- Replace reverse-alpha removal with localize -> fill (template-free mask + one
shared cv2/MI-GAN/big-LaMa fill) for every mark; drops the colour-shift / dark-pit
failure modes, version-robust to a moved or re-rendered mark
- Separate perception/decision/action: engines report Candidates, a pure
decide(candidates, Context) arbiter owns all policy (sensitivity + provenance +
pill gate), remove_auto_marks orchestrates -- behavior-preserving (corpus 46/46/92)
- Three orthogonal knobs replace --method: --backend cv2|migan|lama,
--sensitivity auto|strict|assume-ai, provenance (auto from metadata)
- Add high-level api.remove_visible / visible_provenance (lazy top-level re-export);
visible --mark auto delegates to it so CLI and library share ONE path
- Read+write HEIC/AVIF on the pixel path via pillow-heif; imwrite preserves the input
format at max quality (JPEG q100/4:4:4); a no-op copies the original bytes verbatim
- Lossless byte-level JPEG metadata strip (no DCT re-encode); consolidate the two
remove_ai_metadata into one, delete legacy noai/cleaner + best_auto_mark
- Bump 0.13.0 -> 0.14.0
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
- Add the Jimeng-basic top-left "AI生成" pill as a CAPTURE-LESS mark
(pill_engine.py): synthetic-silhouette edge-NCC detect + inpaint-only removal.
Gated in remove_auto_marks: kept only when Jimeng is confirmed (TC260 metadata
OR the bottom-right "★ 即梦AI" wordmark fired -- the wordmark keeps recall on
metadata-STRIPPED uploads) AND Doubao did not fire.
- Add an inpaint-fallback removal path + MI-GAN ONNX backend (migan extra, MIT,
~28 MB / ~1 GB peak -- droplet-friendly) alongside big-LaMa. New
--method auto|reverse-alpha|inpaint (shared across visible/all/batch) and
erase --backend migan; footprint_mask on each engine.
- auto is deterministic: reverse-alpha for capture marks (recovers exact pixels,
lighter -- measured cleaner than MI-GAN on structured backgrounds) and inpaint
only for the capture-less pill.
- --mark auto now removes EVERY detected mark in one pass (remove_auto_marks),
so a Jimeng-basic image's top-left pill AND bottom-right wordmark both clear.
- Bump 0.12.1 -> 0.13.0.
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
extract_c2pa_info now uses the c2pa-python Reader first (any container, whole
manifest store incl. ingredient manifests), falling back to the hand-rolled caBX
parser for blobs the validator rejects (synthetic/partial, broken wheel). The
issuer/source-type/SynthID/soft-binding registry scan is shared by both paths
(_populate_registry_fields), so the return-dict contract is unchanged. Also
replaces the dead `from c2pa import has_c2pa_metadata` import in metadata.py with
a real Reader presence check. c2pa-python added as a core dep (MIT/Apache, ~+5MB
RSS, no torch; wheels cover the CI matrix).
Validated on the full local spaces corpus (25,725 imgs): 0 regressions; 384
manifests newly parsed (379 non-PNG JPEG/WebP + 2 PNGs the byte-scanner missed);
3 false Adobe/Microsoft->Google attributions fixed via real-manifest parsing.
The docs/module-internals.md section for this change already landed in 41f6797.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Research-informed metadata for organic dev discovery:
- pyproject: add a keywords field (was absent; biggest PyPI search gap) and
expand classifiers (audience, console, security, AI, utilities); rewrite the
summary noun-first, naming Nano Banana / SynthID / C2PA verbatim.
- README: add PyPI version, Python versions, downloads, and license badges.
GitHub topics (comfyui, watermark-remover) and the repo description were
updated out of band. PyPI metadata ships on the next release.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
BREAKING:
- Drop `--restore-faces` / `--restore-faces-method` CLI flags
- Drop `restore`, `photomaker`, `instantid` extras
- Drop `restore_faces` / `restore_faces_method` params from
InvisibleEngine.remove_watermark and AutoConfig
Rationale (full empirical record in
docs/synthid-robust-identity-research-2026-06-08.md "Empirical follow-up"):
every face-restore approach evaluated 2026-06-04 - 2026-06-08 (GFPGAN-on-
cleaned, PhotoMaker-V2, InstantID txt2img, InstantID img2img-on-cleaned
at three parameter sweeps) regenerates the face via SDXL diffusion --
output face pixels are diffusion-fresh, so the regenerated face inherits
SDXL's "clean skin" aesthetic and loses original identity precision. The
result looks MORE AI-generated than the cleaned image, not less. The
cleaned controlnet 0.20 image is the least-AI face state we can reach
without re-introducing SynthID.
License:
- MIT -> Apache 2.0 (Apache adds an explicit patent grant + trademark
clause; better fit with the upstream Apache projects this library
mirrors / depends on -- diffusers, transformers, controlnet-aux,
xinsir's controlnet weights)
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Replace LICENSE with the canonical Apache License 2.0 text + a brief
copyright notice for "wiltodelta 2025-2026". Update pyproject.toml's
`license` field to "Apache-2.0" and the PyPI classifier to "Apache
Software License". Update README's License section to point at the
LICENSE file and name the copyright holder.
Why: Apache 2.0 gives downstream users an explicit patent grant and the
trademark-use clause, which MIT doesn't carry. It is also the more
common license among the upstream projects this library depends on /
mirrors (diffusers, transformers, controlnet-aux, xinsir's canny
controlnet weights), so contributions can flow either way without a
permission-shape mismatch.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Empirical conclusion from the 2026-06-04 - 2026-06-08 Modal cert sweeps:
every face-restore approach we built (GFPGAN-on-cleaned, PhotoMaker-V2,
InstantID txt2img, InstantID img2img-on-cleaned at three parameter
settings) regenerates the face via SDXL diffusion rather than preserves
it. Output face pixels are diffusion-fresh, so the regenerated face
inherits SDXL "clean skin" aesthetic and loses original identity
precision -- it looks MORE AI-generated than the cleaned image, not
less. The cleaned image from the main controlnet 0.20 removal pass is
the least-AI face state we can reach without re-introducing SynthID.
Nothing in the restore family achieves the actual goal (preserve the
original person's face). Keeping them around as opt-in invites users to
ship something that defeats the point. Removing entirely.
Library changes:
- Deleted src/remove_ai_watermarks/instantid_restore.py
- Deleted src/remove_ai_watermarks/photomaker_restore.py
- Deleted tests/test_instantid_restore.py
- Deleted tests/test_photomaker_restore.py
- Removed `instantid` and `photomaker` extras from pyproject.toml
- Removed `[tool.hatch.metadata] allow-direct-references = true` (was
only needed for the photomaker git+ URL)
- InvisibleEngine.remove_watermark: dropped `restore_faces` +
`restore_faces_method` params, removed both `_restore_faces_instantid`
and `_restore_faces_photomaker` private methods, removed dispatch
- CLI: dropped `_restore_faces_options` decorator, all four cmd_*
signatures lose `restore_faces` + `restore_faces_method`, kwarg passes
to remove_watermark dropped
- _apply_auto: dropped `restore_faces` from tuple shape (was unused after
the engine no longer takes it)
- auto_config.AutoConfig: dropped `restore_faces` field; `plan()` no
longer sets it; `reason` no longer mentions it
- Tests updated accordingly (test_auto_config.TestReason no longer asserts
"face-restore on" in the reason string)
Docs updated:
- CLAUDE.md: removed the photomaker extras bullet, the Face restore
trade-off bullet, the instantid_restore.py + photomaker_restore.py
module bullets; replaced restore mentions in watermark_remover and
controlnet bullets and prod recipe with the empirical conclusion
- README.md: removed both `--restore-faces` callouts and the install
snippet; the feature bullet and auto-mode comment updated
- docs/synthid-robust-identity-research.md: added Status-retired notice
at the top pointing at the 2026-06-08 followup
raiw-app:
- modal_cert.py: dropped `--restore-faces` flag entirely; sweep() no
longer takes restore_faces; pinned _LIB_SPEC to `[gpu]` extras (no
`photomaker` / `instantid` extras), points at main
ruff + strict pyright clean; 569 tests pass; 18 restore-specific tests
gone.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Per the 2026-06-08 deep-research synthesis (docs/synthid-robust-identity-
research-2026-06-08.md), the entire ArcFace-class identity-adapter ecosystem
for SDXL is blocked from commercial use by InsightFace's non-commercial model
packs (antelopev2 / buffalo_l). No commercial-safe ArcFace-grade identity
stack exists today. The user explicitly opted into shipping a non-commercial
restore path (research / personal use; raiw.cc must NOT install the extra).
Architectural choice: InstantID over PhotoMaker-V2 as the default.
- PhotoMaker-V2 (CLIP+ArcFace dual encoder, txt2img only): documented upstream
identity drift on Asian male faces, visually confirmed in our cert sweep
(tatsunari rendered as a generic woman; group photo collapsed into a
patchwork).
- InstantID (ArcFace cross-attention + landmark ControlNet): semantic
identity branch + spatial weak landmark control, decoupled. Per InstantID
paper (arXiv:2401.07519) and the research report, stronger identity fidelity
on single portraits. Critically: NO original face pixels enter the diffusion
(ArcFace embedding is semantic, landmark stick figure is pure geometry), so
SynthID is not transported.
Implementation:
- New `src/remove_ai_watermarks/instantid_restore.py` mirrors the
`photomaker_restore.py` shape (lazy singletons for pipeline + FaceAnalysis,
per-face crop + _composite_faces from photomaker_restore). Loads the
InstantID community pipeline via `DiffusionPipeline.from_pretrained(
custom_pipeline="pipeline_stable_diffusion_xl_instantid")` -- no upstream
Python package needed; diffusers fetches the file from its community
examples.
- New `instantid` extra in pyproject (insightface + onnxruntime +
huggingface-hub). NON-COMMERCIAL block in the comment explains why.
- CLI: `--restore-faces-method [instantid|photomaker]`, default `instantid`.
Both methods explicitly labeled NON-COMMERCIAL in the help text.
- Engine: dispatch on `restore_faces_method` to either
`_restore_faces_instantid` or `_restore_faces_photomaker`.
- 9 control-flow tests for InstantID without model download (mirror the
photomaker_restore.py test pattern + draw_kps helper checks). 587/587 pass.
Diffusers-0.38 compat verified by upstream code inspection: the InstantID
pipeline inherits from `StableDiffusionXLControlNetPipeline`, uses only
public diffusers APIs (`encode_prompt`, `prepare_image`, `prepare_latents`,
`get_guidance_scale_embedding`), uses legacy attention processor API which
diffusers preserves for backward compat. No PhotoMaker-V1-style internal
text_encoder access. End-to-end execution will be validated by the Modal
cert sweep in the next step.
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>
Modal cert sweep #4 got further -- PhotoMaker V1 components actually loaded
("Loading PhotoMaker v1 components [1] id_encoder ... [2] lora_weights") -- and
died on the next step: "PEFT backend is required for this method." That's
diffusers' fuse_lora call gated on the peft library, which PhotoMaker doesn't
declare in its install_requires either.
Pin peft>=0.10.0 in the photomaker extra.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
The upstream PhotoMaker package's `__init__.py` unconditionally imports a
face-analyser class from its `insightface_package` submodule, so JUST importing
`PhotoMakerStableDiffusionXLPipeline` (the V1 pipeline class we use) raises
`ModuleNotFoundError: No module named 'insightface'` if insightface isn't
present in the env. The Modal cert sweep caught this on the V1 image.
Resolution: pin `insightface>=0.7.3` (and its `onnxruntime` runtime dep) in the
`photomaker` extra. The PyPI insightface package is MIT-licensed CODE; the
non-commercial restriction sits on the pretrained model packs (antelopev2,
buffalo_l) which download only when `FaceAnalysis()` is instantiated. Our V1 path
never instantiates the face-analyser -- it loads photomaker-v1.bin (CLIP-only
encoder) via `load_photomaker_adapter` -- so the model-pack license does not
bind us; we depend only on the MIT code for the import to resolve.
Safety guards:
- Runtime check in `_get_pipeline`: raises if `_PHOTOMAKER_FILE` is ever pointed
at v2 (so a future maintainer can't silently regress to the InsightFace path).
- New test class `TestV1OnlyCommercialSafetyGuard`: asserts repo + filename
pin to V1 AND asserts the module source never references the face-analyser
class (a static check that our codepath stays out of the runtime that would
pull the non-commercial model packs).
Docs: documented the import dance + legal split inline at the top of
`photomaker_restore.py`.
ruff clean; 581 tests pass (the 9 PhotoMaker tests plus 3 new V1-guard tests).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
PhotoMaker imports einops in its forward path but its install_requires doesn't
declare it, so the photomaker extra resolved without einops on a clean install
and the Modal cert sweep died at the restore-faces step with
"No module named 'einops'" -- the post-pass failed gracefully and returned the
un-restored cleaned output, so the cert artifact had no face recovery.
Pin einops>=0.7.0 in the photomaker extra so the extra is self-contained.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
