is forked from
diffusers v0.29.1 and our env runs 0.38; SDXL prompt-encoder handling changed
significantly between those versions, so making PhotoMaker work end-to-end
needs a proper fork or a diffusers downgrade — both expensive. Not worth
shipping today.
Pivot: restore `face_restore.py` (GFPGAN) with a single-line fix that makes it
SynthID-safe by construction. The previous design ran GFPGAN.enhance on the
ORIGINAL watermarked image and was oracle-confirmed to re-add SynthID via the
weight-0.5 pixel blend. The fix is to run GFPGAN on the diffusion-CLEANED
image — whatever pixels GFPGAN derives from are already SynthID-free, so the
partial blend cannot transport the watermark. Identity fidelity is lower than
a true identity-as-embedding stack would deliver, but it ships and works.
Changes:
- `src/remove_ai_watermarks/face_restore.py` restored from pre-wipe state with
one line changed: `restorer.enhance(cleaned_bgr, ...)` instead of
`restorer.enhance(original_bgr, ...)`. `original_bgr` is kept as an unused
positional argument for API stability.
- `src/remove_ai_watermarks/photomaker_restore.py` and its tests REMOVED. The
research note (`docs/synthid-robust-identity-research.md`) keeps a "status
notice" documenting why PhotoMaker is parked for now and what the path back
in would look like.
- `pyproject.toml` `restore` extra restored (gfpgan/facexlib/basicsr +
scipy<1.18 + numba<0.60 pins + the basicsr setuptools<69 build pin), plus
`photomaker` extra (with its einops/insightface/peft pile) and the
`[tool.hatch.metadata] allow-direct-references = true` block REMOVED.
- `InvisibleEngine._restore_faces_photomaker` removed; `_restore_faces`
restored. The `--restore-faces` CLI flag and its plumbing through cmd_*
signatures are unchanged.
- CLAUDE.md, README.md, docs/synthid.md, docs/controlnet-removal-pipeline-
research.md updated to describe the shipped GFPGAN-on-cleaned design and to
reference PhotoMaker only as the parked alternative.
ruff + strict pyright(src/) clean; 578 tests pass.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
A Modal cert sweep caught what the research doc missed: PhotoMaker-V2 fails at
import without InsightFace ("No module named 'insightface'"). Reading the upstream
source confirms it: `photomaker/__init__.py` imports `FaceAnalysis2` (an InsightFace
wrapper) at module load, V2's encoder is named
`PhotoMakerIDEncoder_CLIPInsightfaceExtendtoken`, and `model_v2.py`'s forward
takes an `id_embeds` argument that the pipeline computes via
`insightface.app.FaceAnalysis(name='antelopev2', ...)`. So V2 is a DUAL encoder
(CLIP + ArcFace), not CLIP-only as the model card line "id_encoder includes
finetuned OpenCLIP-ViT-H-14 and a few fuse layers" implied.
InsightFace's pretrained model packs (antelopev2, buffalo_l) are research/
non-commercial only per their own README:
"The pretrained models we provided with this library are available for
non-commercial research purposes only."
So V2 is blocked for a paid service like raiw.cc.
PhotoMaker-V1 is the commercial-safe alternative — its `PhotoMakerIDEncoder`
(model.py) forward takes only `(id_pixel_values, prompt_embeds, class_tokens_mask)`,
no ArcFace branch. Identity is CLIP-only, license is Apache-2.0, no InsightFace.
Code change: swap the repo + filename constants in `photomaker_restore.py`
(TencentARC/PhotoMaker, photomaker-v1.bin). Tests still pass (the 9 PhotoMaker
tests use a fake pipeline, so the model swap is transparent to them).
Doc correction: rewrote the verdict / license table / section 5 of
`docs/synthid-robust-identity-research.md` to lead with V1 and add a correction
notice explaining the V2 misread. Bulk-renamed `PhotoMaker-V2` to `PhotoMaker-V1`
across CLAUDE.md, README.md, docs/synthid.md, and
docs/controlnet-removal-pipeline-research.md (kept V2 only in the correction
notice, the license table, and the anchor reference).
ruff clean; 578 tests pass.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
The GFPGAN `restore` extra and its `face_restore.py` module are gone. They were
oracle-confirmed to re-introduce SynthID by blending watermarked original face
pixels at fidelity weight 0.5 (clean A/B: gemini_3 controlnet 0.20 detected WITH
GFPGAN, clean WITHOUT). Keeping them as the default restore method was a footgun
for the removal pipeline. PhotoMaker-V2 (added in the previous commit) is the
single shipped restore path now -- identity-as-embedding, SynthID-safe by
construction.
Removed:
- src/remove_ai_watermarks/face_restore.py + tests/test_face_restore.py
- pyproject.toml `restore` extra (gfpgan/facexlib/basicsr + scipy/numba pins)
- pyproject.toml `[tool.uv.extra-build-dependencies] basicsr = [...]` build pin
- CLI: `--restore-faces-method` and `--restore-faces-weight` (no method choice
to make, no GFPGAN weight knob to expose)
- InvisibleEngine._restore_faces method (only _restore_faces_photomaker remains)
- All restore-faces-method / restore-faces-weight threading through cmd_*
signatures and _process_batch_image
Kept:
- `--restore-faces / --no-restore-faces`: now binds to PhotoMaker-V2.
- All adopted oracle findings about GFPGAN re-introducing SynthID (kept in the
research docs as historical context that explains why the path was removed).
Docs updated: CLAUDE.md (restore extras bullet collapsed to photomaker, removed
face_restore Key-modules bullet, several inline GFPGAN refs scrubbed), README.md
(face-identity callout + install section now point to the photomaker extra),
docs/synthid.md 5.5 (net recipe), docs/controlnet-removal-pipeline-research.md
(recommendations).
ruff + strict pyright (src/) clean; 578 tests pass (the 9 GFPGAN tests are gone,
the 9 PhotoMaker tests stay green).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Ran the isolated raiw-controlnet-cert Modal app (raiw-app/modal_cert.py) over a
strength x seed grid, restore OFF, --max-resolution 1536, each vendor checked on its
OWN oracle (OpenAI -> openai.com/verify, Gemini -> the Gemini app). Certified
controlnet SynthID-removal floors:
- OpenAI 0.20: 2 photoreal images (9-face grid + bracelet) x seed {1,2,3} = 6/6 clean;
the bracelet that flipped at 0.15 is seed-robust at 0.20. Transfers to prod (OpenAI
removal is resolution-independent).
- Gemini 0.30: 0.20 detected -> 0.30 clean on 2/2 seeds (hardest face). Holds only at
<= 1536; Gemini is resolution-sensitive and raiw.cc runs NATIVE, so cap Gemini
<= 1536 + use 0.30, or native-calibrate (~0.35+).
Prod recipe recorded: controlnet + a controlnet-specific per-vendor schedule in
resolve_strength (OpenAI 0.20 / Gemini 0.30, NOT the default 0.10/0.15 ladder) +
FIXED prod seed (kills the near-threshold non-determinism) + restore reworked/off.
Added to docs/controlnet-removal-pipeline-research.md (certified floors table),
docs/synthid.md 5.5, and the CLAUDE.md controlnet bullet. Docs only.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Oracle validation (openai.com/verify + the Gemini app) overturned three claims that
were on main, and consolidates the controlnet findings into one authoritative place.
- controlnet does NOT reliably remove SynthID at the low vendor-adaptive strength:
removal is content x pipeline dependent and the survivors FLIP by content type
(photoreal survives controlnet / clears default; flat graphic survives default /
clears controlnet; flat text clears both). Root cause is insufficient strength,
not the pipeline; controlnet needs a higher, per-vendor floor than default.
- removal near the threshold is SEED-non-deterministic (same image+pipeline+strength
can pass or fail run-to-run); a single clean run does not certify a strength.
- `--restore-faces` RE-INTRODUCES SynthID: GFPGAN runs on the ORIGINAL watermarked
face at weight 0.5 and composites it back over the cleaned result (clean A/B:
a Gemini face stayed detected through controlnet 0.15/0.20/0.25 WITH restore,
cleared at 0.20 with --no-restore-faces). The old "GFPGAN scrubs SynthID" claim
was wrong.
Corrected in CLAUDE.md (watermark_remover controlnet bullet, controlnet
Known-limitations bullet, face_restore bullet, vendor-adaptive strength bullet) and
docs/synthid.md (5.1 controlnet/face-identity, 5.2 strength floors, new 5.5 oracle
validation log). docs/controlnet-removal-pipeline-research.md gains an authoritative
"Oracle validation 2026-06-04" section that the others point to as the single source.
Docs only; no code change.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Add `--pipeline controlnet` (SDXL base + xinsir canny ControlNet via
StableDiffusionXLControlNetImg2ImgPipeline): the canny edge map conditions the
img2img regeneration so text and face STRUCTURE stay sharp, while the watermark
is still removed by the regeneration (`strength`) -- no original pixels are
copied or frozen, so SynthID does not survive. Oracle-verified clean on OpenAI
with better text/structure fidelity than plain img2img at equal strength.
`--controlnet-scale` tunes structure preservation; fp32 on mps/cpu (fp16-fixed
VAE on cuda/xpu). Shares the img2img runner (live progress + MPS->CPU fallback)
and the fp16-VAE-fix / device-move helpers with the default pipeline.
Remove the superseded subsystems -- ctrlregen (SD1.5 clean-noise),
text-protection (differential / region-hires) and face-protection: they either
destroyed real content or shielded the watermark by re-using original pixels.
controlnet replaces them by regenerating everything under edge conditioning.
Canny preserves face structure but not identity; face IDENTITY is a separate
face-restoration post-pass (CodeFormer/GFPGAN), researched + prototyped but not
yet shipped. An IP-Adapter FaceID attempt was built and removed (footgun: needs
high strength, corrupts faces at removal strength).
Docs: docs/controlnet-removal-pipeline-research.md, scripts/controlnet_sweep.py.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
