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https://github.com/aloshdenny/reverse-SynthID.git
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feat(scripts): add V4 codebook build, batch dissolve, and calibration scripts
build_codebook_v4.py — builds SpectralCodebookV4 from the hierarchical reverse-synthid-dataset (model × color × resolution). dissolve_batch.py — runs all bypass presets (gentle … nuke) over an input directory. Supports Round-06 'final' and 'nuke' strengths. calibrate_from_feedback.py — updates carrier_weights from detection feedback, closing the human-in-the-loop calibration loop. Made-with: Cursor
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Alosh Denny
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#!/usr/bin/env python3
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"""
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Phase-2 driver for the reverse-SynthID V4 manual-validation loop.
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Takes an input folder of watermarked images and emits one or more strength
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variants per image (``A``, ``B``, ``C``, ... by default). Writes a
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``manifest.csv`` that pairs each variant with:
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- source image path
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- output path
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- strength preset
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- profile key used
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- PSNR / SSIM achieved
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You then paste the variants into the Gemini app, run SynthID detection, and
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fill in a small ``tally.csv`` (columns: ``source,variant,still_watermarked``,
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values ``y/n``). Feed both files into ``calibrate_from_feedback.py`` to
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update the codebook's per-carrier weights and iterate.
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Usage::
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python scripts/dissolve_batch.py \\
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--input /path/to/input_images \\
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--output /path/to/out_dir \\
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--codebook artifacts/spectral_codebook_v4.npz \\
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--model nano-banana-pro-preview \\
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--strengths gentle moderate aggressive
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Strengths map to filesystem-safe single-letter variants (A,B,C,D) in
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manifest order, which makes the tally CSV trivial to fill by hand.
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"""
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from __future__ import annotations
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import argparse
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import csv
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import glob
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import os
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import sys
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import time
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from typing import List, Optional
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REPO_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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sys.path.insert(0, os.path.join(REPO_ROOT, "src", "extraction"))
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import cv2 # noqa: E402
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import numpy as np # noqa: E402
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from synthid_bypass_v4 import SpectralCodebookV4, SynthIDBypassV4 # noqa: E402
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IMAGE_EXTS = (".png", ".jpg", ".jpeg", ".webp")
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DEFAULT_STRENGTHS = ("gentle", "moderate", "aggressive")
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VARIANT_LETTERS = "ABCDEFGH"
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def iter_input_images(input_path: str) -> List[str]:
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"""Resolve ``--input`` (file, directory, or glob) to a sorted list."""
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if os.path.isdir(input_path):
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out: List[str] = []
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for ext in IMAGE_EXTS:
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out.extend(glob.glob(os.path.join(input_path, f"*{ext}")))
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out.extend(glob.glob(os.path.join(input_path, f"*{ext.upper()}")))
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return sorted(set(out))
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if os.path.isfile(input_path):
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return [input_path]
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# Treat as a glob pattern.
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return sorted(glob.glob(input_path))
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def dissolve_one(
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bypass: SynthIDBypassV4,
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codebook: SpectralCodebookV4,
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src: str,
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out_dir: str,
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variant_letter: str,
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strength: str,
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model: Optional[str],
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) -> dict:
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"""Dissolve one image at one strength; return a manifest row."""
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base = os.path.splitext(os.path.basename(src))[0]
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out_name = f"{base}__{variant_letter}_{strength}.png"
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out_path = os.path.join(out_dir, out_name)
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t0 = time.time()
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try:
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result = bypass.bypass_v4_file(
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src, out_path, codebook,
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strength=strength, model=model, verify=False,
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)
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row = {
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"source": os.path.abspath(src),
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"variant": variant_letter,
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"strength": strength,
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"output": os.path.abspath(out_path),
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"profile_key": result.details["profile_key"],
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"exact_match": int(bool(result.details["exact_match"])),
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"psnr": round(result.psnr, 3),
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"ssim": round(result.ssim, 5),
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"n_passes_applied": result.details["n_passes_applied"],
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"n_passes_rolled_back": result.details["n_passes_rolled_back"],
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"elapsed_sec": round(time.time() - t0, 3),
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"still_watermarked": "", # filled by you during validation
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"notes": "",
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}
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except Exception as e:
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row = {
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"source": os.path.abspath(src),
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"variant": variant_letter,
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"strength": strength,
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"output": "",
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"profile_key": "",
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"exact_match": 0,
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"psnr": "",
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"ssim": "",
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"n_passes_applied": 0,
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"n_passes_rolled_back": 0,
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"elapsed_sec": round(time.time() - t0, 3),
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"still_watermarked": "",
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"notes": f"ERROR: {e}",
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}
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return row
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def run(
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input_path: str,
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out_dir: str,
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codebook_path: str,
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strengths: List[str],
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model: Optional[str] = None,
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limit: Optional[int] = None,
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manifest_name: str = "manifest.csv",
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) -> str:
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sources = iter_input_images(input_path)
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if limit is not None:
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sources = sources[:limit]
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if not sources:
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print(f"No images found in {input_path}")
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sys.exit(2)
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os.makedirs(out_dir, exist_ok=True)
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codebook = SpectralCodebookV4()
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codebook.load(codebook_path)
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if model is not None and model not in codebook.models:
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print(f"WARNING: --model {model} not found in codebook. "
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f"Available: {codebook.models}. Proceeding anyway "
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"(best-effort fallback across models).")
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bypass = SynthIDBypassV4()
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if len(strengths) > len(VARIANT_LETTERS):
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raise ValueError(
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f"Too many strengths ({len(strengths)}); "
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f"max supported: {len(VARIANT_LETTERS)}"
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)
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letters = list(VARIANT_LETTERS[:len(strengths)])
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manifest_path = os.path.join(out_dir, manifest_name)
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fieldnames = [
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"source", "variant", "strength", "output", "profile_key",
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"exact_match", "psnr", "ssim",
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"n_passes_applied", "n_passes_rolled_back",
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"elapsed_sec", "still_watermarked", "notes",
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]
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print(f"Dissolving {len(sources)} image(s) × {len(strengths)} variant(s) "
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f"→ {out_dir}")
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if model:
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print(f"Model hint: {model}")
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rows = []
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for i, src in enumerate(sources):
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print(f"[{i + 1}/{len(sources)}] {os.path.basename(src)}")
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for letter, strength in zip(letters, strengths):
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row = dissolve_one(
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bypass=bypass,
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codebook=codebook,
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src=src,
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out_dir=out_dir,
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variant_letter=letter,
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strength=strength,
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model=model,
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)
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rows.append(row)
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if row["notes"].startswith("ERROR"):
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print(f" {letter}/{strength:12s} {row['notes']}")
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else:
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print(f" {letter}/{strength:12s} "
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f"psnr={row['psnr']:>6} ssim={row['ssim']:>7} "
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f"profile={row['profile_key']} "
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f"exact={row['exact_match']}")
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with open(manifest_path, "w", newline="") as f:
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writer = csv.DictWriter(f, fieldnames=fieldnames)
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writer.writeheader()
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writer.writerows(rows)
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print(f"\nManifest: {manifest_path}")
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print("\nNext steps:")
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print(" 1. Upload each ABS-path output to the Gemini app and run "
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"SynthID detection.")
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print(" 2. For each row, fill the `still_watermarked` column with "
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"`y` or `n` (leave blank to skip).")
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print(f" 3. Save the filled file as tally.csv and run:")
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print(f" python scripts/calibrate_from_feedback.py "
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f"--manifest {manifest_path} --tally <your_tally.csv> "
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f"--codebook {codebook_path}")
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return manifest_path
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def main() -> None:
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p = argparse.ArgumentParser(
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description="Emit bypass variants for manual Gemini validation.",
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)
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p.add_argument("--input", required=True,
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help="Path to an image, a directory, or a glob pattern.")
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p.add_argument("--output", required=True,
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help="Directory to write variants and manifest.csv into.")
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p.add_argument("--codebook", required=True,
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help="Path to the V4 codebook .npz.")
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p.add_argument("--strengths", nargs="+", default=list(DEFAULT_STRENGTHS),
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choices=["gentle", "moderate", "aggressive", "maximum",
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"demolish", "annihilate", "combo",
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"blog_pure", "blog_plus", "blog_combo",
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"residual_pure", "residual_plus", "residual_combo",
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"regen_pure", "regen_plus", "regen_combo",
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"final", "nuke"],
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help=f"Strengths to emit (default: {DEFAULT_STRENGTHS}).")
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p.add_argument("--model", default=None,
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help=(
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"Optional model hint (e.g. nano-banana-pro-preview). "
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"Omit to let the codebook auto-select by resolution."
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))
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p.add_argument("--limit", type=int, default=None,
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help="Stop after this many input images (for quick tests).")
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p.add_argument("--manifest-name", default="manifest.csv",
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help="Manifest filename inside --output (default: manifest.csv).")
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args = p.parse_args()
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run(
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input_path=args.input,
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out_dir=args.output,
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codebook_path=args.codebook,
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strengths=args.strengths,
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model=args.model,
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limit=args.limit,
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manifest_name=args.manifest_name,
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)
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if __name__ == "__main__":
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main()
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