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feat(visible): capture-less AI生成 pill (#54), inpaint fallback, MI-GAN backend (#56)

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- Add the Jimeng-basic top-left "AI生成" pill as a CAPTURE-LESS mark
  (pill_engine.py): synthetic-silhouette edge-NCC detect + inpaint-only removal.
  Gated in remove_auto_marks: kept only when Jimeng is confirmed (TC260 metadata
  OR the bottom-right "★ 即梦AI" wordmark fired -- the wordmark keeps recall on
  metadata-STRIPPED uploads) AND Doubao did not fire.
- Add an inpaint-fallback removal path + MI-GAN ONNX backend (migan extra, MIT,
  ~28 MB / ~1 GB peak -- droplet-friendly) alongside big-LaMa. New
  --method auto|reverse-alpha|inpaint (shared across visible/all/batch) and
  erase --backend migan; footprint_mask on each engine.
- auto is deterministic: reverse-alpha for capture marks (recovers exact pixels,
  lighter -- measured cleaner than MI-GAN on structured backgrounds) and inpaint
  only for the capture-less pill.
- --mark auto now removes EVERY detected mark in one pass (remove_auto_marks),
  so a Jimeng-basic image's top-left pill AND bottom-right wordmark both clear.
- Bump 0.12.1 -> 0.13.0.

Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
This commit is contained in:
Victor Kuznetsov
2026-07-06 20:38:23 +03:00
committed by GitHub
co-authored by Claude Opus 4.8
parent 0f54c6b54d
commit 0e5a4cbc54
18 changed files with 994 additions and 96 deletions
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src/remove_ai_watermarks/cli.py
+108 -51
View File
@@ -293,6 +293,18 @@ _force_option = click.option(
)
_visible_method_option = click.option(
"--method",
"removal_method",
type=click.Choice(["auto", "reverse-alpha", "inpaint"]),
default="auto",
help="Visible-mark removal method. auto: reverse-alpha for capture marks (exact "
"pixels, lighter), inpaint for the capture-less pill. reverse-alpha recovers "
"pixels from a captured alpha map; inpaint erases the footprint (MI-GAN with the "
"'migan' extra, else cv2).",
)
def _resolve_auto_polish(auto: bool, adaptive_polish: bool) -> bool:
"""Warn on the retired ``--auto`` flag, returning ``adaptive_polish`` unchanged.
@@ -327,9 +339,23 @@ def _warn_if_esrgan_unavailable(upscaler: str) -> None:
console.print(" Note: --upscaler esrgan needs the 'esrgan' extra; falling back to Lanczos.")
def _aigc_metadata_present(path: Path) -> bool:
"""True when the file carries a China-AIGC (TC260) metadata label. Used to gate
the weak-detector 'AI生成' pill: metadata confirms Jimeng-class provenance. NB
this is only ONE of two confirmations -- ``remove_auto_marks`` also accepts the
bottom-right wordmark, so a metadata-STRIPPED upload can still be handled."""
with contextlib.suppress(Exception):
from remove_ai_watermarks import metadata
return bool(metadata.aigc_label(path))
return False
def _remove_visible_auto(
image: NDArray[Any],
*,
source_path: Path | None = None,
removal_method: str = "auto",
inpaint: bool = True,
inpaint_method: str = "ns",
inpaint_strength: float = 0.85,
@@ -340,19 +366,26 @@ def _remove_visible_auto(
standalone ``visible`` command uses, so EVERY registered mark is handled (the
Gemini sparkle AND the Doubao/Jimeng/Samsung text marks), not just the sparkle.
Returns ``(result, label-or-None)``; when no ``in_auto`` mark fires the image is
returned unchanged with ``None``. ``inpaint*`` tune the Gemini edge-residual
cleanup only (the text engines ignore them).
returned unchanged with ``None``. ``removal_method`` selects reverse-alpha vs the
inpaint fallback (see ``KnownMark.remove``); ``inpaint*`` tune the Gemini
edge-residual cleanup only (the text engines ignore them).
"""
from remove_ai_watermarks import watermark_registry
best = watermark_registry.best_auto_mark(image)
if best is None:
return image, None
rmethod: watermark_registry.RemovalMethod = removal_method # type: ignore[assignment]
method: Literal["telea", "ns"] = "ns" if inpaint_method == "ns" else "telea"
result, _ = watermark_registry.get_mark(best.key).remove(
image, inpaint_method=method, inpaint=inpaint, inpaint_strength=inpaint_strength, force=False
pill_md = _aigc_metadata_present(source_path) if source_path is not None else False
result, removed = watermark_registry.remove_auto_marks(
image,
pill_metadata=pill_md,
method=rmethod,
inpaint_method=method,
inpaint=inpaint,
inpaint_strength=inpaint_strength,
)
return result, best.label
if not removed:
return image, None
return result, ", ".join(removed)
# Exit code for the standalone ``visible`` command when no visible mark was
@@ -535,8 +568,9 @@ def main(ctx: click.Context, verbose: bool) -> None:
type=click.Choice(["auto", *watermark_registry.mark_keys()]),
default="auto",
help="Which known visible mark to target (auto picks the strongest detected). "
"All marks are removed by exact reverse-alpha against a captured alpha map.",
"Removal method is chosen by --method (default auto).",
)
@_visible_method_option
@click.option("--strip-metadata/--keep-metadata", default=True, help="Strip AI metadata from output.")
@click.pass_context
def cmd_visible(
@@ -548,14 +582,17 @@ def cmd_visible(
inpaint_strength: float,
detect: bool,
mark: str,
removal_method: str,
strip_metadata: bool,
) -> None:
"""Remove a known visible AI watermark from an image.
Finds a known mark in its usual place (Gemini sparkle / Doubao text) via the
watermark registry and removes it by exact reverse-alpha against a captured
alpha map -- recovering the true pixels, not an inpaint guess. ``--mark auto``
picks the strongest detected mark. For arbitrary logos/objects, use ``erase``.
watermark registry and removes it. Default ``--method auto`` recovers the true
pixels by exact reverse-alpha for the capture marks, and inpaints only the
capture-less "AI生成" pill (MI-GAN with the ``migan`` extra, else cv2).
``--mark auto`` picks the strongest detected mark. For arbitrary logos/objects,
use ``erase``.
"""
from remove_ai_watermarks import watermark_registry as registry
@@ -574,41 +611,52 @@ def cmd_visible(
h, w = image.shape[:2]
console.print(f" Input: {source.name} ({w}x{h})")
# Resolve the target mark from the known-watermark registry. ``auto`` scans
# every in-auto mark in its usual place and picks the strongest; an explicit
# ``--mark <key>`` targets that one (the user asserts its presence).
if mark == "auto":
best = registry.best_auto_mark(image)
if best is None:
console.print(" No known visible mark detected (gemini / doubao / jimeng / samsung).")
if detect:
_no_visible_mark_exit(source)
target = "gemini" # forced (no-detect): fall back to the default mark
else:
target = best.key
console.print(f" Mark auto: {best.label} ({best.location}, conf {best.confidence:.2f})")
else:
target = mark
chosen = registry.get_mark(target)
det = chosen.detect(image)
if detect and not det.detected:
console.print(f" {chosen.label} not detected (conf {det.confidence:.2f}). Use --no-detect to force.")
_no_visible_mark_exit(source)
if det.detected:
console.print(f" {chosen.label} detected ({chosen.location}, conf {det.confidence:.2f})")
method: Literal["telea", "ns"] = "ns" if inpaint_method == "ns" else "telea"
t0 = time.monotonic()
with console.status(f"Removing {chosen.label}... ({chosen.recovery})"):
result, _ = chosen.remove(
image,
inpaint_method=method,
inpaint=inpaint,
inpaint_strength=inpaint_strength,
force=not detect,
)
elapsed = time.monotonic() - t0
# ``auto`` removes EVERY detected in_auto mark in one pass (a Jimeng-basic image
# carries the top-left pill AND the bottom-right wordmark); an explicit
# ``--mark <key>`` targets that one (the user asserts its presence).
if mark == "auto" and detect:
t0 = time.monotonic()
with console.status("Detecting & removing visible marks..."):
result, removed = registry.remove_auto_marks(
image,
pill_metadata=_aigc_metadata_present(source),
method=removal_method, # type: ignore[arg-type]
inpaint_method=method,
inpaint=inpaint,
inpaint_strength=inpaint_strength,
)
elapsed = time.monotonic() - t0
if not removed:
console.print(" No known visible mark detected (gemini / doubao / jimeng / jimeng-pill / samsung).")
_no_visible_mark_exit(source)
console.print(f" Removed: {', '.join(removed)}")
else:
target = "gemini" if mark == "auto" else mark # --no-detect auto: gemini fallback
chosen = registry.get_mark(target)
det = chosen.detect(image)
if detect and not det.detected:
console.print(f" {chosen.label} not detected (conf {det.confidence:.2f}). Use --no-detect to force.")
_no_visible_mark_exit(source)
if det.detected:
console.print(f" {chosen.label} detected ({chosen.location}, conf {det.confidence:.2f})")
resolved = registry.resolve_removal_method(removal_method, chosen.has_capture) # type: ignore[arg-type]
if resolved == "inpaint" and not registry.inpaint_model_available():
console.print(
" Note: --method inpaint using cv2 (install the 'migan' extra for a lightweight ONNX model)."
)
t0 = time.monotonic()
with console.status(f"Removing {chosen.label}... ({resolved})"):
result, _ = chosen.remove(
image,
method=removal_method, # type: ignore[arg-type]
inpaint_method=method,
inpaint=inpaint,
inpaint_strength=inpaint_strength,
force=not detect,
)
elapsed = time.monotonic() - t0
# Save (rejoins the original alpha plane unchanged)
output.parent.mkdir(parents=True, exist_ok=True)
@@ -653,9 +701,10 @@ def _parse_region(spec: str) -> tuple[int, int, int, int]:
)
@click.option(
"--backend",
type=click.Choice(["cv2", "lama"]),
type=click.Choice(["cv2", "migan", "lama"]),
default="cv2",
help="Inpaint backend. cv2: instant, no deps. lama: onnxruntime big-LaMa, better quality (extra 'lama').",
help="Inpaint backend. cv2: instant, no deps. migan: light ONNX MI-GAN, ~1 GB RAM, "
"near-LaMa quality (extra 'migan'). lama: big-LaMa, best quality but ~4.7 GB RAM (extra 'lama').",
)
@click.option("--inpaint-method", type=click.Choice(["telea", "ns"]), default="telea", help="cv2 inpaint method.")
@click.option("--dilate", type=int, default=3, help="Grow the box by this many px before inpainting.")
@@ -666,7 +715,7 @@ def cmd_erase(
source: Path,
regions: tuple[str, ...],
output: Path | None,
backend: Literal["cv2", "lama"],
backend: Literal["cv2", "migan", "lama"],
inpaint_method: str,
dilate: int,
strip_metadata: bool,
@@ -999,6 +1048,7 @@ def cmd_identify(ctx: click.Context, source: Path, no_visible: bool, as_json: bo
@click.option(
"--inpaint-method", type=click.Choice(["ns", "telea", "gaussian"]), default="ns", help="Inpainting method."
)
@_visible_method_option
@_strength_option
@click.option("--steps", type=int, default=50, help="Number of denoising steps for invisible removal.")
@_pipeline_option
@@ -1036,6 +1086,7 @@ def cmd_all(
output: Path | None,
inpaint: bool,
inpaint_method: Literal["ns", "telea", "gaussian"],
removal_method: str,
strength: float | None,
steps: int,
pipeline: str,
@@ -1105,7 +1156,9 @@ def cmd_all(
console.print(f" Input: {source.name} ({w}x{h})")
with console.status("Removing visible watermark..."):
result, removed_label = _remove_visible_auto(image, inpaint=inpaint, inpaint_method=inpaint_method)
result, removed_label = _remove_visible_auto(
image, source_path=source, removal_method=removal_method, inpaint=inpaint, inpaint_method=inpaint_method
)
if removed_label is not None:
console.print(f" Visible watermark removed ({removed_label})")
else:
@@ -1244,6 +1297,7 @@ def _process_batch_image(
seed: int | None,
hf_token: str | None,
humanize: float,
removal_method: str = "auto",
unsharp: float = 0.0,
max_resolution: int = 0,
min_resolution: int = 1024,
@@ -1276,7 +1330,7 @@ def _process_batch_image(
if image is None:
raise ValueError("Failed to read image")
result, _ = _remove_visible_auto(image, inpaint=inpaint)
result, _ = _remove_visible_auto(image, source_path=img_path, removal_method=removal_method, inpaint=inpaint)
_write_bgr_with_alpha(out_path, result, alpha)
saved_alpha = alpha
@@ -1361,6 +1415,7 @@ def _process_batch_image(
@_strength_option
@click.option("--steps", type=int, default=50, help="Number of denoising steps (invisible mode).")
@click.option("--inpaint/--no-inpaint", default=True, help="Apply inpainting (visible mode).")
@_visible_method_option
@click.option(
"--humanize", type=float, default=0.0, help="Analog Humanizer film grain intensity (0 = off, typical: 2.0-6.0)."
)
@@ -1402,6 +1457,7 @@ def cmd_batch(
seed: int | None,
hf_token: str | None,
inpaint: bool,
removal_method: str,
humanize: float,
unsharp: float,
max_resolution: int,
@@ -1468,6 +1524,7 @@ def cmd_batch(
seed=seed,
hf_token=hf_token,
humanize=humanize,
removal_method=removal_method,
unsharp=unsharp,
max_resolution=max_resolution,
min_resolution=min_resolution,