mirror of
https://github.com/wiltodelta/remove-ai-watermarks.git
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Victor KuznetsovandClaude Opus 5
29de21256b
The Hub model and the private training repository were both called raiw-models, so the training repo's README had to spend a paragraph telling the two apart. Renamed on both sides: the Hub model to raiw-photo-classify and the GitHub repository to raiw-classifier-training. Done now because it is free now. classify.py landed after v0.36.0 and is in no release, and the Hub model had zero all-time downloads, so no installed package and no user carries the old id. Hugging Face redirects the old URL and preserves counts, and the pinned freeze revision c0ac82b6 resolves on the new name -- verified by downloading through the library's own constants. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Maintainer scripts
These scripts are development and evaluation tools, not installed CLI commands. Run
them from the repository root with uv run python scripts/<name>.py --help. Inputs
under .local-eval/ and generated reports remain untracked unless a data README
explicitly names a tracked canonical result.
Audits and release checks
| Script | Purpose |
|---|---|
corpus_gap_scan.py |
Compare a local image corpus with the library's identify results. |
detection_timing.py |
Record per-method metadata and verdict timings. |
detection_timing_report.py |
Aggregate timing records by method and segment. |
fidelity_metrics.py |
Compute objective image-fidelity metrics for paired outputs. |
invisible_quality_audit.py |
Pair originals and invisible-removal outputs for quality review. |
metadata_removal_audit.py |
Check metadata detection/removal parity over a corpus. |
pill_gate_audit.py |
Measure the Jimeng pill detector on the product path. |
real_examples_e2e.py |
Run end-to-end confidence checks over local examples. |
record_parity_audit.py |
Compare record-based and file-based identification. |
resource_ceilings.py |
Measure peak RSS and runtime for fill backends. |
robustness_suite.py |
Exercise CLI failures on adversarial and degenerate inputs. |
sidecar_regression.py |
Compare current identification with recorded sidecars. |
smoke_matrix.py |
Exercise CLI parameter choices on real local data. |
video_fidelity_probe.py |
Compare delivered video fidelity with its source. |
visible_eval.py |
Benchmark registered visible-mark detectors. |
visible_removal_audit.py |
Audit visible-removal results over a local corpus. |
Calibration and corpus preparation
| Script | Purpose |
|---|---|
contentseal_transforms.py |
Reproduce and hash-check deterministic Content Seal variants. |
detector_response.py |
Measure detector response over mark size, contrast, background, and aspect. |
fill_quality.py |
Measure visible-fill quality against constructed ground truth. |
ladder_headroom.py |
Measure recall cost from the coarse scale ladder. |
registered_mark_calibrate.py |
Measure a registered detector without conflating visual positives, metadata cohorts, adjudicated negatives, and unlabeled controls. |
synthid_corpus.py |
Ingest and inspect the local SynthID reference corpus. |
vendor_cohort_harvest.py |
Partition TC260 carriers by producer code. |
vendor_mark_calibrate.py |
Calibrate a candidate vendor text detector. |
visible_alpha_solve.py |
Rebuild visible-watermark alpha assets from controlled captures. |
visible_groundtruth.py |
Consolidate blinded contact-sheet labels into ground truth. |
visible_positives.py |
List corpus images carrying a registered visible mark. |
visible_recall_sample.py |
Build an unbiased blinded sample for recall measurement. |
visible_sheets.py |
Build blinded contact sheets for relaxation candidates. |
retrain_photo_classify.py |
CPU-retrain the 2026-08-31 photo heads from a sha256-keyed cache pack, no images. |
publish_photo_classify_hf.py |
Upload the photo-classify card and freeze weights to Hugging Face wiltodelta/raiw-photo-classify. Manual; the Action publish-photo-classify-hf.yml is the write-token path. |
verify_engine_selection_fixtures.py |
Verify hashes, dimensions, and prompt pairing in the tracked auto-engine content matrix. |
Research and diagnostic prototypes
| Script | Purpose |
|---|---|
chroma_text_restoration_study.py |
Compare verified-text restoration for Qwen and Chroma on H100 without running both engines in production. |
cjk_tail_probe.py |
Test a generic template for otherwise uncovered CJK labels. |
controlnet_sweep.py |
Sweep the historical ControlNet removal prototype. |
engine_selection_study.py |
Run one global engine at a time over the tracked prompt-matched content matrix. |
analyze_engine_selection_study.py |
Reduce engine-study output to paired deltas and exact sign tests. |
infer_text_lines.py |
Draft stable source-text lines without modifying pixels. |
qwen_scrub_prototype.py |
Probe low-strength Qwen regeneration on a GPU. |
selective_text_restoration.py |
Evaluate text restoration over a scrubbed image. |
synthid_pixel_probe.py |
Run the experimental local SynthID carrier probe. |
video_synthid_sweep.py |
Build oracle-gated video regeneration candidates. |
Generated assets
| Script | Purpose |
|---|---|
render_pill_silhouette.py |
Render the synthetic Jimeng pill silhouette. |
render_vendor_silhouettes.py |
Render synthetic vendor text-mark silhouettes. |
Shared helpers
_plain_console.py provides plain-text fallbacks for Rich output, and
_text_eval.py contains normalization helpers shared by text-evaluation scripts.