For AI-enhanced composites (digitalSourceType compositeWithTrainedAlgorithmicMedia,
identify ai_source_kind == "enhanced"; roadmap P1#8): regenerate ONLY the AI
region and preserve the real photo elsewhere, instead of regenerating the whole
frame.
- noai.tiling.feather_region_composite(base, regenerated, box, *, feather): pure,
model-free compositor that blends the regenerated AI box back over the original
with a feathered seam, leaving pixels OUTSIDE the box exactly equal to base.
Fully unit-tested (outside-box exactness, interior == regenerated, hard paste at
feather 0, monotonic seam ramp, dtype/grayscale/clamp/empty-box/shape-mismatch).
- WatermarkRemover.remove_watermark(region=, region_feather=) and the module-level
convenience function thread it through: the remover regenerates (or tiles) the
frame, then composites only the AI box back over the original input. The box is
caller-supplied -- a C2PA composite manifest carries no reliable machine-readable
region, so none is fabricated. The no-model lossless region path stays
region_eraser.erase.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Add a lossless alternative to the --max-resolution downscale for large
images that OOM on MPS/GPU: regenerate in overlapping, feather-blended
tiles at native resolution.
- noai/tiling.py: pure plan_tiles (uniform tiles, last flush to edge) +
feather_weights (strictly-positive separable taper -> partition-of-unity
blend) + run_tiled (per-tile generate callable, decoupled from the
pipeline). Unit-tested without the model.
- WatermarkRemover.remove_watermark: refactor _generate into _generate_one
+ a tiled branch that engages only when --tile is set and the long side
exceeds tile_size (ControlNet canny is rebuilt per tile).
- Thread tile/tile_size/tile_overlap through InvisibleEngine and the
invisible/all/batch CLI commands via a shared _tile_options decorator.
Verified end-to-end on the real SDXL pipeline (forced 2x2 tiling on a
1024px sample, MPS): non-degenerate output, no gross seam at tile borders.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Add `--pipeline controlnet` (SDXL base + xinsir canny ControlNet via
StableDiffusionXLControlNetImg2ImgPipeline): the canny edge map conditions the
img2img regeneration so text and face STRUCTURE stay sharp, while the watermark
is still removed by the regeneration (`strength`) -- no original pixels are
copied or frozen, so SynthID does not survive. Oracle-verified clean on OpenAI
with better text/structure fidelity than plain img2img at equal strength.
`--controlnet-scale` tunes structure preservation; fp32 on mps/cpu (fp16-fixed
VAE on cuda/xpu). Shares the img2img runner (live progress + MPS->CPU fallback)
and the fp16-VAE-fix / device-move helpers with the default pipeline.
Remove the superseded subsystems -- ctrlregen (SD1.5 clean-noise),
text-protection (differential / region-hires) and face-protection: they either
destroyed real content or shielded the watermark by re-using original pixels.
controlnet replaces them by regenerating everything under edge conditioning.
Canny preserves face structure but not identity; face IDENTITY is a separate
face-restoration post-pass (CodeFormer/GFPGAN), researched + prototyped but not
yet shipped. An IP-Adapter FaceID attempt was built and removed (footgun: needs
high strength, corrupts faces at removal strength).
Docs: docs/controlnet-removal-pipeline-research.md, scripts/controlnet_sweep.py.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Apply fixes from a full-repo review (code, tests, docs).
Security / correctness:
- Clamp attacker-controlled PNG/caBX chunk lengths to the remaining file
size in metadata.py and noai/c2pa.py (a malformed length no longer drives
a multi-GB read); skipped chunks seek instead of read.
- noai/isobmff.strip_c2pa_boxes is now fail-safe on a malformed box: return
the original bytes with a warning instead of silently truncating the tail,
so metadata --remove can no longer emit a corrupt file.
- doubao_engine._fixed_alpha_map clamps the glyph box to the image (no crash
on degenerate width-vs-height).
- watermark_remover._run_region_hires gates the phaseCorrelate offset on
response and magnitude (a spurious shift no longer garbles text) and drops
the generator after a CPU fallback (no MPS/CPU device mismatch).
Robustness:
- gemini_engine, doubao_engine, region_eraser normalize grayscale and RGBA
inputs to BGR at the engine entry points.
- image_io.imwrite returns False on an unwritable path (matches cv2).
- invisible_engine guards a None imread result before use.
- trustmark_detector._decoder uses a double-checked threading lock.
- ctrlregen.tiling.tile_positions raises on overlap >= tile.
- humanizer chromatic shift no longer wraps opposite-edge pixels.
- identify OpenAI caveat keyed on the normalized vendor, not a substring.
- Remove the dead "visible --detect-threshold" CLI option.
- publish.yml verifies the release tag matches the package version.
Docs:
- README strength 0.05 to 0.10; .env.example HF_TOKEN marked optional;
doubao_capture README updated to reverse-alpha-only; CLAUDE.md synced with
the new behaviors and the batch command.
Tests: new test_security_clamp.py for the read clamp and isobmff fail-safe;
erase CLI coverage; integrity-clash rule 2 end-to-end; multi-tag EXIF
survival and cross-format strip guards; channel/size, tiling, humanizer, and
imwrite regressions. Full suite 493 passed, 2 skipped; ruff and pyright src/
clean.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Coverage audit (pytest --cov) found real, non-model logic at 0%/low cover.
Add unit tests that need no model download:
- img2img_runner.py 0% -> 100%: the MPS->CPU fallback orchestration, mocked
via injected load_pipeline/reload_on_cpu callables. Guards the production
behavior hit this session (native-res SDXL OOMs on MPS, must retry on CPU;
non-MPS errors must propagate; "mps"-worded error on a cpu device must not
reload).
- ctrlregen/tiling.py 0% -> 40%: the pure tile math (tile_positions,
make_blend_weight, resize_center_crop) that decides how large images are
split and blended. (run_tiled stays model-bound, untested.)
- isobmff.py 93% -> 100%: size==0 (box-to-EOF) and truncated 64-bit largesize
parsing branches for AVIF/HEIF/JXL C2PA stripping.
- c2pa.py: non-PNG-signed .png reads as clean (has_c2pa_metadata /
extract_c2pa_chunk) instead of mis-parsing.
309 tests pass (+23). Document in CLAUDE.md that these pure helpers are
unit-tested without downloads so future sessions don't skip them as "ML".
No src/ change, no release.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Victor Kuznetsov