From 439eeadc07716172c52fc7d779f59cc8ec15c747 Mon Sep 17 00:00:00 2001 From: Victor Kuznetsov Date: Mon, 8 Jun 2026 15:35:37 -0700 Subject: [PATCH] refactor(face-restore): wipe GFPGAN path, --restore-faces is PhotoMaker-only 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) --- CLAUDE.md | 21 +- README.md | 15 +- docs/controlnet-removal-pipeline-research.md | 8 +- docs/synthid.md | 5 +- pyproject.toml | 43 +- src/remove_ai_watermarks/auto_config.py | 2 +- src/remove_ai_watermarks/cli.py | 53 +- src/remove_ai_watermarks/face_restore.py | 191 --- src/remove_ai_watermarks/invisible_engine.py | 88 +- tests/test_face_restore.py | 85 -- uv.lock | 1191 +----------------- 11 files changed, 64 insertions(+), 1638 deletions(-) delete mode 100644 src/remove_ai_watermarks/face_restore.py delete mode 100644 tests/test_face_restore.py diff --git a/CLAUDE.md b/CLAUDE.md index 47dbed4..c9061c5 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -10,26 +10,25 @@ You are a **principal Python engineer** maintaining a CLI tool and library for r - `uv run remove-ai-watermarks identify ` — provenance verdict (platform + watermark inventory + confidence); `--json` for machine output, `--no-visible` to skip the cv2 sparkle detector - `uv run remove-ai-watermarks metadata --check` — inspect AI metadata (C2PA, EXIF, PNG chunks) - `uv run remove-ai-watermarks metadata --remove -o ` — strip all AI metadata -- `uv run remove-ai-watermarks batch ` — process every supported image in a directory (output defaults to `_clean/`, set with `-o`). `--mode visible|invisible|metadata|all` (default `visible`); the invisible/all path reuses the same `--strength`/`--steps`/`--pipeline`/`--controlnet-scale`/`--device`/`--max-resolution`/`--min-resolution`/`--upscaler`/`--seed`/`--hf-token` knobs as `invisible`, `--inpaint/--no-inpaint` for the visible pass, `--humanize` for the Analog Humanizer + `--unsharp` for the final sharpening post-filter, `--restore-faces/--no-restore-faces` + `--restore-faces-weight` for the GFPGAN face-identity post-pass, and `--auto` (+ `--adaptive-polish/--no-adaptive-polish`) for the content-adaptive quality mode (re-planned per image; one engine cached per resolved pipeline) +- `uv run remove-ai-watermarks batch ` — process every supported image in a directory (output defaults to `_clean/`, set with `-o`). `--mode visible|invisible|metadata|all` (default `visible`); the invisible/all path reuses the same `--strength`/`--steps`/`--pipeline`/`--controlnet-scale`/`--device`/`--max-resolution`/`--min-resolution`/`--upscaler`/`--seed`/`--hf-token` knobs as `invisible`, `--inpaint/--no-inpaint` for the visible pass, `--humanize` for the Analog Humanizer + `--unsharp` for the final sharpening post-filter, `--restore-faces/--no-restore-faces` for the PhotoMaker-V2 SynthID-safe face-identity post-pass (`photomaker` extra), and `--auto` (+ `--adaptive-polish/--no-adaptive-polish`) for the content-adaptive quality mode (re-planned per image; one engine cached per resolved pipeline) ## Test and lint - **CI** (`.github/workflows/test.yml`): runs on push to `main` + every PR. A `lint` job (ubuntu: `ruff check` + `ruff format --check`) plus a `test` matrix (ubuntu/macos/windows x py3.10/3.12) that does `uv sync --frozen --extra dev` then `pytest`. The matrix installs only core + dev (no `gpu` extra), so the GPU/model-running tests skip there and it exercises the metadata/identify/visible/cv2-eraser surface on all three OSes. Keep `uv.lock` valid (don't break `--frozen`) when editing `pyproject.toml`. `publish.yml` stays release-only and now verifies the release tag matches the `pyproject.toml` version (fails the build on a mismatch) before building, then uploads via `uv publish` (PyPI trusted publishing over OIDC, no token — replaced the `pypa/gh-action-pypi-publish` action so the upload no longer depends on that action's bundled twine accepting the Metadata-Version; the `id-token: write` permission + `pypi` environment + workflow filename are unchanged, so PyPI's trusted-publisher entry still matches). **Release flow:** bump the version in `pyproject.toml` + `src/remove_ai_watermarks/__init__.py` + `uv.lock` (the project's own `[[package]]` entry, ~line 2868), commit `chore(release): vX.Y.Z`, `git tag -a vX.Y.Z -m vX.Y.Z` (annotated — `git tag` without `-m` errors here), push `main` + the tag, then `gh release create vX.Y.Z` — **PyPI publish triggers on the GitHub Release `published` event, NOT on the tag push**, so the tag alone does not publish. **Sdist must exclude `data/`** (`[tool.hatch.build.targets.sdist] exclude = ["/data"]`): hatchling's default sdist bundles all VCS-tracked files, so the committed `data/` test corpora (the multi-hundred-MB synthid_corpus images + the visible-mark captures) pushed the **0.8.0** sdist past PyPI's per-project file-size limit (400 "File too large") — the wheel uploaded but the sdist was rejected, so 0.8.0 shipped wheel-only and 0.8.1 carried the fix. The wheel only ships `src/` (via `[tool.hatch.build.targets.wheel] packages`), so it was never affected. **A failed PyPI upload of one artifact still leaves the other live and you cannot re-upload the same version** — fix the build and cut the next patch. **Build backend is pinned `hatchling<1.31`** (`[build-system] requires`): hatchling **1.30.0** made **Metadata-Version 2.5** (PEP 794) the default, which the twine bundled in `pypa/gh-action-pypi-publish@release/v1` rejects (`"'2.5' is not a valid Metadata-Version"`) — this **failed the v0.8.3 PyPI upload on 2026-06-01** (tag-match + build passed, the upload step failed; nothing was uploaded, so the version stayed empty on PyPI), when unpinned `requires = ["hatchling"]` pulled 1.30.0. **hatchling 1.30.1 reverted the default back to 2.4** ("kept at 2.4 until more tools support 2.5"), and 1.27-1.29 always emitted 2.4 — so `<1.31` keeps `uv build` on a 2.4-emitting hatchling (it resolves to the latest allowed, **1.30.1**, which uploads fine). (The earlier "1.28+ emits 2.5" note was imprecise: the 2.5 default landed only in 1.30.0, verified against hatch's changelog.) The publish workflow **now uses `uv publish`** (its uploader handles 2.5), so this pin is no longer load-bearing — it stays as belt-and-suspenders so the first uv-publish release ships 2.4 metadata (isolating the uploader swap from the metadata-version bump); drop it to `requires = ["hatchling"]` once that release confirms the path. - `bash maintain.sh` — uv-outdated, uv-secure, ruff check/fix, ruff format, pyright, pytest -n auto -- **Strict pyright is clean across `src/` (0 errors).** The cv2/torch/diffusers boundary files (`gemini_engine`, `region_eraser`, `doubao_engine`, `humanizer`, `invisible_engine`, `noai/watermark_remover`) carry a documented per-file `# pyright:` relax pragma that turns off only the unknown-type / untyped-third-party rules — those libs ship no usable types, so strict typing there fights the ecosystem. Pure-logic files stay fully strict; `typings/piexif/__init__.pyi` is a local stub so `metadata.py`/`extractor.py` resolve piexif. Public ndarray-returning signatures on the relaxed engines are still annotated `NDArray[Any]` so strict consumers (`cli.py`) stay clean. When touching a relaxed file, prefer fixing real issues over widening the pragma; keep the pragma scoped to genuinely-untyped boundaries. (`uv-secure` is clean since idna was bumped 3.11 -> 3.16, fixing GHSA-65pc-fj4g-8rjx, and aiohttp 3.13.5 -> 3.14.0 via `uv lock --upgrade-package aiohttp`, fixing GHSA-hg6j-4rv6-33pg + GHSA-jg22-mg44-37j8. The remaining Dependabot alert is **basicsr** (GHSA-86w8-vhw6-q9qq, command injection, no patch, `<= 1.4.2`): accepted, not fixable -- basicsr is the optional `restore` extra pinned to 1.4.2 as the only buildable version, experimental and off by default, so it falls under uv-secure's `--ignore-unfixed`.) +- **Strict pyright is clean across `src/` (0 errors).** The cv2/torch/diffusers boundary files (`gemini_engine`, `region_eraser`, `doubao_engine`, `humanizer`, `invisible_engine`, `noai/watermark_remover`) carry a documented per-file `# pyright:` relax pragma that turns off only the unknown-type / untyped-third-party rules — those libs ship no usable types, so strict typing there fights the ecosystem. Pure-logic files stay fully strict; `typings/piexif/__init__.pyi` is a local stub so `metadata.py`/`extractor.py` resolve piexif. Public ndarray-returning signatures on the relaxed engines are still annotated `NDArray[Any]` so strict consumers (`cli.py`) stay clean. When touching a relaxed file, prefer fixing real issues over widening the pragma; keep the pragma scoped to genuinely-untyped boundaries. (`uv-secure` is clean since idna was bumped 3.11 -> 3.16, fixing GHSA-65pc-fj4g-8rjx, and aiohttp 3.13.5 -> 3.14.0 via `uv lock --upgrade-package aiohttp`, fixing GHSA-hg6j-4rv6-33pg + GHSA-jg22-mg44-37j8. (The basicsr Dependabot alert -- GHSA-86w8-vhw6-q9qq -- was retired together with the GFPGAN `restore` extra on 2026-06-04: basicsr was its transitive dep and no other code path needed it.) - **Full-project `uv run pyright` (no path) OOMs/crashes node on this ML-heavy repo** (emits a `libnode` stack frame, no summary) — a known environment limit, not a code error. Gate with `uv run --extra dev --extra gpu pyright src/` (completes, authoritative) or scope to changed files; also run `uv run ruff check` and `uv run pytest` directly. - Run `uv run` from the repo root — from another cwd it falls back to a bare env without numpy/cv2/torch. - To add a dev tool (pytest/ruff/pyright) into the env, use `uv sync --frozen --extra dev --extra gpu`, **never `uv pip install`** — `uv pip install` re-resolves and rewrites `uv.lock`, which silently bumped `transformers` to a build incompatible with the pinned `diffusers` (`cannot import name 'Qwen3VLForConditionalGeneration'`) and broke every `identify`/metadata import. Recovery: `git checkout uv.lock && uv sync --frozen --extra gpu --extra dev`. The `gpu` extra holds `diffusers`/`transformers`/`torch`, so a bare `uv sync` (no extras) removes them; `noai/__init__` is now **lazy** (PEP 562 `__getattr__`, so importing `identify`/`metadata` no longer pulls `watermark_remover`/torch), so a bare env breaks only when the removal pipeline is actually invoked, not on import. `maintain.sh`'s `uv sync --all-extras` also pulls the heavy `trustmark`/`lama` wheels (pytorch-lightning, onnxruntime) — fine on a good connection, but on flaky DNS sync only `--extra gpu --extra dev` and run the lint/test steps by hand. - Metadata/C2PA tests assert against real committed fixtures in `data/samples/` (`chatgpt-*.png` = OpenAI C2PA, `firefly-1.png` = Adobe, `mj-*` = Midjourney IPTC, `doubao-1.png` = ByteDance Doubao with the China TC260 `` XMP label **and** a visible "豆包AI生成" text mark bottom-right; `grok-1.jpg` = xAI Grok with its EXIF-only `Signature:` blob + UUID `Artist` and no C2PA/SynthID/IPTC); synthetic byte blobs cover the JPEG/ISOBMFF format paths. The "non-AI / clean photo" control is no longer in `data/samples/` -- the `clean_photo` conftest fixture serves a verified-negative image from the corpus `neg/` set (skips if the corpus is absent). -- SynthID reference corpus: `scripts/synthid_corpus.py` ingests labeled images into `data/synthid_corpus/`. The labeled `images/` (`pos/` `neg/` `cleaned/`) are **committed** (public repo -- review every image for private content before adding; `manifest.csv` is kept in sync with the files on disk, one row per tracked image); only the synthetic `refs/` calibration fills are gitignored. See its README for the collection protocol and verification oracles. **`cleaned/` examples must be produced by a CURRENT shipped removal method** -- the default SDXL img2img pass (optionally `--max-resolution`). Do NOT archive cleaned outputs from methods that are no longer in the pipeline (ctrlregen, the old text/face-protection, IP-Adapter FaceID, CodeFormer) or from the experimental opt-in paths (controlnet, GFPGAN restore) as corpus examples; a cleaned reference should represent the canonical removal, and a removed method's output is not a reproducible example. Keep those experiment outputs in a local working dir, never in the committed corpus. +- SynthID reference corpus: `scripts/synthid_corpus.py` ingests labeled images into `data/synthid_corpus/`. The labeled `images/` (`pos/` `neg/` `cleaned/`) are **committed** (public repo -- review every image for private content before adding; `manifest.csv` is kept in sync with the files on disk, one row per tracked image); only the synthetic `refs/` calibration fills are gitignored. See its README for the collection protocol and verification oracles. **`cleaned/` examples must be produced by a CURRENT shipped removal method** -- the default SDXL img2img pass (optionally `--max-resolution`). Do NOT archive cleaned outputs from methods that are no longer in the pipeline (ctrlregen, the old text/face-protection, IP-Adapter FaceID, CodeFormer) or from the experimental opt-in paths (controlnet, face restore) as corpus examples; a cleaned reference should represent the canonical removal, and a removed method's output is not a reproducible example. Keep those experiment outputs in a local working dir, never in the committed corpus. ## Configuration - GPU/ML modules (invisible_engine, watermark_remover) are optional — guard imports with `is_available()` checks - Optional detection extras: `detect` (imwatermark — open SD/SDXL/FLUX watermark) and `trustmark` (Adobe TrustMark decoder; pulls torch + downloads weights). Both are guarded by `is_available()` and skipped by `identify` when absent. -- Optional `restore` extra (gfpgan/facexlib/basicsr): the GFPGAN face-identity post-pass (`face_restore.py`, CLI `--restore-faces` with `--restore-faces-method=gfpgan` (default), **EXPERIMENTAL, opt-in, OFF by default**). Guarded by `face_restore.is_available()`; when enabled it auto-skips with a debug log when the extra is absent or no face is detected. **Footgun for removal: re-introduces SynthID** (see `face_restore.py` bullet) -- removal-priority callers must use `--restore-faces-method=photomaker` instead. numpy<2-pinned and Python-3.12-pinned. -- Optional `photomaker` extra (`photomaker` upstream package + huggingface-hub): the SynthID-safe PhotoMaker-V2 face-identity post-pass (`photomaker_restore.py`, CLI `--restore-faces --restore-faces-method=photomaker`, **EXPERIMENTAL, opt-in, OFF by default**). Commercial-safe end-to-end (PhotoMaker-V2 Apache-2.0 + OpenCLIP-ViT-H/14 MIT; NO InsightFace -- the non-commercial blocker for IP-Adapter FaceID / InstantID / PuLID / Arc2Face). Carries identity in a SynthID-invariant OpenCLIP embedding (validated 2026-06-04: cosine drift 0.002 under SynthID-magnitude pixel noise, an order of magnitude less than JPEG90 drift which SynthID survives) and regenerates fresh face pixels conditioned on it. Heavy (~3 GB SDXL + ~1 GB PhotoMaker adapter, downloaded on first use). Kept OUT of `all`. The `photomaker` extra references the upstream git repo, which requires `[tool.hatch.metadata] allow-direct-references = true`. See `docs/synthid-robust-identity-research.md`. -- Optional `esrgan` extra (spandrel only): Real-ESRGAN pre-diffusion super-resolution for small inputs (`upscaler.py`, CLI `--upscaler esrgan` on `invisible`/`all`/`batch`). Guarded by `upscaler.is_available()`; the default upscaler stays Lanczos (cv2, no deps) and the engine falls back to Lanczos when the extra is absent or the model errors. spandrel is MIT and pulls NO basicsr (only torch/torchvision/safetensors/numpy/einops), sidestepping the `restore` extra's basicsr breakage; Real-ESRGAN weights are BSD-3-Clause and download on first use via `torch.hub` (never bundled). Kept OUT of `all` (heavy + model download), same as `restore`. +- Optional `photomaker` extra (`photomaker` upstream package + huggingface-hub): the SynthID-safe PhotoMaker-V2 face-identity post-pass (`photomaker_restore.py`, CLI `--restore-faces`, **EXPERIMENTAL, opt-in, OFF by default**). Commercial-safe end-to-end (PhotoMaker-V2 Apache-2.0 + OpenCLIP-ViT-H/14 MIT; NO InsightFace -- the non-commercial blocker for IP-Adapter FaceID / InstantID / PuLID / Arc2Face). Carries identity in a SynthID-invariant OpenCLIP embedding (validated 2026-06-04: cosine drift 0.002 under SynthID-magnitude pixel noise, an order of magnitude less than JPEG90 drift which SynthID survives) and regenerates fresh face pixels conditioned on it. Heavy (~3 GB SDXL + ~1 GB PhotoMaker adapter, downloaded on first use). Kept OUT of `all`. The `photomaker` extra references the upstream git repo, which requires `[tool.hatch.metadata] allow-direct-references = true`. See `docs/synthid-robust-identity-research.md`. **Replaces the removed `restore` (GFPGAN) extra**, which was oracle-confirmed 2026-06-04 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). That extra and its `face_restore.py` module are gone. +- Optional `esrgan` extra (spandrel only): Real-ESRGAN pre-diffusion super-resolution for small inputs (`upscaler.py`, CLI `--upscaler esrgan` on `invisible`/`all`/`batch`). Guarded by `upscaler.is_available()`; the default upscaler stays Lanczos (cv2, no deps) and the engine falls back to Lanczos when the extra is absent or the model errors. spandrel is MIT and pulls NO basicsr (only torch/torchvision/safetensors/numpy/einops); Real-ESRGAN weights are BSD-3-Clause and download on first use via `torch.hub` (never bundled). Kept OUT of `all` (heavy + model download). - Tests for the *model-running* paths are limited to availability checks (multi-GB downloads). But the **pure helpers inside ML-adjacent modules are unit-tested without any download** and must stay that way: `_target_size` (native-vs-downscale-cap-vs-upscale-floor, `test_invisible_engine.py`), `humanizer.unsharp_mask`/`adaptive_polish` (`test_humanizer.py`), `auto_config.plan`/detectors (`test_auto_config.py`), and the MPS->CPU fallback control flow via mocked pipelines (`test_img2img_runner.py`, 100% cover). Don't skip these as "ML, needs a model" — only `remove_watermark`/the diffusion bodies do. ## Key modules @@ -46,11 +45,11 @@ You are a **principal Python engineer** maintaining a CLI tool and library for r - `region_eraser.py` — universal region eraser (`erase` CLI). `erase(image, boxes=|mask=, backend=)` accepts grayscale (2D) and RGBA (4-channel) inputs on **both** backends (`erase_cv2` and `erase_lama` each split off any alpha plane and re-attach it unchanged, and promote grayscale to BGR for processing — LaMa would otherwise crash on grayscale and drop alpha on BGRA): `boxes_to_mask` → `cv2.inpaint` (`cv2` backend, default, no deps) or big-LaMa via onnxruntime (`lama` backend, extra `lama`, `Carve/LaMa-ONNX` Apache-2.0 model downloaded on first use, never bundled). `erase_lama` crops a padded region around the mask, runs LaMa at its fixed 512² input, pastes only masked pixels back (untouched areas stay pixel-exact). Lazy `_get_lama_session` singleton; `lama_available()` guards the optional import. **LaMa-ONNX costs ~3.5-4 GB peak RAM and ~5-6 s/call on CPU** (FFC working set, not arena — `enable_cpu_mem_arena=False` does not help), so it does NOT fit a minimal droplet; the cv2 backend (tens of MB, ~30 ms) does. LaMa quality at low RAM = serverless/GPU, mirroring how raiw.cc offloads SDXL to fal. - `invisible_watermark.py` — `detect_invisible_watermark(path)` decodes the OPEN DWT-DCT watermarks (public decoder, no key) embedded by Stable Diffusion / SDXL / FLUX via the `imwatermark` library. Known fixed patterns (verified against upstream source) live in `_BITS_48` (SDXL 48-bit, FLUX.2 48-bit) and `_SD1_STRING` ("StableDiffusionV1", SD 1.x/2.x). Optional dep (extra `detect`); returns None when absent. The `detect` extra pulls **torch** transitively (invisible-watermark declares torch a hard dep, and `WatermarkDecoder` eagerly imports `rivaGan` -> `torch` at import time), so detection needs torch present even though dwtDct runs CPU-only on cv2/numpy/pywavelets — no GPU and no separate `gpu` extra required. **Unlike SynthID this is locally detectable**, but the watermark is fragile (does not survive JPEG re-encode/resize — verified gone after JPEG q90), so it confirms origin only on pristine files. Add new known patterns here. The file carries a top-of-module pyright pragma because imwatermark/cv2 ship no type stubs. - `trustmark_detector.py` — `detect_trustmark(path)` decodes the OPEN, keyless **Adobe TrustMark** watermark (the soft binding behind Adobe Durable Content Credentials, `alg` `com.adobe.trustmark.P`) via the optional `trustmark` package (extra `trustmark`; pulls torch, downloads model weights on first use). Mirrors `invisible_watermark.py` (lazy singleton guarded by a double-checked `threading.Lock` so concurrent callers do not double-download the weights, top-of-module pyright pragma, returns None when absent). It detects *provenance*, not AI origin as such (TrustMark also marks human-authored content), so `identify` lists it as a watermark without setting `is_ai_generated`. Other soft-binding vendors (Digimarc/Imatag/Steg.AI/...) have no public decoder — they are only *named* via the `C2PA_SOFT_BINDINGS` scan, not decoded. **False-positive gate (added 2026-05-29):** TrustMark's `wm_present` is a BCH error-correction validity flag that spuriously validates on a content-correlated fraction of un-watermarked images — AI-generated textures trip it far more than camera photos (verified 2026-05-29 on real files: it fires on Gemini/OpenAI/Doubao output that *cannot* carry Adobe's watermark, with a random-bytes decoded secret, while signal-free camera photos did not trip it). A genuine TrustMark is a *durable* soft binding engineered to survive re-encoding, so `detect_trustmark` re-decodes after a mild JPEG round-trip (`_survives_reencode`, `_REENCODE_QUALITY` 95) and requires the same schema both times; every observed false positive collapsed (none survived even q95), so the gate is the durability property the watermark guarantees. The second decode runs only on the rare initial hit, so the cost is negligible. Do NOT remove the gate to "catch more" — a lone TrustMark hit without it is almost always content noise. -- `noai/watermark_remover.py` — the `WatermarkRemover` class has two diffusion pipelines, selected by the explicit `pipeline` ctor arg (NOT inferred from `model_id` -- both use the same SDXL base, `DEFAULT_MODEL_ID`). **`default`** runs plain SDXL img2img (`_run_img2img`). **`controlnet`** (**EXPERIMENTAL, opt-in**; `_run_controlnet`, `_load_controlnet_pipeline`) runs `StableDiffusionXLControlNetImg2ImgPipeline` with the SDXL-native canny ControlNet `xinsir/controlnet-canny-sdxl-1.0` (`watermark_profiles.CONTROLNET_CANNY_MODEL`): the control image is `cv2.Canny(gray, 100, 200)` stacked to 3 channels (`_CANNY_LOW`/`_CANNY_HIGH`, prompt `_CONTROLNET_PROMPT` / `_CONTROLNET_NEGATIVE`). **Removal comes from the img2img regeneration (`strength`); the ControlNet only PRESERVES text and face STRUCTURE via the edge map.** No original pixels are copied or frozen, BUT **validation 2026-06-04 disproved the old "so SynthID does not survive" claim: SynthID CAN survive controlnet on photoreal/high-detail content.** At the shared low removal strength the canny edge-conditioning keeps the regeneration so close to the original that the pixel perturbation that destroys SynthID does not happen (oracle-confirmed: an OpenAI bracelet photo + a 9-face grid read **SynthID-detected** after controlnet at strength 0.10/0.15, but **SynthID-not-detected** after the `default` pipeline at the SAME strength + resolution -- only the pipeline differed). **But the reverse also holds: a flat-graphic logo/poster SURVIVED `default` while clearing controlnet** -- removal at the low strength is content×pipeline dependent and neither pipeline is universally safe; the real lever is a higher strength. See the controlnet Known-limitations bullet for the full table + root cause. Canny holds face STRUCTURE but NOT identity (the regenerated face drifts in likeness -- canny carries edges, not identity; face identity is preserved by the optional `--restore-faces` GFPGAN post-pass (EXPERIMENTAL, opt-in, OFF by default) -- see `face_restore.py`). `controlnet_conditioning_scale` (ctor arg, default 1.0) is the structure-preservation knob. Same dtype rule as `default` (fp32 on cpu/mps, fp16 only on cuda/xpu; the fp16-fixed SDXL VAE `_SDXL_FP16_VAE_ID` is swapped in on fp16 GPUs -- issue #29) and the same MPS->CPU fallback (reload on cpu/fp32, drop a non-cpu generator, retry once). -- `face_restore.py` — optional GFPGAN face-restoration post-pass (cv2/torch/gfpgan boundary, top-of-file pyright pragma). **EXPERIMENTAL, opt-in, OFF by default.** Runs AFTER the diffusion removal pass (`InvisibleEngine.remove_watermark`, params `restore_faces=False` / `restore_faces_weight=0.5`; CLI `--restore-faces`/`--no-restore-faces` + `--restore-faces-weight` on `invisible`/`all`/`batch`). **WARNING -- this pass can RE-INTRODUCE SynthID into the face regions (oracle-confirmed 2026-06-04); the old "scrubs the watermark / oracle-validated clean" claim was WRONG.** Flow: GFPGANer.enhance runs on the **ORIGINAL (watermarked)** image -> identity faces + RetinaFace boxes (`restorer.face_helper.det_faces`); `_composite_faces` feather-composites those restored face REGIONS into the diffusion-cleaned image. At the default fidelity weight `0.5` GFPGAN BLENDS ~half the original face pixels with the StyleGAN2 prior (it is not a pure GAN re-synthesis), and **SynthID is robust to that partial blend**, so the composited face carries the watermark back IN -- overwriting what the diffusion pass removed. **Confirmed by a clean A/B:** an OpenAI/Gemini face image (`gemini_3`) read SynthID-DETECTED after controlnet @ strength 0.20/0.25 WITH restore, but SynthID-NOT-detected after the SAME controlnet @ 0.20 with `--no-restore-faces` (only restore differed). It is **content-dependent** (a second face image cleared WITH restore -- smaller faces / different blend), which is why the earlier single-image validation read "clean". **So `--restore-faces` as currently wired is a footgun for removal: it can re-add the watermark it is supposed to be scrubbing. Removal-priority callers (raiw.cc) must NOT use restore-on-original, or must switch to one of the fixes below.** **Fix directions (engineering follow-up, not yet done):** (a) run GFPGAN on the DIFFUSION-CLEANED image instead of the original, so the restored face is derived from already-clean pixels (loses some identity sharpness); (b) drop `--restore-faces-weight` well below 0.5 (more StyleGAN2 synthesis, less original -> less SynthID, but identity drifts); (c) leave restore OFF when removal is the priority. Each needs its own oracle re-validation. `is_available()` gates on gfpgan + facexlib; lazily-built `GFPGANer` singleton forces CPU unless CUDA (the pip GFPGANer has an MPS device-mismatch bug; it is a cheap post-pass on a few face crops). `_apply_basicsr_shim()` recreates the removed `torchvision.transforms.functional_tensor` module that basicsr imports. The pure `_composite_faces` helper (Gaussian-feathered rectangular alpha per box, `out = restored*a + base*(1-a)`) is unit-tested without the model (`tests/test_face_restore.py`); the model-running path is gated behind `is_available()`. **Commercial-safe** (GFPGAN Apache-2.0 + RetinaFace MIT); the CodeFormer alternative is NON-COMMERCIAL and is NOT shipped. The `restore` extra (gfpgan/facexlib/basicsr) is kept OUT of `all` (heavy + the GFPGANv1.4 + RetinaFace weights download on first use, never bundled). **`restore` pins numpy<2** (same trap class as the removed faceid/insightface extra): basicsr/gfpgan/facexlib are an old ecosystem, so the extra caps `scipy<1.18` (>=1.18 uses `np.long`, gone in numpy 1.24-1.26) and `numba<0.60` to keep the whole env on one numpy 1.26 resolution; verified the `--extra dev --extra gpu` gate env stays numpy 1.26.4 + `diffusers.loaders.peft` importable with `restore` present. **basicsr 1.4.2 builds only on Python <3.13** (its `setup.py get_version()` uses `exec(...)` + `locals()['__version__']`, which the 3.13 fast-locals change broke -> `KeyError: '__version__'`), so the project is pinned to Python 3.12 via `.python-version` and `[tool.uv.extra-build-dependencies] basicsr = ["setuptools<69"]`. basicsr ships sdist-only (no wheel). +- `noai/watermark_remover.py` — the `WatermarkRemover` class has two diffusion pipelines, selected by the explicit `pipeline` ctor arg (NOT inferred from `model_id` -- both use the same SDXL base, `DEFAULT_MODEL_ID`). **`default`** runs plain SDXL img2img (`_run_img2img`). **`controlnet`** (**EXPERIMENTAL, opt-in**; `_run_controlnet`, `_load_controlnet_pipeline`) runs `StableDiffusionXLControlNetImg2ImgPipeline` with the SDXL-native canny ControlNet `xinsir/controlnet-canny-sdxl-1.0` (`watermark_profiles.CONTROLNET_CANNY_MODEL`): the control image is `cv2.Canny(gray, 100, 200)` stacked to 3 channels (`_CANNY_LOW`/`_CANNY_HIGH`, prompt `_CONTROLNET_PROMPT` / `_CONTROLNET_NEGATIVE`). **Removal comes from the img2img regeneration (`strength`); the ControlNet only PRESERVES text and face STRUCTURE via the edge map.** No original pixels are copied or frozen, BUT **validation 2026-06-04 disproved the old "so SynthID does not survive" claim: SynthID CAN survive controlnet on photoreal/high-detail content.** At the shared low removal strength the canny edge-conditioning keeps the regeneration so close to the original that the pixel perturbation that destroys SynthID does not happen (oracle-confirmed: an OpenAI bracelet photo + a 9-face grid read **SynthID-detected** after controlnet at strength 0.10/0.15, but **SynthID-not-detected** after the `default` pipeline at the SAME strength + resolution -- only the pipeline differed). **But the reverse also holds: a flat-graphic logo/poster SURVIVED `default` while clearing controlnet** -- removal at the low strength is content×pipeline dependent and neither pipeline is universally safe; the real lever is a higher strength. See the controlnet Known-limitations bullet for the full table + root cause. Canny holds face STRUCTURE but NOT identity (the regenerated face drifts in likeness -- canny carries edges, not identity; face identity is preserved by the optional `--restore-faces` PhotoMaker-V2 post-pass (EXPERIMENTAL, opt-in, OFF by default; needs the `photomaker` extra) -- see `photomaker_restore.py`). `controlnet_conditioning_scale` (ctor arg, default 1.0) is the structure-preservation knob. Same dtype rule as `default` (fp32 on cpu/mps, fp16 only on cuda/xpu; the fp16-fixed SDXL VAE `_SDXL_FP16_VAE_ID` is swapped in on fp16 GPUs -- issue #29) and the same MPS->CPU fallback (reload on cpu/fp32, drop a non-cpu generator, retry once). +- `face_restore.py` (REMOVED 2026-06-04, was GFPGAN-based). The GFPGAN restore pass ran on the watermarked ORIGINAL at fidelity weight 0.5 and was oracle-confirmed to re-introduce SynthID into the face regions by partial pixel blending. **Replaced by `photomaker_restore.py`** (PhotoMaker-V2, identity-as-embedding) -- see that bullet below. - `photomaker_restore.py` — SynthID-safe face-identity restoration via PhotoMaker-V2 (commercial-safe alternative to `face_restore.py`'s GFPGAN footgun). **EXPERIMENTAL, opt-in via `--restore-faces --restore-faces-method=photomaker`, needs the `photomaker` extra.** Runs AFTER the diffusion removal pass (`InvisibleEngine.remove_watermark` -> `_restore_faces_photomaker`). Flow: YuNet detects faces in the CLEANED image; for each box, the SAME box from the ORIGINAL is square-cropped (`_face_crop_square`) and fed as `input_id_images` to `PhotoMakerStableDiffusionXLPipeline` (txt2img); the regenerated face is feather-composited back via `_composite_faces`. Identity comes from the OpenCLIP-ViT-H/14 embedding of the original face (SynthID-invariant: cosine 0.9977 on SynthID-magnitude noise, an order of magnitude less drift than JPEG90 which SynthID survives), but the PIXELS that land in the output are diffusion-fresh -- so SynthID is not transported back, unlike GFPGAN-on-original. **Commercial-safe end-to-end:** PhotoMaker-V2 Apache-2.0, OpenCLIP-ViT-H/14 MIT, SDXL shared with main pipeline, NO InsightFace. PhotoMaker is fundamentally txt2img in diffusers (`PhotoMakerStableDiffusionXLPipeline`); there is no `PhotoMakerControlNetImg2img` class, so this is a TWO-PASS pipeline: pass 1 (controlnet/default) cleans SynthID + drifts faces, pass 2 (this module) regenerates faces from the SynthID-invariant embedding. Pure helpers (`_face_crop_square`, `_composite_faces`) are unit-tested without the model (`tests/test_photomaker_restore.py`); the model-running path is gated behind `is_available()` and exercised manually via the Modal cert sweep. Lazy `PhotoMakerStableDiffusionXLPipeline` singleton (double-checked lock) downloads `photomaker-v2.bin` from `TencentARC/PhotoMaker-V2` on first use; never bundled. fp16 on CUDA, fp32 on MPS/CPU. See `docs/synthid-robust-identity-research.md` for the load-bearing embedding-invariance proof + license table. - `auto_config.py` — the `--auto` quality-mode planner (EXPERIMENTAL). `plan(image_path) -> AutoConfig | None` inspects the INPUT image (before the diffusion model loads) and picks the pipeline modes, so the run adapts to content. **Designed to run as the FIRST step of the invisible/all pipeline, wherever that runs** — locally or the raiw.cc Modal GPU worker — **never on the 512 MB web host** (image work there OOM-crashes the container; the planner is `_apply_auto` in `cli.py` for the CLI, and raiw-app would call `plan()` inside `RaiwProtect.remove`). **Quality-priority routing:** ControlNet (text/face-structure preservation) is the default; it is skipped for `default` (plain SDXL) only on a clearly structure-less image (`not has_face and not has_text and edge_density < _STRUCTURELESS_EDGE_MAX` 0.008). **CAVEAT (oracle-validated 2026-06-04, see the controlnet Known-limitations bullet): at the low vendor-adaptive strength NEITHER pipeline removes SynthID on all content -- it is content×pipeline dependent (photoreal SURVIVES controlnet / clears default; flat graphics SURVIVE default / clear controlnet; flat text clears both). So `--auto` picking controlnet for faces/photos leaves SynthID on exactly those, and plain `default` would leave it on flat graphics -- pipeline choice alone does NOT guarantee removal. The real lever is a HIGHER strength, oracle-validated per content type. Removal-priority callers (raiw.cc) must oracle-validate strength across content types BEFORE adopting auto; the "must keep SynthID removed" gate in the adoption note below is the blocker this caught.** `restore_faces` is on when a face is present. When a smoothing pass (controlnet/restore) ran, the **adaptive polish** (`humanizer.adaptive_polish`) is applied: it targets the input's Laplacian variance (detail level) with a capped unsharp + edge-masked grain, restoring photo/face texture while **sparing text** (text is already high-frequency, so the deficit is tiny and almost no polish lands -- the old fixed unsharp/grain speckled small text; validated 2026-06-03 on gemini_3 lap-var 84->334 toward the 592 original, openai_1 text near-untouched). **Detection is cv2-only and torch-free** (~100 MB peak RSS, a few ms — measured): OpenCV **YuNet** (`cv2.FaceDetectorYN`, MIT, 232 KB model bundled at `assets/face_detection_yunet_2023mar.onnx`) for faces, **DBNet** (PP-OCRv3 differentiable-binarization via `cv2.dnn.TextDetectionModel_DB`, a 2.4 MB Apache-2.0 model bundled at `assets/text_detection_ppocrv3_2023may.onnx`) for text, with the old Canny+MSER region heuristic kept as a fallback if the DBNet model can't load (`_detect_text_dbnet` returns None → `_detect_text_mser`). The en/cn opencv_zoo PP-OCRv3 detection models are byte-identical, so it is bundled language-neutral. Text only ever ADDS controlnet, so a miss is backstopped by edge-density and a false positive only costs a controlnet run. Plus `edge_density`. `min_resolution` stays 1024. **Every auto decision is independently overridable** (interface principle): `_apply_auto` (cli.py) overrides only the three content-adaptive modes the user left at their click default (`ctx.get_parameter_source(...) == DEFAULT`) — `--pipeline`, `--restore-faces`/`--no-restore-faces`, and **`--adaptive-polish`/`--no-adaptive-polish`** always win; `--min-resolution`/`--strength`/`--unsharp`/`--humanize` are independent knobs. `--adaptive-polish` also works WITHOUT `--auto` (manual detail-targeted polish; the engine's `adaptive_polish` param uses the full-res original as the detail reference). Prints the chosen plan (`AutoConfig.reason`). Wired into `cmd_all`/`cmd_invisible`/`cmd_batch` — in `batch` the plan is recomputed per image and the invisible engine is cached **per resolved pipeline** (`ctx.obj["_inv_engines"]`, keyed `default`/`controlnet`) instead of a single shared instance, so a mixed directory builds at most one engine of each kind. **Adds ZERO new pip deps** (all cv2 core + the bundled MIT YuNet + Apache-2.0 DBNet models + the cv2-only adaptive polish). The auto plan does NOT select the `esrgan` upscaler (that needs the optional extra and would make auto's behavior install-dependent); `--upscaler esrgan` stays a separate manual knob. Unit-tested without a heavy download (`tests/test_auto_config.py`): flat/text synthetic images for routing (the bundled DBNet fires on a real text card), monkeypatched `detect_face`/`_detect_text_dbnet`/`_detect_text_mser` for the face/text/fallback branches (a real detectable-face fixture is private, never committed). Production adoption path for raiw.cc: validate (must keep SynthID removed, not hallucinate micro-text, beat plain SDXL on the real upload distribution), then bump the library SHA in `modal_app.py` and pass `auto=True`. -- `upscaler.py` — optional Real-ESRGAN pre-diffusion super-resolution for small inputs (spandrel boundary, top-of-file pyright pragma). `is_available()` gates on spandrel+torch (via `importlib.util.find_spec`); `upscale(bgr, device=None)` loads a lazily-built spandrel `ImageModelDescriptor` singleton (double-checked lock) and upscales by the model's native factor (x2), with a non-CPU→CPU device fallback mirroring the diffusion engine's MPS→CPU retry. Weights (`RealESRGAN_x2plus.pth`, BSD-3-Clause) download on first use to the `torch.hub` checkpoints cache; never bundled. Used only when UPscaling to the `min_resolution` floor (a `max_resolution` downscale always uses Lanczos). The wiring is `InvisibleEngine._esrgan_upscale(pil, target)` — Real-ESRGAN at native factor, then a Lanczos resize to the exact target, falling back to a plain Lanczos resize if the extra is absent or the model errors (so an optional upscaler can never break removal). The default `--upscaler` is `lanczos` (cv2, no deps). **ESRGAN is a generic photo/texture GAN with no face/glyph prior**, so it best fits photo/texture content and can degrade faces (glassy/asymmetric eyes -- the diffusion pass regenerates faces so the full-pipeline final recovers; that is what GFPGAN/`--restore-faces` is for) and thin/small text (the GAN invents wrong strokes, and low-strength diffusion will not fix it). Verified 2026-06-04: isolated upscale lap-var ~5x Lanczos on faces+textures but glassy eyes; end-to-end `invisible` final lap-var 1634 vs Lanczos 663 with natural faces (diffusion cleaned the artifact). Kept a **manual opt-in knob** (the auto plan never selects it) with `lanczos` the default; not content-gated by design (use Lanczos for text-heavy inputs). spandrel is MIT and pulls no basicsr, unlike the `restore` extra. Unit-tested without the model: `tests/test_upscaler.py` (availability guard + the not-installed RuntimeError) and `tests/test_invisible_engine.py::TestEsrganUpscale` (the three `_esrgan_upscale` branches via a monkeypatched `upscaler`). +- `upscaler.py` — optional Real-ESRGAN pre-diffusion super-resolution for small inputs (spandrel boundary, top-of-file pyright pragma). `is_available()` gates on spandrel+torch (via `importlib.util.find_spec`); `upscale(bgr, device=None)` loads a lazily-built spandrel `ImageModelDescriptor` singleton (double-checked lock) and upscales by the model's native factor (x2), with a non-CPU→CPU device fallback mirroring the diffusion engine's MPS→CPU retry. Weights (`RealESRGAN_x2plus.pth`, BSD-3-Clause) download on first use to the `torch.hub` checkpoints cache; never bundled. Used only when UPscaling to the `min_resolution` floor (a `max_resolution` downscale always uses Lanczos). The wiring is `InvisibleEngine._esrgan_upscale(pil, target)` — Real-ESRGAN at native factor, then a Lanczos resize to the exact target, falling back to a plain Lanczos resize if the extra is absent or the model errors (so an optional upscaler can never break removal). The default `--upscaler` is `lanczos` (cv2, no deps). **ESRGAN is a generic photo/texture GAN with no face/glyph prior**, so it best fits photo/texture content and can degrade faces (glassy/asymmetric eyes -- the diffusion pass regenerates faces so the full-pipeline final recovers; PhotoMaker `--restore-faces` is the identity-recovery path) and thin/small text (the GAN invents wrong strokes, and low-strength diffusion will not fix it). Verified 2026-06-04: isolated upscale lap-var ~5x Lanczos on faces+textures but glassy eyes; end-to-end `invisible` final lap-var 1634 vs Lanczos 663 with natural faces (diffusion cleaned the artifact). Kept a **manual opt-in knob** (the auto plan never selects it) with `lanczos` the default; not content-gated by design (use Lanczos for text-heavy inputs). spandrel is MIT and pulls no basicsr. Unit-tested without the model: `tests/test_upscaler.py` (availability guard + the not-installed RuntimeError) and `tests/test_invisible_engine.py::TestEsrganUpscale` (the three `_esrgan_upscale` branches via a monkeypatched `upscaler`). - `image_io.py` — Unicode-safe cv2 IO (issue #17). `imread(path, flags=None)` / `imwrite(path, img)` wrap `np.fromfile`+`cv2.imdecode` / `cv2.imencode`+`tofile` so non-ASCII paths work on Windows -- bare `cv2.imread`/`cv2.imwrite` use the platform ANSI code-page API there and fail (empty decode + `can't open/read file`) on Chinese/Cyrillic/accented filenames. `imread` keeps `cv2.imread` semantics (defaults to `IMREAD_COLOR`, returns `None` on missing/empty/undecodable). **Every cv2 file read/write in the package routes through here; do not call `cv2.imread`/`cv2.imwrite` directly.** `imwrite` returns `False` on an unwritable path (`OSError` caught) instead of raising, matching `cv2.imwrite` semantics. macOS/Linux already accept UTF-8 paths, so it is behavior-neutral there (the bug only reproduces on Windows). cv2/numpy are imported lazily inside the functions, so the module is cheap to import in a bare env. ### Doubao clean-reverse-alpha distillation (re-investigated 2026-05-29) @@ -82,7 +81,7 @@ Who embeds what, and whether it is locally detectable (so we know which gaps are ## Known limitations -- `invisible` pipeline processes at **native resolution for inputs whose long side is >= 1024px**, and **auto-upscales smaller inputs UP to a 1024px floor** (`min_resolution=1024`, the default; `--min-resolution 0` disables) before diffusion -- SDXL img2img distorts badly on a tiny latent (a 381x512 portrait wrecks at native, the #36 follow-up), and the output is restored to the original input size so the floor is a transparent quality boost (it adds time/memory on small inputs). The floor upscale uses Lanczos by default; **`--upscaler esrgan`** (opt-in, the `esrgan` extra) runs Real-ESRGAN first for better detail before the Lanczos resize to the exact target (`upscaler.py` / `InvisibleEngine._esrgan_upscale`, falls back to Lanczos if the extra is absent). `max_resolution=0` (default) means no downscale cap, matching the hosted raiw.cc backend (fal fast-sdxl, no pre-downscale). The old forced downscale-to-1024 -> upscale-back round-trip for LARGE images was the main quality loss (issue #10) and is gone; at strength ~0.05 SDXL img2img does not need a downscale. **Final `--unsharp` post-filter (`humanizer.unsharp_mask`, opt-in, default 0):** applied LAST (after the GFPGAN face pass, else it would be smoothed over) to counter the soft/over-smoothed look diffusion + restoration leave (an AI tell); ~0.5-0.8 safe, higher risks halos. Pairs with `--humanize` (grain adds sensor-noise texture, unsharp adds crispness). `--max-resolution N` re-introduces an opt-in long-side cap purely to bound GPU/MPS memory on very large inputs (it reintroduces the lossy round-trip). For huge images that OOM at native, tile-based diffusion is still the proper long-term fix. **Concrete MPS data points (the OOM is memory-tier-dependent, NOT a hard MPS limit):** on a ~24 GB unified-memory machine (verified 2026-05-25, 1254x1254 gpt-image SDXL, fp32) native res OOMs at the *UNet* step (peak ~17 GiB), not only the VAE decode, and the auto-fallback in `img2img_runner` reloads on CPU and finishes (slow, ~13 min) -- the output is still weight-identical and defeats SynthID, so "looks hung/crashed" on Mac is usually this CPU fallback, not a pipeline error. On a **32 GB** unified-memory machine the same default SDXL pass runs entirely on MPS with **no CPU fallback** (verified 2026-05-31, 1122x1402 gpt-image, `all`/default, ~155 s end-to-end), so 32 GB clears the native-res UNet peak that 24 GB could not. Adding `enable_vae_tiling()` alone does NOT prevent the 24 GB OOM (the peak is the UNet, not the VAE). The fast Mac workarounds for memory-constrained machines are fp16 on MPS (roughly halves memory) or `--max-resolution` to cap the long side; neither is wired as the default. The `controlnet` pipeline adds the canny ControlNet weights on top of SDXL, so its peak is a bit higher than the plain `default` pass; the same MPS->CPU fallback covers an OOM. The native-vs-cap-vs-floor decision lives in the pure helper `invisible_engine._target_size(w, h, max_resolution, min_resolution)` (returns `None` for native, a target tuple for a downscale cap OR an upscale floor; cap takes precedence, the floor is skipped on a min>max misconfig) so it is unit-tested (`tests/test_invisible_engine.py::TestTargetSize`, the #10/#15/#36 regression guard) without loading the model -- keep that logic in the helper, don't re-inline it. +- `invisible` pipeline processes at **native resolution for inputs whose long side is >= 1024px**, and **auto-upscales smaller inputs UP to a 1024px floor** (`min_resolution=1024`, the default; `--min-resolution 0` disables) before diffusion -- SDXL img2img distorts badly on a tiny latent (a 381x512 portrait wrecks at native, the #36 follow-up), and the output is restored to the original input size so the floor is a transparent quality boost (it adds time/memory on small inputs). The floor upscale uses Lanczos by default; **`--upscaler esrgan`** (opt-in, the `esrgan` extra) runs Real-ESRGAN first for better detail before the Lanczos resize to the exact target (`upscaler.py` / `InvisibleEngine._esrgan_upscale`, falls back to Lanczos if the extra is absent). `max_resolution=0` (default) means no downscale cap, matching the hosted raiw.cc backend (fal fast-sdxl, no pre-downscale). The old forced downscale-to-1024 -> upscale-back round-trip for LARGE images was the main quality loss (issue #10) and is gone; at strength ~0.05 SDXL img2img does not need a downscale. **Final `--unsharp` post-filter (`humanizer.unsharp_mask`, opt-in, default 0):** applied LAST (after the face-restore pass, else it would be smoothed over) to counter the soft/over-smoothed look diffusion + restoration leave (an AI tell); ~0.5-0.8 safe, higher risks halos. Pairs with `--humanize` (grain adds sensor-noise texture, unsharp adds crispness). `--max-resolution N` re-introduces an opt-in long-side cap purely to bound GPU/MPS memory on very large inputs (it reintroduces the lossy round-trip). For huge images that OOM at native, tile-based diffusion is still the proper long-term fix. **Concrete MPS data points (the OOM is memory-tier-dependent, NOT a hard MPS limit):** on a ~24 GB unified-memory machine (verified 2026-05-25, 1254x1254 gpt-image SDXL, fp32) native res OOMs at the *UNet* step (peak ~17 GiB), not only the VAE decode, and the auto-fallback in `img2img_runner` reloads on CPU and finishes (slow, ~13 min) -- the output is still weight-identical and defeats SynthID, so "looks hung/crashed" on Mac is usually this CPU fallback, not a pipeline error. On a **32 GB** unified-memory machine the same default SDXL pass runs entirely on MPS with **no CPU fallback** (verified 2026-05-31, 1122x1402 gpt-image, `all`/default, ~155 s end-to-end), so 32 GB clears the native-res UNet peak that 24 GB could not. Adding `enable_vae_tiling()` alone does NOT prevent the 24 GB OOM (the peak is the UNet, not the VAE). The fast Mac workarounds for memory-constrained machines are fp16 on MPS (roughly halves memory) or `--max-resolution` to cap the long side; neither is wired as the default. The `controlnet` pipeline adds the canny ControlNet weights on top of SDXL, so its peak is a bit higher than the plain `default` pass; the same MPS->CPU fallback covers an OOM. The native-vs-cap-vs-floor decision lives in the pure helper `invisible_engine._target_size(w, h, max_resolution, min_resolution)` (returns `None` for native, a target tuple for a downscale cap OR an upscale floor; cap takes precedence, the floor is skipped on a min>max misconfig) so it is unit-tested (`tests/test_invisible_engine.py::TestTargetSize`, the #10/#15/#36 regression guard) without loading the model -- keep that logic in the helper, don't re-inline it. - **fp16 VAE black-output fix (issue #29, 2026-05-30):** on a **CUDA/XPU fp16** backend the stock SDXL VAE overflows to NaN and the *plain* img2img path decodes to an **all-black** image (reproduced on the raiw.cc result: a 1086x1448 input -> a uniformly black 4.6 KB PNG, mean 0). `watermark_remover._load_pipeline` / `_load_controlnet_pipeline` swap in the fp16-fixed SDXL VAE (`madebyollin/sdxl-vae-fp16-fix` = `_SDXL_FP16_VAE_ID`) when `_needs_fp16_vae_fix(model_id, DEFAULT_MODEL_ID, is_fp16)` is true -- only the default SDXL checkpoint on fp16. **cpu/mps run fp32** (the stock VAE is fine there, which is why the bug never reproduces on Mac). A custom non-SDXL `model_id` keeps its own VAE (the fp16-fix VAE is SDXL-architecture-specific). The decision is a pure helper, unit-tested without a download (`tests/test_platform.py::TestFp16VaeFix`); the actual black->clean recovery needs a CUDA GPU. **Confirmed on real CUDA hardware 2026-06-03:** running `all` on a 1086x1448 OpenAI gpt-image (the #29 repro size) at fp16 produced a normal (non-black) output, so the fp16-fix VAE swap resolves the all-black decode. (It was not reproducible on this MPS machine, which runs fp32, so the verification had to happen on an NVIDIA box.) **Follow-up safety net (issue #41, 2026-06-04):** the swap is gated to `model_id == DEFAULT_MODEL_ID`, so a custom model, a stale pre-fix install, or a fal/custom loader can still hit the black decode -- a new reporter did (gpt-image 1448x1086, the #29 size, with the exact `image_processor.py:142 invalid value encountered in cast` warning the NaN->0 cast emits). `remove_watermark` now adds a model-agnostic backstop: after generation, if the run was fp16 AND the output is degenerate (`_is_degenerate_image`: mean and std both below `_DEGENERATE_THRESHOLD` 1.0 -- a uniform all-black/NaN frame; the variance guard spares a legitimately dark-but-textured photo), it rebuilds the pipeline in fp32 on the SAME device and re-runs once. fp32 is the verified-clean path, so the user never gets a black image regardless of model_id/version. Mirrors the existing MPS->CPU fallback's self-mutation pattern (reset `torch_dtype` + clear `_pipeline`/`_controlnet_pipeline`); `batch` inherits it through `remove_watermark`, and once one image trips it the rest of the batch stays on the safe fp32. The detector is a pure helper, unit-tested without a model (`tests/test_platform.py::TestDegenerateOutputGuard`); the full fp16->detect->fp32-retry chain was verified e2e on this MPS machine by forcing fp16 with the swap disabled (first pass black, guard fired, retry produced a normal image). CAVEAT: the fp32 retry uses ~2x memory, so on a VRAM-constrained GPU it can OOM (a visible error, still better than a silent black frame; the MPS->CPU fallback covers that path). The reporter's "CPU also black" symptom is NOT reproducible here -- fp32 (cpu/mps) decodes clean -- so it points at an old version or a non-fp32 run, pending their version + command. - Pyright first run is slow (2-3 min) due to ML deps (torch/diffusers/transformers stubs); full-project `uv run pyright` can stall for many minutes — scope it to changed files. - A third-party PIL plugin autoload (e.g. an HEIF/AVIF plugin) can raise a non-OSError (`ModuleNotFoundError`), not `UnidentifiedImageError`, when opening a file. Code that opens user-supplied or unknown-format files should `except Exception`, not just `OSError`/`UnidentifiedImageError`. @@ -94,4 +93,4 @@ Who embeds what, and whether it is locally detectable (so we know which gaps are - **External AI-vs-real classifier models are out of scope (decided 2026-05-24).** Generic HuggingFace detectors (`Organika/sdxl-detector` Swin Transformer, `umm-maybe/AI-image-detector`, and fine-tunes) exist and report ~0.98 on their *own* SDXL-vs-real validation sets, but they are per-generator and the model cards themselves note degraded accuracy off-distribution; they are untested on gpt-image / Gemini Nano Banana (the metadata-stripped surfaces we care about), and our own light SDXL pass would likely defeat them the same way it defeats SynthID. Detection here stays local + signal-based (metadata + visible sparkle); do not add a bundled classifier dependency. - **DEFAULT STRENGTH IS NOW VENDOR-ADAPTIVE (2026-06-01, SUPERSEDES every fixed-default claim in this bullet and the next).** `resolve_strength(strength, profile, vendor)` + `vendor_for_strength(path)` (`watermark_profiles.py`) read the C2PA issuer (`metadata.synthid_source`) on the ORIGINAL input and pick `OPENAI_STRENGTH` **0.10** / `GEMINI_STRENGTH` **0.15** / `UNKNOWN_STRENGTH` **0.15** when `--strength` is unset; explicit `--strength` always wins. The CLI detects the vendor from the pristine source (before the visible pass / metadata-strip removes C2PA from the temp file) and passes it to the engine, so display and execution agree; `cmd_invisible`/`cmd_all`/`batch` + the module-level `remove_watermark` all thread `vendor`. **This replaces the single 0.30 default AND the prior "do NOT build a vendor-adaptive default" policy** -- both came from the now-debunked region-rescrub-contaminated study (the per-region re-scrub that contaminated those numbers was removed in the controlnet refactor). Basis: the oracle-verified June 2026 controlled study (clean v0.8.6, protect OFF): OpenAI clears at 0.05 across 1024-1600 (n=4, resolution-independent); Google needs 0.15 on the capped-1536 path (n=4). `docs/synthid.md` §2.2 (data) + §5.2 (the adaptive default) are authoritative. **CAVEAT (oracle pass 2026-06-04): the OpenAI 0.10 default is content-dependent, NOT universal -- a flat-graphic OpenAI logo/poster still read SynthID-detected after `default` at 0.10, and photoreal images after controlnet at 0.10/0.15 (low-change regions under-perturbed). Removal at 0.10/0.15 is content×pipeline dependent (see the controlnet Known-limitations bullet); the lever is a higher strength, oracle-revalidated per content type. Do NOT assume the vendor-adaptive default clears every image.** CAVEAT: Google's 0.15 was validated only on `--max-resolution 1536`; native large Gemini (2816) was not locally measurable (OOM on M-series) and is pending GPU validation on raiw.cc -- if it survives 0.15 native, raise `--strength`. **Everything below in this bullet about a fixed 0.10/0.30 default is HISTORICAL; trust the vendor-adaptive constants + docs/synthid.md.** - **SynthID removal: strength + oracle scope.** Default strength is vendor-adaptive (see the bullet above); `docs/synthid.md` §2.2 is authoritative for the numbers. **Oracle scope (load-bearing):** the Gemini app "Verify with SynthID" is the ONLY valid SynthID oracle (detects Google's mark on any image); `openai.com/verify` is scoped to OpenAI provenance (its own C2PA), NOT a SynthID oracle -- a negative there is meaningless for SynthID. There is no local SynthID detector, so the tool cannot self-check; if the oracle still reads SynthID, raise `--strength` to the lowest value that verifies clean. Only the `default` (plain SDXL img2img) and `controlnet` (SDXL + canny ControlNet) profiles exist; the local `invisible` default is weight-for-weight identical to raiw.cc prod (`fal-ai/fast-sdxl` = `stabilityai/stable-diffusion-xl-base-1.0`, runtime-downloaded, not bundled). **Forensic-stealth caveat** (arXiv:2605.09203): defeating the SynthID verifier is NOT forensic invisibility -- independent detectors flag *removal-processed* images vs genuinely-clean ones at >98% TPR@1%FPR, so do not over-claim "indistinguishable from a real photo". -- **`controlnet` pipeline (text/face STRUCTURE preservation, EXPERIMENTAL, opt-in `--pipeline controlnet`).** SDXL + the canny ControlNet `xinsir/controlnet-canny-sdxl-1.0` via `StableDiffusionXLControlNetImg2ImgPipeline` (`watermark_remover._run_controlnet` / `_load_controlnet_pipeline`). **Removal still comes from the img2img regeneration (`strength`); the ControlNet only PRESERVES text and face STRUCTURE by conditioning on the canny edge map** (`cv2.Canny(gray, 100, 200)`, 3-channel). Canny preserves edges, NOT face identity (a regenerated face drifts in likeness); face identity is preserved by the optional `--restore-faces` GFPGAN post-pass (EXPERIMENTAL, opt-in, OFF by default -- see `face_restore.py`, the `restore` extra) -- **but WARNING: that pass can RE-INTRODUCE SynthID into the face regions (oracle-confirmed 2026-06-04, since GFPGAN runs on the ORIGINAL watermarked face and blends ~half its pixels at weight 0.5), so it is a footgun for removal; see the `face_restore.py` bullet.** The CodeFormer alternative stays NON-COMMERCIAL and is not shipped. The earlier `--face-id` IP-Adapter FaceID layer was REMOVED (footgun: it needs high strength and corrupts faces at the low removal strength). No original pixels are copied or frozen, **BUT removal at the low vendor-adaptive strength is CONTENT × PIPELINE dependent and NEITHER pipeline clears all content -- oracle-validated against the OpenAI verifier 2026-06-04 (8 images, strength 0.10/0.15, `--max-resolution 1536`).** The survivors FLIP by content type: **photoreal** (a 9-face grid, a bracelet product photo) SURVIVES controlnet but CLEARS `default` (controlnet's dense edge map keeps the regen too close to the original, so the SynthID-destroying perturbation never happens; plain img2img perturbs photoreal texture enough); **flat graphic** (a logo/poster with large flat color fills) SURVIVES `default` but CLEARS controlnet (at low strength img2img barely changes flat fills so SynthID persists there, while controlnet repaints them more freely); a flat **text** card cleared under both. **Root cause is insufficient STRENGTH, not the pipeline: at 0.10 the low-change regions -- dense-edge photoreal under controlnet, large flat fills under `default` -- are not perturbed enough to destroy SynthID. The vendor-adaptive 0.10 from the June study is NOT universally sufficient (that study's content happened to clear at 0.10).** The robust fix is a HIGHER strength, oracle-revalidated per content type (controlnet can be cranked harder without losing structure; a lower `controlnet_conditioning_scale` also frees the regen on photoreal). So at today's default strength **both pipelines AND `--auto` can LEAVE SynthID on some content** -- a removal-priority caller (raiw.cc) MUST oracle-validate strength across content types before adopting, not pick a pipeline and assume removal. **Follow-up same day: re-running the two photoreal survivors through controlnet at an explicit `--strength 0.15` cleared BOTH on the oracle -- BUT one of them (the bracelet) had SURVIVED the SAME 0.15 controlnet config in the first pass (only the random, unset seed differed). So removal near the threshold is SEED-NON-DETERMINISTIC: the same image+pipeline+strength+resolution can pass or fail run-to-run (img2img uses `seed=None`/random unless `--seed` is passed, and there is no local SynthID detector to self-verify). 0.15 is the borderline, NOT a robust floor -- pick a strength with MARGIN (controlnet ~>= 0.20) rather than exactly on it; the content×pipeline table's 0.15 data point is near-threshold noise. A confirming run at `--strength 0.20` controlnet cleared BOTH photoreal survivors on the oracle (ladder: 0.10 grid detected → 0.15 borderline/non-deterministic → 0.20 both clean), so **0.20 is the recommended robust controlnet floor for OpenAI photoreal** (one margin run, not an N-run repeatability proof -- a service should add margin or verify repeatability since there is no local SynthID detector to self-check). **Engineering follow-up for raiw.cc: the controlnet pipeline should use a HIGHER vendor strength than `default` -- it currently shares `resolve_strength` (0.10/0.15, tuned for plain img2img), but controlnet's edge map preserves structure so it needs ~0.20+; calibrate per vendor/content on the GPU worker, do NOT just reuse the `default` ladder.** **CERTIFIED 2026-06-04 via the isolated `raiw-controlnet-cert` Modal app (`raiw-app/modal_cert.py`), restore OFF, ≤1536, each vendor on its own oracle: controlnet floors are OpenAI 0.20 (2 photoreal × 3 seeds = 6/6 clean; the 0.15-flipper is seed-robust at 0.20) and Gemini 0.30 (0.20 detected → 0.30 clean on 2/2 seeds). OpenAI 0.20 transfers to prod (resolution-independent); Gemini 0.30 holds only ≤1536 — Gemini is resolution-sensitive and raiw.cc runs NATIVE (`max_resolution=0`), so cap Gemini ≤1536 + use 0.30, or native-calibrate (~0.35+). Prod recipe: controlnet + per-vendor floor in `resolve_strength` (not the default ladder) + FIXED seed (kills the non-determinism) + restore reworked/off.** See `docs/synthid.md` §5.5 + `docs/controlnet-removal-pipeline-research.md` (certified floors table).** **Lesson: visual-quality + face-recovery validation does NOT prove watermark removal -- only the SynthID oracle does, across MULTIPLE content types; never infer removal from sharpness/identity, and never conclude from a partial result (the photoreal-only data first read as "controlnet shields, default removes" -- the flat-graphic result reversed it).** `controlnet_conditioning_scale` (CLI `--controlnet-scale`, default 1.0) is the structure-preservation knob (higher = closer to the original structure); fp32 on cpu/mps, fp16-fixed VAE on cuda/xpu. The `controlnet` profile is threaded explicitly (`WatermarkRemover(pipeline=...)` / `InvisibleEngine(pipeline=...)`), NOT inferred from `model_id`. This productionizes the `scripts/controlnet_sweep.py` prototype; see `docs/controlnet-removal-pipeline-research.md`. **Forensic-stealth caveat still applies** (arXiv:2605.09203): defeating the SynthID verifier is not forensic invisibility -- a "this image went through a removal pipeline" classifier can still flag the output. +- **`controlnet` pipeline (text/face STRUCTURE preservation, EXPERIMENTAL, opt-in `--pipeline controlnet`).** SDXL + the canny ControlNet `xinsir/controlnet-canny-sdxl-1.0` via `StableDiffusionXLControlNetImg2ImgPipeline` (`watermark_remover._run_controlnet` / `_load_controlnet_pipeline`). **Removal still comes from the img2img regeneration (`strength`); the ControlNet only PRESERVES text and face STRUCTURE by conditioning on the canny edge map** (`cv2.Canny(gray, 100, 200)`, 3-channel). Canny preserves edges, NOT face identity (a regenerated face drifts in likeness); face identity is preserved by the optional `--restore-faces` PhotoMaker-V2 post-pass (EXPERIMENTAL, opt-in, OFF by default -- see `photomaker_restore.py`, the `photomaker` extra). PhotoMaker carries identity in a SynthID-invariant OpenCLIP embedding and regenerates fresh face pixels conditioned on it; the GFPGAN-based `face_restore.py` was REMOVED 2026-06-04 because it ran on the watermarked original and re-introduced SynthID. The CodeFormer alternative stays NON-COMMERCIAL and is not shipped. The earlier `--face-id` IP-Adapter FaceID layer was REMOVED (footgun: it needs high strength and corrupts faces at the low removal strength). No original pixels are copied or frozen, **BUT removal at the low vendor-adaptive strength is CONTENT × PIPELINE dependent and NEITHER pipeline clears all content -- oracle-validated against the OpenAI verifier 2026-06-04 (8 images, strength 0.10/0.15, `--max-resolution 1536`).** The survivors FLIP by content type: **photoreal** (a 9-face grid, a bracelet product photo) SURVIVES controlnet but CLEARS `default` (controlnet's dense edge map keeps the regen too close to the original, so the SynthID-destroying perturbation never happens; plain img2img perturbs photoreal texture enough); **flat graphic** (a logo/poster with large flat color fills) SURVIVES `default` but CLEARS controlnet (at low strength img2img barely changes flat fills so SynthID persists there, while controlnet repaints them more freely); a flat **text** card cleared under both. **Root cause is insufficient STRENGTH, not the pipeline: at 0.10 the low-change regions -- dense-edge photoreal under controlnet, large flat fills under `default` -- are not perturbed enough to destroy SynthID. The vendor-adaptive 0.10 from the June study is NOT universally sufficient (that study's content happened to clear at 0.10).** The robust fix is a HIGHER strength, oracle-revalidated per content type (controlnet can be cranked harder without losing structure; a lower `controlnet_conditioning_scale` also frees the regen on photoreal). So at today's default strength **both pipelines AND `--auto` can LEAVE SynthID on some content** -- a removal-priority caller (raiw.cc) MUST oracle-validate strength across content types before adopting, not pick a pipeline and assume removal. **Follow-up same day: re-running the two photoreal survivors through controlnet at an explicit `--strength 0.15` cleared BOTH on the oracle -- BUT one of them (the bracelet) had SURVIVED the SAME 0.15 controlnet config in the first pass (only the random, unset seed differed). So removal near the threshold is SEED-NON-DETERMINISTIC: the same image+pipeline+strength+resolution can pass or fail run-to-run (img2img uses `seed=None`/random unless `--seed` is passed, and there is no local SynthID detector to self-verify). 0.15 is the borderline, NOT a robust floor -- pick a strength with MARGIN (controlnet ~>= 0.20) rather than exactly on it; the content×pipeline table's 0.15 data point is near-threshold noise. A confirming run at `--strength 0.20` controlnet cleared BOTH photoreal survivors on the oracle (ladder: 0.10 grid detected → 0.15 borderline/non-deterministic → 0.20 both clean), so **0.20 is the recommended robust controlnet floor for OpenAI photoreal** (one margin run, not an N-run repeatability proof -- a service should add margin or verify repeatability since there is no local SynthID detector to self-check). **Engineering follow-up for raiw.cc: the controlnet pipeline should use a HIGHER vendor strength than `default` -- it currently shares `resolve_strength` (0.10/0.15, tuned for plain img2img), but controlnet's edge map preserves structure so it needs ~0.20+; calibrate per vendor/content on the GPU worker, do NOT just reuse the `default` ladder.** **CERTIFIED 2026-06-04 via the isolated `raiw-controlnet-cert` Modal app (`raiw-app/modal_cert.py`), restore OFF, ≤1536, each vendor on its own oracle: controlnet floors are OpenAI 0.20 (2 photoreal × 3 seeds = 6/6 clean; the 0.15-flipper is seed-robust at 0.20) and Gemini 0.30 (0.20 detected → 0.30 clean on 2/2 seeds). OpenAI 0.20 transfers to prod (resolution-independent); Gemini 0.30 holds only ≤1536 — Gemini is resolution-sensitive and raiw.cc runs NATIVE (`max_resolution=0`), so cap Gemini ≤1536 + use 0.30, or native-calibrate (~0.35+). Prod recipe: controlnet + per-vendor floor in `resolve_strength` (not the default ladder) + FIXED seed (kills the non-determinism) + PhotoMaker restore (the GFPGAN footgun is gone).** See `docs/synthid.md` §5.5 + `docs/controlnet-removal-pipeline-research.md` (certified floors table).** **Lesson: visual-quality + face-recovery validation does NOT prove watermark removal -- only the SynthID oracle does, across MULTIPLE content types; never infer removal from sharpness/identity, and never conclude from a partial result (the photoreal-only data first read as "controlnet shields, default removes" -- the flat-graphic result reversed it).** `controlnet_conditioning_scale` (CLI `--controlnet-scale`, default 1.0) is the structure-preservation knob (higher = closer to the original structure); fp32 on cpu/mps, fp16-fixed VAE on cuda/xpu. The `controlnet` profile is threaded explicitly (`WatermarkRemover(pipeline=...)` / `InvisibleEngine(pipeline=...)`), NOT inferred from `model_id`. This productionizes the `scripts/controlnet_sweep.py` prototype; see `docs/controlnet-removal-pipeline-research.md`. **Forensic-stealth caveat still applies** (arXiv:2605.09203): defeating the SynthID verifier is not forensic invisibility -- a "this image went through a removal pipeline" classifier can still flag the output. diff --git a/README.md b/README.md index d7b45b2..8d3486f 100644 --- a/README.md +++ b/README.md @@ -23,7 +23,7 @@ If this tool saves you time, consider [sponsoring its development](https://githu - **AI metadata stripping** — EXIF, PNG text chunks, C2PA provenance manifests (PNG / JPEG / AVIF / HEIF / JPEG-XL, **MP4 / MOV / M4V / M4A** at the container level, and **WebM / MP3 / WAV / FLAC / OGG** losslessly via ffmpeg), XMP DigitalSourceType - **"Made with AI" label removal** — removes the AI-disclosure metadata that platforms read to apply automatic labels (useful for clearing a false-positive label from a human-edited photograph) - **Analog Humanizer** — optional film grain and chromatic aberration post-processing -- **Text and face preservation (experimental)** — optional `--pipeline controlnet` adds a canny ControlNet that keeps text and face structure sharp through the removal pass (without copying original pixels, so SynthID is still removed). Canny preserves face *structure*, not *identity* (the regenerated face drifts in likeness); identity is preserved by the `--restore-faces` GFPGAN post-pass (opt-in). Both are experimental and off by default. +- **Text and face preservation (experimental)** — optional `--pipeline controlnet` adds a canny ControlNet that keeps text and face structure sharp through the removal pass (without copying original pixels, so SynthID is still removed). Canny preserves face *structure*, not *identity* (the regenerated face drifts in likeness); identity is preserved by the `--restore-faces` PhotoMaker-V2 post-pass (opt-in, SynthID-safe). Both are experimental and off by default. - **Batch processing** — process entire directories - **Detection** — three-stage NCC watermark detection with confidence scoring - **Provenance detection (`identify`)** — aggregate C2PA issuer, the C2PA soft-binding forensic-watermark vendor (Adobe TrustMark, Digimarc, Imatag, ...), IPTC "Made with AI" plus the IPTC 2025.1 `AISystemUsed` field, embedded SD/ComfyUI params, EXIF/XMP generator tags, the xAI/Grok EXIF signature, the China TC260 AIGC label (XMP, PNG chunk, or EXIF), the HuggingFace `hf-job-id` job marker, the SynthID metadata proxy, the visible marks (Gemini sparkle plus the Doubao "豆包AI生成" / Jimeng "即梦AI" / Samsung Galaxy AI "Contenuti generati dall'AI" text marks), the open SD/SDXL/FLUX invisible watermark, and (with the `trustmark` extra) the open Adobe TrustMark watermark into one origin-platform + watermark-inventory verdict (`--json` for machine output) @@ -128,7 +128,7 @@ image → encode to latent space (VAE) at native resolution > > **`--pipeline controlnet` preserves text and face structure (experimental, opt-in).** It runs the same SDXL img2img scrub but adds a canny ControlNet that conditions the regeneration on the image's edge map, so text and structure stay sharp at the strengths that remove SynthID. The watermark removal still comes from the img2img regeneration (`--strength`); the ControlNet only preserves structure — no original pixels are copied or frozen, so SynthID does not survive. `--controlnet-scale` tunes the preservation strength (higher = closer to the original structure). Runs fp32 on mps/cpu (fp16 only on cuda/xpu, where the fp16-fixed SDXL VAE is loaded automatically). > -> **`--restore-faces` preserves face identity (GFPGAN, experimental, opt-in).** Canny preserves where a face is, but not who it is — the regenerated face drifts in likeness. The `--restore-faces` post-pass (experimental, off by default; needs the `restore` extra) fixes this: after the removal pass it runs GFPGAN on the original faces and composites the restored face regions into the cleaned image. GFPGAN re-synthesizes each face from a StyleGAN2 prior, so those pixels are GAN-generated (not copied) — the watermark is still scrubbed in the face regions while identity is held (oracle-confirmed clean). It auto-skips when no face is detected or the extra is absent. Tune fidelity with `--restore-faces-weight` (default `0.5`; lower = more regeneration / cleaner scrub, higher = closer to the input). Commercial-safe (GFPGAN is Apache-2.0, its RetinaFace detector MIT); the CodeFormer alternative is non-commercial and is not shipped. (An IP-Adapter FaceID approach was tried earlier and removed: it needs high denoise strength and corrupts faces at the low strength used for removal.) +> **`--restore-faces` preserves face identity (PhotoMaker-V2, experimental, opt-in).** Canny preserves where a face is, but not who it is — the regenerated face drifts in likeness. The `--restore-faces` post-pass (experimental, off by default; needs the `photomaker` extra) fixes this in a SynthID-safe way: identity comes from an OpenCLIP-ViT-H/14 embedding of the original face (validated 2026-06-04: cosine 0.9977 invariance to SynthID-magnitude pixel noise, an order of magnitude less drift than JPEG90 which SynthID survives), and a fresh face is regenerated from that embedding — the pixels are diffusion-fresh, so the watermark is not transported. Commercial-safe end-to-end: PhotoMaker-V2 weights Apache-2.0, OpenCLIP-ViT-H/14 MIT, no InsightFace. The earlier GFPGAN-based `restore` extra was removed 2026-06-04 because it ran on the watermarked original and was oracle-confirmed to re-introduce SynthID; CodeFormer stays non-commercial and is not shipped. See `docs/synthid-robust-identity-research.md`. SDXL is the default since May 2026: empirically defeats SynthID v2 on Gemini 3 Pro outputs, where the older SD-1.5 pipeline at 768 px did not. The SD-1.5 path was removed once it was verified not to handle v2. Note the scope: this defeats the SynthID *verifier*, which is not the same as being forensically indistinguishable from a real photo. Recent work ([arXiv:2605.09203](https://arxiv.org/abs/2605.09203)) shows watermark-removal pipelines leave detectable traces, so a separate "this image was processed" classifier can still flag the output. @@ -136,7 +136,7 @@ SDXL is the default since May 2026: empirically defeats SynthID v2 on Gemini 3 P > **Technical deep-dive:** see [`docs/synthid.md`](docs/synthid.md) for a primary-source-cited breakdown of how SynthID works mechanically (post-hoc encoder/decoder, 136-bit payload, pixel-space embedding), what it empirically survives (JPEG, crop, resize: ~99.98% TPR at 0.1% FPR from arXiv:2510.09263), what removes it, and the forensic-stealth tradeoff (all known removal attacks are detectable at >98% TPR@1%FPR per arXiv:2605.09203). -**Text and face preservation** (experimental, opt-in `--pipeline controlnet`): adds a canny ControlNet so text and face *structure* stay sharp through the removal pass, without copying or freezing any original pixels (so SynthID is still removed). Tune the preservation strength with `--controlnet-scale`. Canny preserves structure but not face *identity* (identity is preserved by the `--restore-faces` GFPGAN post-pass, experimental and off by default — see the callout above). Both features are experimental. +**Text and face preservation** (experimental, opt-in `--pipeline controlnet`): adds a canny ControlNet so text and face *structure* stay sharp through the removal pass, without copying or freezing any original pixels (so SynthID is still removed). Tune the preservation strength with `--controlnet-scale`. Canny preserves structure but not face *identity* (identity is preserved by the `--restore-faces` PhotoMaker-V2 post-pass, experimental and off by default — see the callout above). Both features are experimental. **Analog Humanizer**: optional film grain and chromatic aberration injection that mimics a photo of a screen, raising the bar for AI-generated image classifiers. (It frustrates generic classifiers but does not guarantee forensic invisibility — see the [arXiv:2605.09203](https://arxiv.org/abs/2605.09203) note above.) @@ -215,12 +215,13 @@ After installation the `remove-ai-watermarks` command is available system-wide. > ``` > > To preserve face identity after invisible removal (the `--restore-faces` -> GFPGAN post-pass, experimental and opt-in), install the `restore` extra. The GFPGANv1.4 -> and RetinaFace weights download on first use. It needs Python < 3.13 (basicsr -> does not build on 3.13): +> PhotoMaker-V2 post-pass, experimental and opt-in, SynthID-safe), install the +> `photomaker` extra. The PhotoMaker-V2 adapter and SDXL base weights download on +> first use (~4 GB total). Commercial-safe end-to-end (Apache-2.0 + MIT, no +> InsightFace): > > ```bash -> pip install -e ".[restore]" # or: uv pip install -e ".[restore]" +> pip install -e ".[photomaker]" # or: uv pip install -e ".[photomaker]" > ``` > > For sharper upscaling of small inputs before diffusion (`--upscaler esrgan`, diff --git a/docs/controlnet-removal-pipeline-research.md b/docs/controlnet-removal-pipeline-research.md index 18626cc..64cbd27 100644 --- a/docs/controlnet-removal-pipeline-research.md +++ b/docs/controlnet-removal-pipeline-research.md @@ -124,9 +124,11 @@ Gemini app; the two payloads are vendor-specific and never cross-checked): - **Fix the seed in prod.** The non-determinism is purely `seed=None` (random); a fixed `--seed` makes every run reproduce the certified-clean result, so you ship a deterministic, re-certifiable config (and the seed sweep collapses to one config). -- **Rework `--restore-faces` before any removal use:** run GFPGAN on the diffusion-CLEANED - image (not the original), or drop the weight well below 0.5, or leave it off — then - re-validate on the oracle. +- **`--restore-faces` is SynthID-safe by construction now (PhotoMaker-V2, 2026-06-04).** + The GFPGAN-on-original path that re-added SynthID was removed; the shipped restore + carries identity in a SynthID-invariant OpenCLIP embedding and regenerates fresh + pixels conditioned on it. Needs the `photomaker` extra. See + `docs/synthid-robust-identity-research.md`. - **No local SynthID detector exists** → the service can't self-verify; bake in strength margin and periodic oracle spot-checks. - **Lesson:** visual-quality / face-identity recovery does NOT prove removal — only the diff --git a/docs/synthid.md b/docs/synthid.md index 0e77cd1..c441ab7 100644 --- a/docs/synthid.md +++ b/docs/synthid.md @@ -568,8 +568,9 @@ table. **Net for raiw.cc:** (1) controlnet needs a higher, per-vendor strength than `default` -- CERTIFIED OpenAI 0.20 / Gemini 0.30 (above); add a controlnet-specific schedule to `resolve_strength`, do not reuse the default ladder; (2) the -`--restore-faces` pass can re-add SynthID and must be reworked (restore on the -cleaned image / lower weight / off) before it is safe in a removal pipeline; (3) +`--restore-faces` pass is now SynthID-safe by construction (the GFPGAN-on-original +path that re-added SynthID was removed 2026-06-04; the shipped restore is +PhotoMaker-V2, identity-as-embedding, see `synthid-robust-identity-research.md`); (3) removal near threshold is seed-non-deterministic -> FIX the prod seed (kills the coin-flip; ship a deterministic certified config). diff --git a/pyproject.toml b/pyproject.toml index f9163fc..997a9a1 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -76,42 +76,25 @@ lama = [ "onnxruntime>=1.16.0", "huggingface-hub>=0.20.0", ] -# Optional GFPGAN face-restoration post-pass (commercial-safe Apache-2.0 GFPGAN + -# MIT RetinaFace). Re-synthesizes each face from a StyleGAN2 prior after the -# diffusion removal pass, so it restores identity while still scrubbing the pixel -# watermark. The GFPGANv1.4 weights + RetinaFace detector download on first use; -# they are never bundled. gfpgan/basicsr/facexlib are an OLD ecosystem and must -# stay on numpy < 2.0 to match the pinned gpu diffusion stack -- scipy is capped -# < 1.18 (>= 1.18 uses np.long, gone in numpy 1.24-1.26) and numba < 0.60 to keep -# the whole env on one numpy 1.26 resolution (same trap class as the removed -# faceid/insightface extra). Kept OUT of `all` (heavy + model download). -restore = [ - "gfpgan>=1.3.8", - "facexlib>=0.3.0", - "basicsr>=1.4.2", - "scipy<1.18", - "numba<0.60", -] # Optional PhotoMaker-V2 face-identity restoration (commercial-safe end-to-end: -# PhotoMaker-V2 weights Apache-2.0 + OpenCLIP-ViT-H/14 MIT, NO InsightFace). Unlike -# the `restore` extra above (which runs GFPGAN on the watermarked ORIGINAL and was -# oracle-confirmed to re-introduce SynthID), PhotoMaker carries identity in a -# SEMANTIC EMBEDDING and generates fresh face pixels conditioned on it -- so the -# pixel watermark is not transported. Empirically validated 2026-06-04: the OpenCLIP -# embedding changes by cosine 0.002 under SynthID-magnitude pixel noise (an order of -# magnitude less than JPEG90 drift, which SynthID survives). See +# PhotoMaker-V2 weights Apache-2.0 + OpenCLIP-ViT-H/14 MIT, NO InsightFace). Carries +# identity in a SEMANTIC EMBEDDING and generates fresh face pixels conditioned on it +# -- so the pixel watermark is not transported. Empirically validated 2026-06-04: the +# OpenCLIP embedding changes by cosine 0.002 under SynthID-magnitude pixel noise (an +# order of magnitude less than JPEG90 drift, which SynthID survives). Replaces the +# removed `restore` (GFPGAN) extra, which ran on the watermarked ORIGINAL and was +# oracle-confirmed to re-introduce SynthID. See # docs/synthid-robust-identity-research.md and # src/remove_ai_watermarks/photomaker_restore.py. Weights (~3 GB SDXL + ~1 GB # PhotoMaker-V2 adapter) download on first use; never bundled. Kept OUT of `all` -# (heavy + model download), same as `restore`/`esrgan`. +# (heavy + model download), same as `esrgan`. photomaker = [ "photomaker @ git+https://github.com/TencentARC/PhotoMaker.git", "huggingface-hub>=0.20.0", ] # Optional pre-diffusion super-resolution for small inputs (Real-ESRGAN). Loaded via # spandrel (MIT) -- a pure model-loader with NO basicsr dependency (it pulls only -# torch / torchvision / safetensors / numpy / einops), which sidesteps the -# basicsr / torchvision.functional_tensor breakage that the `restore` extra fights. +# torch / torchvision / safetensors / numpy / einops). # The Real-ESRGAN weights (BSD-3-Clause) download on first use and are cached; they # are never bundled. CPU works but is slow on large inputs -- it is meant for the # pre-diffusion upscale of SMALL inputs (and the GPU worker). Guarded by @@ -137,14 +120,6 @@ all = ["remove-ai-watermarks[gpu,detect,trustmark,lama,dev]"] [tool.uv] prerelease = "allow" -# basicsr 1.4.2 (pulled by the `restore` GFPGAN extra) ships sdist-only and its -# setup.py get_version() reads basicsr/version.py in a way that newer setuptools -# (>= 69) breaks with ``KeyError: '__version__'`` under isolated PEP 517 builds. -# Pin an old setuptools as its build dependency so the sdist builds; this is -# scoped to basicsr and does not affect the rest of the resolution. -[tool.uv.extra-build-dependencies] -basicsr = ["setuptools<69"] - # PyTorch Intel-GPU (XPU) wheel index. ``explicit = true`` keeps it inert for # the default CPU/CUDA install: uv consults it only when a torch install # explicitly targets it (see the ``gpu`` extra comment), so it does not alter diff --git a/src/remove_ai_watermarks/auto_config.py b/src/remove_ai_watermarks/auto_config.py index ee08f94..61c9e02 100644 --- a/src/remove_ai_watermarks/auto_config.py +++ b/src/remove_ai_watermarks/auto_config.py @@ -8,7 +8,7 @@ host (image work there OOM-crashes the container). Routing is **quality-priority**: ControlNet (text/face-structure preservation) is the default; it is only skipped for a clearly structure-less image (no face, no text, -near-zero edges), where plain SDXL is cheaper and just as good. GFPGAN face +near-zero edges), where plain SDXL is cheaper and just as good. PhotoMaker face restoration is enabled when a face is present. When a smoothing pass (controlnet or face restore) ran, the **adaptive polish** (``humanizer.adaptive_polish``) restores the input's detail level -- a capped unsharp + edge-masked grain targeting the input's diff --git a/src/remove_ai_watermarks/cli.py b/src/remove_ai_watermarks/cli.py index e46f732..2a8bdb9 100644 --- a/src/remove_ai_watermarks/cli.py +++ b/src/remove_ai_watermarks/cli.py @@ -236,32 +236,21 @@ def _warn_if_esrgan_unavailable(upscaler: str) -> None: def _restore_faces_options(f: Any) -> Any: - """Attach the shared face-restoration flags to an invisible-pipeline command.""" - restore_flag = click.option( + """Attach the face-restoration flag to an invisible-pipeline command. + + PhotoMaker-V2 is the only restoration method shipped (the prior GFPGAN path was + oracle-confirmed to re-introduce SynthID by partial pixel blending and has been + removed). PhotoMaker carries identity in a SynthID-invariant OpenCLIP embedding + and regenerates fresh face pixels conditioned on it -- see + ``docs/synthid-robust-identity-research.md``. + """ + return click.option( "--restore-faces/--no-restore-faces", default=False, - help="EXPERIMENTAL, opt-in. Restore face identity with a post-pass when faces are " - "present; off by default, auto-skips when no face is detected or the chosen extra " - "is absent.", - ) - method_flag = click.option( - "--restore-faces-method", - type=click.Choice(["gfpgan", "photomaker"]), - default="gfpgan", - help="Face-restore mechanism: 'gfpgan' (cheap, needs 'restore' extra, BUT runs on " - "the watermarked original and re-introduces SynthID) or 'photomaker' (PhotoMaker-V2, " - "needs the 'photomaker' extra; carries identity via a SynthID-invariant OpenCLIP " - "embedding so the regenerated face pixels are watermark-free). Default: gfpgan.", - ) - weight_flag = click.option( - "--restore-faces-weight", - type=float, - default=0.5, - help="GFPGAN fidelity weight (0-1); lower = more GAN regeneration (cleaner " - "watermark scrub), higher = closer to the input. Ignored when " - "--restore-faces-method=photomaker.", - ) - return restore_flag(method_flag(weight_flag(f))) + help="EXPERIMENTAL, opt-in. Restore face identity with the PhotoMaker-V2 post-pass " + "when faces are present (needs the 'photomaker' extra); off by default, auto-skips " + "when no face is detected or the extra is absent.", + )(f) def _watermark_region(det: DetectionResult, width: int, height: int) -> tuple[int, int, int, int]: @@ -612,8 +601,6 @@ def cmd_invisible( min_resolution: int, controlnet_scale: float, restore_faces: bool, - restore_faces_weight: float, - restore_faces_method: str, upscaler: str, auto: bool, adaptive_polish: bool, @@ -676,8 +663,6 @@ def cmd_invisible( upscaler=upscaler, vendor=vendor, restore_faces=restore_faces, - restore_faces_weight=restore_faces_weight, - restore_faces_method=restore_faces_method, ) elapsed = time.monotonic() - t0 @@ -879,8 +864,6 @@ def cmd_all( min_resolution: int, controlnet_scale: float, restore_faces: bool, - restore_faces_weight: float, - restore_faces_method: str, upscaler: str, auto: bool, adaptive_polish: bool, @@ -989,8 +972,6 @@ def cmd_all( upscaler=upscaler, vendor=vendor, restore_faces=restore_faces, - restore_faces_weight=restore_faces_weight, - restore_faces_method=restore_faces_method, ) console.print(" Invisible watermark removed") @@ -1046,8 +1027,6 @@ def _process_batch_image( max_resolution: int = 0, min_resolution: int = 1024, restore_faces: bool = False, - restore_faces_weight: float = 0.5, - restore_faces_method: str = "gfpgan", controlnet_scale: float = 1.0, upscaler: str = "lanczos", auto: bool = False, @@ -1126,8 +1105,6 @@ def _process_batch_image( min_resolution=min_resolution, upscaler=upscaler, restore_faces=restore_faces, - restore_faces_weight=restore_faces_weight, - restore_faces_method=restore_faces_method, # Detect the vendor from the pristine original (`img_path`), not the # visible-processed `out_path` whose C2PA is already gone. vendor=vendor_for_strength(img_path), @@ -1210,8 +1187,6 @@ def cmd_batch( max_resolution: int, min_resolution: int, restore_faces: bool, - restore_faces_weight: float, - restore_faces_method: str, controlnet_scale: float, upscaler: str, auto: bool, @@ -1271,8 +1246,6 @@ def cmd_batch( max_resolution=max_resolution, min_resolution=min_resolution, restore_faces=restore_faces, - restore_faces_weight=restore_faces_weight, - restore_faces_method=restore_faces_method, controlnet_scale=controlnet_scale, upscaler=upscaler, auto=auto, diff --git a/src/remove_ai_watermarks/face_restore.py b/src/remove_ai_watermarks/face_restore.py deleted file mode 100644 index 2cf563a..0000000 --- a/src/remove_ai_watermarks/face_restore.py +++ /dev/null @@ -1,191 +0,0 @@ -"""Optional GFPGAN face-restoration post-pass for the invisible removal pipeline. - -The diffusion removal pass scrubs the watermark everywhere but lets faces drift in -likeness (canny holds face *structure*, not *identity*). This module restores each -face's identity by running GFPGAN on the ORIGINAL (watermarked) image and -feather-compositing the restored face REGIONS into the cleaned image. - -GFPGAN RE-SYNTHESIZES each face from a StyleGAN2 prior -- the composited pixels are -GAN-generated, NOT copied from the original -- so the pixel watermark is scrubbed in -the face regions too, while identity is preserved (oracle-validated at weight 0.5). -Both GFPGAN (Apache-2.0) and its RetinaFace detector (MIT) are commercial-safe. - -The GFPGANv1.4 weights and the RetinaFace detector download on first use and are -never bundled. Requires the optional ``restore`` extra (gfpgan/facexlib/basicsr). -""" - -# cv2/torch/gfpgan boundary: gfpgan/basicsr/facexlib ship no usable type stubs and -# this module wraps cv2 (feather composite) and torch; relax the unknown-type rules -# for this file only. -# pyright: reportUnknownMemberType=false, reportUnknownArgumentType=false, reportUnknownVariableType=false, reportUnknownParameterType=false, reportMissingTypeArgument=false, reportMissingTypeStubs=false, reportMissingImports=false, reportArgumentType=false, reportAssignmentType=false, reportReturnType=false, reportCallIssue=false, reportIndexIssue=false, reportOperatorIssue=false, reportOptionalMemberAccess=false, reportOptionalCall=false, reportOptionalSubscript=false, reportOptionalOperand=false, reportAttributeAccessIssue=false, reportPrivateImportUsage=false, reportPrivateUsage=false, reportInvalidTypeForm=false, reportConstantRedefinition=false, reportUnnecessaryComparison=false -from __future__ import annotations - -import logging -import sys -import threading -from typing import TYPE_CHECKING, Any - -if TYPE_CHECKING: - from numpy.typing import NDArray - -logger = logging.getLogger(__name__) - -# GFPGANv1.4 weights (Apache-2.0). Downloaded on first use, never bundled. -_GFPGAN_MODEL_URL = "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth" -_GFPGAN_ARCH = "clean" -_GFPGAN_CHANNEL_MULTIPLIER = 2 - -_restorer: Any | None = None -_restorer_lock = threading.Lock() - - -def is_available() -> bool: - """True when the optional GFPGAN face-restoration deps are importable.""" - import importlib.util - - return importlib.util.find_spec("gfpgan") is not None and importlib.util.find_spec("facexlib") is not None - - -def _apply_basicsr_shim() -> None: - """Install the ``torchvision.transforms.functional_tensor`` compatibility shim. - - basicsr (a GFPGAN dependency) imports ``rgb_to_grayscale`` from the - ``torchvision.transforms.functional_tensor`` module, which newer torchvision - removed. Recreate that module pointing at the public functional API. Idempotent: - only installed when the real module is missing. - """ - import importlib.util - - if importlib.util.find_spec("torchvision.transforms.functional_tensor") is not None: - return - if "torchvision.transforms.functional_tensor" in sys.modules: - return - - import types - - import torchvision.transforms.functional as tv_functional - - shim = types.ModuleType("torchvision.transforms.functional_tensor") - shim.rgb_to_grayscale = tv_functional.rgb_to_grayscale - sys.modules["torchvision.transforms.functional_tensor"] = shim - - -def _select_device() -> str: - """Pick the GFPGAN device: CUDA when present, else CPU. - - The pip GFPGANer has an MPS device-mismatch bug, and this is a cheap post-pass - on a few face crops, so MPS is deliberately avoided -- CPU is the safe default - on Apple silicon. - """ - try: - import torch - - if torch.cuda.is_available(): - return "cuda" - except Exception as e: - logger.debug("face_restore: CUDA probe failed (%s); using CPU", e) - return "cpu" - - -def _get_restorer() -> Any: - """Return the lazily-built GFPGANer singleton (downloads weights on first use).""" - global _restorer - if _restorer is not None: - return _restorer - with _restorer_lock: - if _restorer is None: - _apply_basicsr_shim() - from gfpgan import GFPGANer - - _restorer = GFPGANer( - model_path=_GFPGAN_MODEL_URL, - upscale=1, - arch=_GFPGAN_ARCH, - channel_multiplier=_GFPGAN_CHANNEL_MULTIPLIER, - device=_select_device(), - ) - return _restorer - - -def _composite_faces( - base_bgr: NDArray[Any], - restored_bgr: NDArray[Any], - boxes: list[tuple[float, float, float, float]], - pad: int = 14, - feather_div: int = 6, -) -> NDArray[Any]: - """Feather-composite restored face regions from ``restored_bgr`` into ``base_bgr``. - - Pure cv2/numpy helper (no gfpgan), so it is unit-testable without the model. - For each ``(x1, y1, x2, y2)`` box: pad and clip to the image, build a Gaussian- - feathered rectangular alpha, and blend ``restored * a + base * (1 - a)``. Boxes - that fall fully outside the image (or an empty list) leave ``base_bgr`` unchanged. - """ - import cv2 - import numpy as np - - out = base_bgr.astype(np.float32) - h, w = base_bgr.shape[:2] - - for box in boxes: - x1 = int(box[0]) - pad - y1 = int(box[1]) - pad - x2 = int(box[2]) + pad - y2 = int(box[3]) + pad - x1 = max(0, min(x1, w)) - y1 = max(0, min(y1, h)) - x2 = max(0, min(x2, w)) - y2 = max(0, min(y2, h)) - bw = x2 - x1 - bh = y2 - y1 - if bw <= 0 or bh <= 0: - continue - - alpha = np.zeros((h, w), dtype=np.float32) - alpha[y1:y2, x1:x2] = 1.0 - k = max(3, (min(bw, bh) // feather_div) | 1) # odd kernel >= 3 - alpha = cv2.GaussianBlur(alpha, (k, k), 0) - alpha = alpha[:, :, None] - out = restored_bgr.astype(np.float32) * alpha + out * (1.0 - alpha) - - return np.clip(out, 0, 255).astype(np.uint8) - - -def restore_faces( - original_bgr: NDArray[Any], - cleaned_bgr: NDArray[Any], - weight: float = 0.5, - pad: int = 14, - feather_div: int = 6, -) -> NDArray[Any]: - """Restore face identity in ``cleaned_bgr`` using GFPGAN on ``original_bgr``. - - Runs GFPGAN on the ORIGINAL (watermarked) image to recover the true-identity, - GAN-regenerated faces plus the RetinaFace boxes, then feather-composites those - face regions into the cleaned image. The composited pixels are GFPGAN-generated - (not original), so no watermark and no pixel-copy. Returns ``cleaned_bgr`` - unchanged when no face is detected. - - Args: - original_bgr: The original (watermarked) image as cv2 BGR. - cleaned_bgr: The diffusion-cleaned image as cv2 BGR (faces drifted). - weight: GFPGAN fidelity weight (0-1); lower = more GAN regeneration. - pad: Pixels to grow each face box before compositing. - feather_div: Larger = sharper composite edge (box-min // feather_div kernel). - """ - restorer = _get_restorer() - _, _, restored_img = restorer.enhance( - original_bgr, - has_aligned=False, - only_center_face=False, - paste_back=True, - weight=weight, - ) - - det_faces = getattr(restorer.face_helper, "det_faces", None) or [] - boxes = [(float(b[0]), float(b[1]), float(b[2]), float(b[3])) for b in det_faces] - if not boxes: - logger.debug("face_restore: no faces detected; returning cleaned image unchanged") - return cleaned_bgr - - return _composite_faces(cleaned_bgr, restored_img, boxes, pad=pad, feather_div=feather_div) diff --git a/src/remove_ai_watermarks/invisible_engine.py b/src/remove_ai_watermarks/invisible_engine.py index 37e3a14..bbb3219 100644 --- a/src/remove_ai_watermarks/invisible_engine.py +++ b/src/remove_ai_watermarks/invisible_engine.py @@ -165,8 +165,6 @@ class InvisibleEngine: min_resolution: int = 1024, vendor: str | None = None, restore_faces: bool = False, - restore_faces_weight: float = 0.5, - restore_faces_method: str = "gfpgan", unsharp: float = 0.0, adaptive_polish: bool = False, upscaler: str = "lanczos", @@ -182,22 +180,16 @@ class InvisibleEngine: guidance_scale: Classifier-free guidance scale. seed: Random seed for reproducibility. humanize: Intensity of Analog Humanizer film grain (0 = off). - restore_faces: EXPERIMENTAL, opt-in (default False). Run the GFPGAN - face-restoration post-pass when faces are present (needs the - ``restore`` extra). Auto-skips with a debug log when the extra is - absent or no face is detected. - restore_faces_method: Which face-identity restoration mechanism to run after - the diffusion pass: ``"gfpgan"`` (default; cheap, but WARNING the GFPGAN - pass runs on the watermarked ORIGINAL and re-introduces SynthID -- see - ``face_restore.py``) or ``"photomaker"`` (PhotoMaker-V2; carries identity - via a SynthID-invariant OpenCLIP embedding and regenerates fresh face - pixels conditioned on it -- SynthID-safe, but heavier and requires the - ``photomaker`` extra). See ``docs/synthid-robust-identity-research.md``. - restore_faces_weight: GFPGAN fidelity weight (0-1); lower = more GAN - regeneration (cleaner watermark scrub), higher = closer to input. + restore_faces: EXPERIMENTAL, opt-in (default False). Run the PhotoMaker-V2 + face-identity post-pass when faces are present (needs the + ``photomaker`` extra). Carries identity via a SynthID-invariant OpenCLIP + embedding and regenerates fresh face pixels conditioned on it, so the + pixel watermark is not transported. Auto-skips with a debug log when the + extra is absent or no face is detected. See + ``docs/synthid-robust-identity-research.md``. unsharp: Final unsharp-mask sharpening strength (0 = off, default). Applied last (after face restoration) to counter the soft, - over-smoothed look of the diffusion/GFPGAN passes; ~0.5-0.8 is a + over-smoothed look of the diffusion + restoration; ~0.5-0.8 is a safe range, higher risks edge halos. adaptive_polish: When True (the --auto mode default), restore the input's detail level in the softened output instead of fixed unsharp/humanize: @@ -320,19 +312,16 @@ class InvisibleEngine: out_cv = cv2.resize(out_cv, orig_size, interpolation=cv2.INTER_LANCZOS4) image_io.imwrite(out_path, out_cv) - # Optional GFPGAN face-restoration post-pass: restore face identity that - # the diffusion regeneration drifted, while still scrubbing the pixel - # watermark (GFPGAN re-synthesizes faces from a StyleGAN2 prior). Runs on - # the cleaned output at its final resolution; auto-skips when faces are + # Optional PhotoMaker-V2 face-identity post-pass: restore face identity that + # the diffusion regeneration drifted, carrying identity in a SynthID-invariant + # OpenCLIP embedding so the regenerated face pixels are watermark-free. Runs + # on the cleaned output at its final resolution; auto-skips when faces are # absent or the optional extra is not installed. if restore_faces: - if restore_faces_method == "photomaker": - self._restore_faces_photomaker(out_path, image, seed) - else: - self._restore_faces(out_path, image, restore_faces_weight) + self._restore_faces_photomaker(out_path, image, seed) # Final sharpening, LAST so it crisps the face-restored result too (a - # pre-GFPGAN sharpen would be smoothed back over by the face pass). + # pre-restore sharpen would be smoothed back over by the face pass). if unsharp > 0.0: import cv2 @@ -368,55 +357,6 @@ class InvisibleEngine: if _tmp_path.exists(): _tmp_path.unlink() - def _restore_faces( - self, - out_path: Path, - original_image: Any, - weight: float, - ) -> None: - """Run the GFPGAN face-restoration post-pass on the cleaned ``out_path``. - - Composites GFPGAN-restored (identity-preserving, watermark-scrubbed) face - regions from the ORIGINAL image into the cleaned output. Best-effort: any - failure logs a warning and leaves the un-restored cleaned output in place; - a missing ``restore`` extra is logged at debug and skipped (the default-on - flag must never error when the extra is absent or no face is present). - """ - from remove_ai_watermarks import face_restore - - if not face_restore.is_available(): - logger.debug("restore_faces requested but the 'restore' extra is not installed; skipping") - return - - try: - import cv2 - import numpy as np - - from remove_ai_watermarks import image_io - - cleaned_bgr = image_io.imread(out_path, cv2.IMREAD_COLOR) - if cleaned_bgr is None: - logger.warning("restore_faces: could not read cleaned output %s; skipping", out_path) - return - - # Original (EXIF-transposed) as BGR, aligned to the cleaned image so the - # GFPGAN face boxes land in the cleaned image's coordinate space. The - # cleaned output is already restored to the original resolution above, so - # this resize is normally a no-op (it only fires if a max-resolution cap - # left the source PIL image smaller). - original_rgb = original_image.convert("RGB") - original_bgr = cv2.cvtColor(np.array(original_rgb), cv2.COLOR_RGB2BGR) - cleaned_size = (cleaned_bgr.shape[1], cleaned_bgr.shape[0]) - if (original_bgr.shape[1], original_bgr.shape[0]) != cleaned_size: - original_bgr = cv2.resize(original_bgr, cleaned_size, interpolation=cv2.INTER_LANCZOS4) - - if self._progress_callback: - self._progress_callback("Restoring face identity (GFPGAN post-pass)...") - restored = face_restore.restore_faces(original_bgr, cleaned_bgr, weight=weight) - image_io.imwrite(out_path, restored) - except Exception as e: - logger.warning("restore_faces post-pass failed (%s); keeping un-restored output", e) - def _restore_faces_photomaker( self, out_path: Path, diff --git a/tests/test_face_restore.py b/tests/test_face_restore.py deleted file mode 100644 index c57bb38..0000000 --- a/tests/test_face_restore.py +++ /dev/null @@ -1,85 +0,0 @@ -"""Tests for the GFPGAN face-restoration post-pass. - -The pure feather-composite helper is unit-tested without the model; the -model-running paths are gated behind ``is_available()`` (a multi-hundred-MB -download), matching the discipline used for the other ML-adjacent modules. -""" - -from __future__ import annotations - -import numpy as np -import pytest - -from remove_ai_watermarks import face_restore - - -class TestIsAvailable: - def test_returns_bool(self): - assert isinstance(face_restore.is_available(), bool) - - def test_reflects_dependencies(self): - import importlib.util - - expected = all(importlib.util.find_spec(m) is not None for m in ("gfpgan", "facexlib")) - assert face_restore.is_available() is expected - - -class TestCompositeFaces: - """Unit tests for the pure ``_composite_faces`` helper (cv2/numpy only).""" - - def _base_and_restored(self, h: int = 100, w: int = 120): - base = np.zeros((h, w, 3), dtype=np.uint8) # black - restored = np.full((h, w, 3), 255, dtype=np.uint8) # white - return base, restored - - def test_output_shape_and_dtype(self): - base, restored = self._base_and_restored() - out = face_restore._composite_faces(base, restored, [(40.0, 30.0, 80.0, 70.0)]) - assert out.shape == base.shape - assert out.dtype == np.uint8 - - def test_box_region_pulls_toward_restored(self): - base, restored = self._base_and_restored() - out = face_restore._composite_faces(base, restored, [(40.0, 30.0, 80.0, 70.0)]) - # Center of the box should be near the restored (white) value. - cy, cx = 50, 60 - assert out[cy, cx].mean() > 200 - - def test_far_from_box_stays_base(self): - base, restored = self._base_and_restored() - out = face_restore._composite_faces(base, restored, [(40.0, 30.0, 80.0, 70.0)], pad=2) - # Top-left corner is far from the box and feather, so it stays black. - assert out[0, 0].mean() < 5 - - def test_empty_boxes_returns_base_unchanged(self): - base, restored = self._base_and_restored() - out = face_restore._composite_faces(base, restored, []) - assert np.array_equal(out, base) - - def test_box_fully_outside_is_skipped(self): - base, restored = self._base_and_restored(h=100, w=120) - # Box entirely beyond the right/bottom edge -> clipped to empty -> no-op. - out = face_restore._composite_faces(base, restored, [(200.0, 200.0, 260.0, 260.0)], pad=0) - assert np.array_equal(out, base) - - def test_near_edge_box_clips_without_error(self): - base, restored = self._base_and_restored(h=100, w=120) - # Box reaching past the bottom-right corner must clip, not raise. - out = face_restore._composite_faces(base, restored, [(100.0, 80.0, 130.0, 110.0)], pad=10) - assert out.shape == base.shape - # The clipped in-bounds region still pulls toward white. - assert out[95, 115].mean() > 100 - - -@pytest.mark.skipif(not face_restore.is_available(), reason="requires the 'restore' extra (gfpgan/facexlib)") -class TestRestoreFacesModel: - """Model-running smoke test, gated behind the optional extra.""" - - def test_no_faces_returns_cleaned_unchanged(self): - # A flat gray image has no faces; restore_faces must return the cleaned - 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