"""Unified CLI for remove-ai-watermarks. Provides commands for: - Visible watermark removal (Gemini sparkle) - works offline, fast - Invisible watermark removal (SynthID etc.) - requires GPU/diffusion models - AI metadata stripping - lightweight, no ML deps needed """ from __future__ import annotations import contextlib import json import logging import time from pathlib import Path from typing import TYPE_CHECKING, Any, Literal, NoReturn import click from remove_ai_watermarks import __version__, watermark_registry from remove_ai_watermarks.noai.constants import SUPPORTED_FORMATS from remove_ai_watermarks.noai.watermark_profiles import ( resolve_strength, strength_default_help, vendor_for_strength, ) if TYPE_CHECKING: from collections.abc import Generator from numpy.typing import NDArray # --- plain-text output layer (replaces rich: no colors, no markup, no boxes) --- class _Table: """Plain-text stand-in for rich.Table.""" def __init__(self, *args: Any, title: str | None = None, **kwargs: Any) -> None: self._title = title self._headers: list[str] = [] self._rows: list[list[str]] = [] def add_column(self, header: str = "", *args: Any, **kwargs: Any) -> None: self._headers.append(str(header)) def add_row(self, *cells: Any) -> None: self._rows.append([str(c) for c in cells]) def render(self) -> str: lines: list[str] = [] if self._title: lines.append(self._title) if any(self._headers): lines.append(" ".join(self._headers)) lines.extend(" ".join(row) for row in self._rows) return "\n".join(f" {line}" for line in lines) class _Progress: """No-op stand-in for rich.Progress; results are printed directly instead.""" def __init__(self, *args: Any, **kwargs: Any) -> None: pass def __enter__(self) -> _Progress: return self def __exit__(self, *exc: object) -> bool: return False def add_task(self, *args: Any, **kwargs: Any) -> int: return 0 def advance(self, *args: Any, **kwargs: Any) -> None: pass def update(self, *args: Any, **kwargs: Any) -> None: pass class _Console: """Minimal plain-text replacement for rich.Console.""" def print(self, *objects: Any, **kwargs: Any) -> None: click.echo(" ".join(o.render() if isinstance(o, _Table) else str(o) for o in objects)) @contextlib.contextmanager def status(self, message: str = "", **kwargs: Any) -> Generator[None, None, None]: if message: click.echo(message) yield def _panel(text: str = "", *args: Any, **kwargs: Any) -> str: return text def _column(*args: Any, **kwargs: Any) -> None: return None Panel = _panel Table = _Table Progress = _Progress SpinnerColumn = BarColumn = TextColumn = TimeElapsedColumn = _column console = _Console() def _setup_logging(verbose: bool) -> None: level = logging.DEBUG if verbose else logging.WARNING logging.basicConfig( level=level, format="%(name)s | %(message)s", handlers=[logging.StreamHandler()], ) def _banner() -> None: console.print( Panel( f"Remove-AI-Watermarks v{__version__}\nVisible & invisible watermark removal", border_style="cyan", padding=(0, 2), ) ) def _validate_image(path: Path) -> Path: if not path.exists(): console.print(f"Error: File not found: {path}") raise SystemExit(1) if path.suffix.lower() not in SUPPORTED_FORMATS: console.print(f"Warning: {path.suffix} may not be supported (expected: {', '.join(SUPPORTED_FORMATS)})") return path _ALPHA_FORMATS = {".png", ".webp"} # Shared option decorator for commands that run the invisible-watermark pipeline. # Both cmd_invisible and cmd_all expose this flag; defining it once avoids # copy-paste drift. _controlnet_scale_option = click.option( "--controlnet-scale", type=float, default=1.0, help="ControlNet conditioning scale (structure/text preservation strength); " "applies to the controlnet pipeline (the default). Higher = closer to original structure.", ) _min_resolution_option = click.option( "--min-resolution", type=int, default=1024, help="Upscale long side UP to this (px) before diffusion when the input is smaller, so SDXL runs " "near 1024 (small inputs distort at native); output is restored to the input size. 0 = off. Default 1024.", ) _unsharp_option = click.option( "--unsharp", type=float, default=0.0, help="Unsharp-mask sharpening strength (0 = off, typical: 0.3-0.8)." ) _upscaler_option = click.option( "--upscaler", type=click.Choice(["lanczos", "esrgan"]), default="lanczos", help="How to upscale a small input to the --min-resolution floor: lanczos (default, cv2, no deps) or " "esrgan (Real-ESRGAN via the 'esrgan' extra; better detail, slower on CPU). Best for photo/texture " "content -- as a generic GAN with no face/glyph prior it can degrade faces (diffusion mitigates) and " "thin text, so lanczos stays the default. Falls back to lanczos if the extra is absent. Only when upscaling.", ) _auto_option = click.option( "--auto", is_flag=True, default=False, help="DEPRECATED: controlnet is already the default pipeline, so --auto now only " "enables --adaptive-polish (the content detectors were removed). Use " "--adaptive-polish instead.", ) _adaptive_polish_option = click.option( "--adaptive-polish/--no-adaptive-polish", default=True, help="Restore the input's detail level after removal (capped unsharp + edge-masked grain " "targeting the input's sharpness, sparing text), countering the over-smoothed look. ON by " "default; it self-limits where there is no detail deficit (text/flat graphics), so it is a " "no-op there. Pass --no-adaptive-polish to disable. Independent of --unsharp/--humanize.", ) # HuggingFace model + CFG knobs, shared by the diffusion commands (invisible/all/batch) # so the surface stays identical across them. _model_option = click.option( "--model", type=str, default=None, help="HuggingFace model ID for the diffusion pipeline. Default: the SDXL base checkpoint.", ) _guidance_scale_option = click.option( "--guidance-scale", type=float, default=None, help="Classifier-free guidance scale (CFG). Default: 7.5 (the library default). " "Lower = follow the prompt less / stay closer to the input.", ) def _normalize_pipeline(ctx: click.Context, param: click.Parameter, value: str | None) -> str | None: """Resolve the legacy ``default`` profile name to ``sdxl`` (click option callback). Emits a one-line deprecation notice when the user explicitly passes the outdated ``default`` value, pointing at the two current choices (``sdxl`` / ``controlnet``). """ if value is None: return None from remove_ai_watermarks.noai.watermark_profiles import normalize_profile normalized = normalize_profile(value) if value.strip().lower() == "default": click.echo( "Warning: --pipeline default is deprecated and maps to 'sdxl'. " "Use --pipeline sdxl (plain SDXL) or --pipeline controlnet (the default).", err=True, ) return normalized # ``controlnet`` (the default-SELECTED value) and ``sdxl`` (plain SDXL img2img) are the # two current profiles; ``default`` is an OUTDATED back-compat alias for ``sdxl`` # (warned + normalized away by _normalize_pipeline). _PIPELINE_CHOICES = ["sdxl", "controlnet", "default"] _PIPELINE_HELP = ( "Pipeline profile. controlnet (DEFAULT) = SDXL + canny ControlNet that preserves " "text/faces via edge conditioning while removing SynthID; sdxl = plain SDXL img2img " "(lighter, no extra model download, but leaves SynthID on flat-graphic content). " "('default' is an OUTDATED alias for 'sdxl' -- use sdxl or controlnet.)" ) # Shared --pipeline / --strength decorators so the three diffusion commands # (invisible/all/batch) keep an identical surface and the strength help can never # drift from the watermark_profiles constants (strength_default_help derives it). _pipeline_option = click.option( "--pipeline", type=click.Choice(_PIPELINE_CHOICES), default="controlnet", callback=_normalize_pipeline, help=_PIPELINE_HELP, ) _strength_option = click.option( "--strength", type=float, default=None, help=f"Denoising strength (0.0-1.0). Default: {strength_default_help()}.", ) def _resolve_auto_polish(auto: bool, adaptive_polish: bool) -> bool: """Warn on the retired ``--auto`` flag, returning ``adaptive_polish`` unchanged. ``--auto`` used to plan the pipeline + polish from content detection, but the pipeline is now always controlnet (the default) and the adaptive polish is ON by default (it self-gates by detail level), so the content detectors were removed and ``--auto`` is now a no-op alias: the polish it used to enable is already the default, and an explicit ``--no-adaptive-polish`` still wins. So it only emits a deprecation warning and passes ``adaptive_polish`` through. """ if auto: click.echo( "Warning: --auto is deprecated and now does nothing (the adaptive polish it " "enabled is ON by default). Use --no-adaptive-polish to turn the polish off.", err=True, ) return adaptive_polish def _warn_if_esrgan_unavailable(upscaler: str) -> None: """Tell the user once if ``--upscaler esrgan`` will silently fall back to Lanczos. The engine downgrades to Lanczos when the ``esrgan`` extra is absent (fail-safe, so a batch never breaks mid-run) -- but without this notice the user would believe Real-ESRGAN ran. Surfaced at the CLI layer, once per invocation (not per image). """ if upscaler != "esrgan": return from remove_ai_watermarks import upscaler as _upscaler if not _upscaler.is_available(): console.print(" Note: --upscaler esrgan needs the 'esrgan' extra; falling back to Lanczos.") def _remove_visible_auto( image: NDArray[Any], *, inpaint: bool = True, inpaint_method: str = "ns", inpaint_strength: float = 0.85, ) -> tuple[NDArray[Any], str | None]: """Remove the strongest auto-detected visible mark via the registry. Routes the ``all``/``batch`` visible step through the same registry path the 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). """ from remove_ai_watermarks import watermark_registry best = watermark_registry.best_auto_mark(image) if best is None: return image, None 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 ) return result, best.label # Exit code for the standalone ``visible`` command when no visible mark was # removed -- distinct from success (0) and a hard error (1) so a wrapping # service can tell "nothing to do here" apart and surface guidance instead of # re-serving the unchanged input as a finished result. EXIT_NO_VISIBLE_MARK = 2 def _no_visible_mark_exit(source: Path) -> NoReturn: """Explain why no visible watermark was removed, then exit non-zero. The visible registry handles only known visual marks (the Gemini sparkle and the Doubao/Jimeng/Samsung text strips). Most real uploads carry no such mark -- frequently an invisible/metadata watermark instead (e.g. an OpenAI or Gemini image whose only signal is C2PA + SynthID). Returning the input unchanged with exit 0 reads as success to a caller and re-serves the watermarked image -- the recurring "it didn't work" report. Instead, run a cheap metadata-only :func:`identify`, tell the user what the image actually carries and which command removes it, and exit :data:`EXIT_NO_VISIBLE_MARK`. """ from remove_ai_watermarks.identify import identify report = identify(source, check_visible=False, check_invisible=False) if report.is_ai_generated and report.watermarks: plat = report.platform or "an unidentified platform" console.print( f" This image carries an invisible/metadata watermark ({plat}), not a visible mark,\n" " so the 'visible' command cannot remove it. Run the full pipeline instead:\n" f" remove-ai-watermarks all {source.name}" ) else: console.print( " No visible mark and no readable AI provenance signal. This does not prove\n" " the image is clean: an invisible pixel watermark such as SynthID cannot be\n" " detected here once the metadata proxy is absent (it may have been stripped\n" " earlier). If the image is AI-generated, regenerate the pixels with:\n" f" remove-ai-watermarks all {source.name}\n" " If instead there is a logo or object to remove, target it with the region eraser:\n" f" remove-ai-watermarks erase {source.name} --region x,y,w,h" ) raise SystemExit(EXIT_NO_VISIBLE_MARK) def _read_bgr_and_alpha(path: Path) -> tuple[NDArray[Any] | None, NDArray[Any] | None]: """Read an image preserving its alpha channel separately. Returns ``(bgr, alpha)`` where ``alpha`` is a single-channel ndarray when the source has transparency, else ``None``. Greyscale inputs are promoted to BGR. Returns ``(None, None)`` if the image cannot be decoded. """ import cv2 from remove_ai_watermarks import image_io image = image_io.imread(path, cv2.IMREAD_UNCHANGED) if image is None: return None, None if image.ndim == 2: return cv2.cvtColor(image, cv2.COLOR_GRAY2BGR), None if image.shape[2] == 4: return image[:, :, :3].copy(), image[:, :, 3].copy() return image, None def _write_bgr_with_alpha( path: Path, bgr: NDArray[Any], alpha: NDArray[Any] | None, ) -> None: """Write BGR (with optional alpha) to ``path``. When ``alpha`` is provided and the output extension supports it, the original alpha plane is rejoined unchanged. The watermark region is NOT made transparent: reverse-alpha (and inpaint) recover real pixels there, so zeroing alpha would punch a transparent hole that renders as a white box on any non-transparent viewer (issue #30). Preserving the input alpha keeps genuinely transparent backgrounds intact without inventing new holes. """ import numpy as np from remove_ai_watermarks import image_io if alpha is None or path.suffix.lower() not in _ALPHA_FORMATS: image_io.imwrite(path, bgr) return bgra = np.dstack([bgr, alpha]) image_io.imwrite(path, bgra) # -- Main group ------------------------------------------------------- @click.group(invoke_without_command=True) @click.version_option(__version__, prog_name="remove-ai-watermarks") @click.option("-v", "--verbose", is_flag=True, help="Enable verbose logging.") @click.pass_context def main(ctx: click.Context, verbose: bool) -> None: """Remove visible and invisible AI watermarks from images.""" from dotenv import load_dotenv load_dotenv() # Load .env (e.g. HF_TOKEN) ctx.ensure_object(dict) ctx.obj["verbose"] = verbose _setup_logging(verbose) if ctx.invoked_subcommand is None: _banner() click.echo(ctx.get_help()) # -- Visible (Gemini) watermark removal ------------------------------- @main.command("visible") @click.argument("source", type=click.Path(exists=True, path_type=Path)) @click.option( "-o", "--output", type=click.Path(path_type=Path), default=None, help="Output path (default: _clean.)." ) @click.option("--inpaint/--no-inpaint", default=True, help="Apply inpainting cleanup after removal.") @click.option( "--inpaint-method", type=click.Choice(["ns", "telea", "gaussian"]), default="ns", help="Inpainting method." ) @click.option("--inpaint-strength", type=float, default=0.85, help="Inpainting blend strength (0.0-1.0).") @click.option("--detect/--no-detect", default=True, help="Detect watermark before removal.") @click.option( "--mark", 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.", ) @click.option("--strip-metadata/--keep-metadata", default=True, help="Strip AI metadata from output.") @click.pass_context def cmd_visible( ctx: click.Context, source: Path, output: Path | None, inpaint: bool, inpaint_method: Literal["ns", "telea", "gaussian"], inpaint_strength: float, detect: bool, mark: 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``. """ from remove_ai_watermarks import watermark_registry as registry _banner() source = _validate_image(source) if output is None: output = source.with_stem(source.stem + "_clean") # Load image (preserving any alpha channel separately) image, alpha = _read_bgr_and_alpha(source) if image is None: console.print(f"Error: Failed to read image: {source}") raise SystemExit(1) 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 `` 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 # Save (rejoins the original alpha plane unchanged) output.parent.mkdir(parents=True, exist_ok=True) _write_bgr_with_alpha(output, result, alpha) # Strip metadata if strip_metadata: try: from remove_ai_watermarks.metadata import remove_ai_metadata remove_ai_metadata(output, output) except Exception as e: if ctx.obj.get("verbose"): console.print(f" Warning: Failed to strip metadata: {e}") size_kb = output.stat().st_size / 1024 console.print(f" Saved: {output} ({size_kb:.0f} KB, {elapsed:.2f}s)") # -- Universal region eraser ----------------------------------------- def _parse_region(spec: str) -> tuple[int, int, int, int]: """Parse an ``x,y,w,h`` region string into a 4-int tuple.""" parts = spec.replace(" ", "").split(",") if len(parts) != 4: raise click.BadParameter(f"region must be 'x,y,w,h', got: {spec!r}") try: x, y, w, h = (int(p) for p in parts) except ValueError as e: raise click.BadParameter(f"region values must be integers: {spec!r}") from e if w <= 0 or h <= 0: raise click.BadParameter(f"region width/height must be positive: {spec!r}") return x, y, w, h @main.command("erase") @click.argument("source", type=click.Path(exists=True, path_type=Path)) @click.option("--region", "regions", multiple=True, required=True, help="x,y,w,h box to erase (repeatable).") @click.option( "-o", "--output", type=click.Path(path_type=Path), default=None, help="Output path (default: _clean.)." ) @click.option( "--backend", type=click.Choice(["cv2", "lama"]), default="cv2", help="Inpaint backend. cv2: instant, no deps. lama: onnxruntime big-LaMa, better quality (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.") @click.option("--strip-metadata/--keep-metadata", default=True, help="Strip AI metadata from output.") @click.pass_context def cmd_erase( ctx: click.Context, source: Path, regions: tuple[str, ...], output: Path | None, backend: Literal["cv2", "lama"], inpaint_method: str, dilate: int, strip_metadata: bool, ) -> None: """Erase arbitrary region(s) from an image via inpainting. Universal and position-agnostic: removes any logo / watermark / object inside the boxes you pass, regardless of colour or location. Runs on CPU. Use this for marks the dedicated ``visible`` engines (Gemini, Doubao) do not cover. """ from remove_ai_watermarks.region_eraser import erase _banner() source = _validate_image(source) if output is None: output = source.with_stem(source.stem + "_clean") boxes = [_parse_region(r) for r in regions] image, alpha = _read_bgr_and_alpha(source) if image is None: console.print(f"Error: Failed to read image: {source}") raise SystemExit(1) h, w = image.shape[:2] console.print(f" Input: {source.name} ({w}x{h}) {len(boxes)} region(s), backend={backend}") t0 = time.monotonic() method: Literal["telea", "ns"] = "ns" if inpaint_method == "ns" else "telea" try: with console.status(f"Erasing ({backend})..."): result = erase(image, boxes=boxes, backend=backend, dilate=dilate, cv2_method=method) except RuntimeError as e: console.print(f" Error: {e}") raise SystemExit(1) from e elapsed = time.monotonic() - t0 output.parent.mkdir(parents=True, exist_ok=True) _write_bgr_with_alpha(output, result, alpha) if strip_metadata: try: from remove_ai_watermarks.metadata import remove_ai_metadata remove_ai_metadata(output, output) except Exception as e: if ctx.obj.get("verbose"): console.print(f" Warning: Failed to strip metadata: {e}") size_kb = output.stat().st_size / 1024 console.print(f" Erased {len(boxes)} region(s) -> {output} ({size_kb:.0f} KB, {elapsed:.2f}s)") # -- Invisible watermark removal ------------------------------------- @main.command("invisible") @click.argument("source", type=click.Path(exists=True, path_type=Path)) @click.option( "-o", "--output", type=click.Path(path_type=Path), default=None, help="Output path (default: _clean.)." ) @_strength_option @click.option("--steps", type=int, default=50, help="Number of denoising steps. Default: 50.") @_pipeline_option @click.option( "--device", type=click.Choice(["auto", "cpu", "mps", "cuda", "xpu"]), default="auto", help="Inference device.", ) @click.option("--seed", type=int, default=None, help="Random seed for reproducibility.") @click.option("--hf-token", type=str, default=None, help="HuggingFace API token.") @click.option( "--humanize", type=float, default=0.0, help="Analog Humanizer film grain intensity (0 = off, typical: 2.0-6.0)." ) @click.option( "--max-resolution", type=int, default=0, help="Cap long side (px) before diffusion; 0 = native (best quality, like raiw.cc). Raise only on GPU/MPS OOM.", ) @_controlnet_scale_option @_min_resolution_option @_unsharp_option @_upscaler_option @_model_option @_guidance_scale_option @_auto_option @_adaptive_polish_option @click.pass_context def cmd_invisible( ctx: click.Context, source: Path, output: Path | None, strength: float | None, steps: int, pipeline: str, device: str, seed: int | None, hf_token: str | None, humanize: float, unsharp: float, max_resolution: int, min_resolution: int, controlnet_scale: float, upscaler: str, model: str | None, guidance_scale: float | None, auto: bool, adaptive_polish: bool, ) -> None: """Remove invisible AI watermarks (SynthID, StableSignature, TreeRing). Uses diffusion-based regeneration. Requires GPU for reasonable speed. Requires the [gpu] extra: pip install 'remove-ai-watermarks[gpu]' """ from remove_ai_watermarks.invisible_engine import is_available as invisible_available if not invisible_available(): console.print( "Error: GPU dependencies not installed.\n Install them with: pip install 'remove-ai-watermarks[gpu]'" ) raise SystemExit(1) from remove_ai_watermarks.invisible_engine import InvisibleEngine source = _validate_image(source) _warn_if_esrgan_unavailable(upscaler) adaptive_polish = _resolve_auto_polish(auto, adaptive_polish) if output is None: output = source.with_stem(source.stem + "_clean") device_str = None if device == "auto" else device def progress_cb(msg: str) -> None: console.print(f" {msg}") engine = InvisibleEngine( model_id=model, device=device_str, pipeline=pipeline, hf_token=hf_token, progress_callback=progress_cb, controlnet_conditioning_scale=controlnet_scale, ) # Detect the SynthID vendor from the ORIGINAL (before processing strips C2PA) so the # displayed and executed strength agree on the vendor-adaptive default. vendor = vendor_for_strength(source) console.print(f" Input: {source.name}") console.print(f" Pipeline: {pipeline}") console.print(f" Strength: {resolve_strength(strength, vendor)} Steps: {steps}") t0 = time.monotonic() result_path = engine.remove_watermark( image_path=source, output_path=output, strength=strength, num_inference_steps=steps, guidance_scale=guidance_scale, seed=seed, humanize=humanize, unsharp=unsharp, adaptive_polish=adaptive_polish, max_resolution=max_resolution, min_resolution=min_resolution, upscaler=upscaler, vendor=vendor, ) elapsed = time.monotonic() - t0 size_kb = result_path.stat().st_size / 1024 console.print(f"\n Saved: {result_path} ({size_kb:.0f} KB, {elapsed:.1f}s)") # -- Metadata operations --------------------------------------------- @main.command("metadata") @click.argument("source", type=click.Path(exists=True, path_type=Path)) @click.option("--check", is_flag=True, help="Check for AI metadata (don't modify).") @click.option("--remove", is_flag=True, help="Remove AI metadata.") @click.option( "-o", "--output", type=click.Path(path_type=Path), default=None, help="Output path (default: overwrite source)." ) @click.option("--keep-standard/--remove-all", default=True, help="Keep standard metadata (Author, Title, etc.).") @click.pass_context def cmd_metadata( ctx: click.Context, source: Path, check: bool, remove: bool, output: Path | None, keep_standard: bool, ) -> None: """Check or remove AI-generation metadata (images, video, and audio). Strips EXIF AI tags, PNG text chunks, C2PA provenance manifests, and the China TC260 AIGC label. Beyond images (PNG/JPEG/WebP/AVIF/HEIF/JXL) it also strips provenance metadata from MP4/MOV/M4V/M4A containers and, via ffmpeg, from WebM/MP3/WAV/FLAC/OGG. The coded image, audio, and video data are left untouched. """ from remove_ai_watermarks.metadata import get_ai_metadata, has_ai_metadata, remove_ai_metadata # No _validate_image() here: unlike the image-only commands, metadata also # accepts video/audio containers, so the image-format warning would misfire. # click's `exists=True` on the argument already enforces the file exists. _banner() if check or (not remove): has_ai = has_ai_metadata(source) if has_ai: console.print(f" Warning: AI metadata detected in {source.name}:") meta = get_ai_metadata(source) if synthid := meta.get("synthid_watermark"): console.print(f" Warning: SynthID watermark (inferred from C2PA metadata) {synthid}") table = Table(show_header=True, header_style="bold") table.add_column("Key", style="cyan") table.add_column("Value") for k, v in meta.items(): table.add_row(k, str(v)[:80]) console.print(table) else: console.print(f" No AI metadata found in {source.name}") if not remove: return # Remove out = remove_ai_metadata(source, output, keep_standard=keep_standard) console.print(f" AI metadata stripped -> {out}") # -- Provenance identification --------------------------------------- @main.command("identify") @click.argument("source", type=click.Path(exists=True, path_type=Path)) @click.option( "--no-visible", is_flag=True, help="Skip pixel-domain detectors (visible sparkle + invisible watermark); metadata-only.", ) @click.option("--json", "as_json", is_flag=True, help="Emit the report as JSON instead of a table.") @click.pass_context def cmd_identify(ctx: click.Context, source: Path, no_visible: bool, as_json: bool) -> None: """Identify where an image was made and what watermarks it carries. Aggregates C2PA Content Credentials, IPTC "Made with AI" tags, embedded generation parameters, the SynthID metadata proxy, and the visible Gemini sparkle into a single provenance verdict. Absence of signals is reported as "unknown", never as "clean" (stripped metadata leaves no local proof). """ from dataclasses import asdict from remove_ai_watermarks.identify import identify source = _validate_image(source) report = identify(source, check_visible=not no_visible, check_invisible=not no_visible) if as_json: click.echo(json.dumps(asdict(report), default=str, indent=2)) return _banner() verdict = {True: "AI-generated", False: "not AI", None: "unknown"}[report.is_ai_generated] console.print(f"\n Verdict: {verdict} (confidence: {report.confidence})") console.print(f" Platform: {report.platform or 'undetermined'}") if report.is_ai_generated is None: console.print( " No locally-readable AI signal found. This is not the same as 'clean': " "metadata is often stripped by re-encoding, screenshots, or upload, and SynthID-class " "pixel watermarks (Gemini / Nano Banana / gpt-image) have no local detector. " "See caveats below." ) if report.integrity_clashes: console.print("\n Warning: Integrity clash (provenance signals contradict each other)") for clash in report.integrity_clashes: console.print(f" - {clash}") if report.watermarks: table = Table(show_header=True, header_style="bold", title="Watermarks / provenance markers") table.add_column("Marker", style="cyan") for wm in report.watermarks: table.add_row(wm) console.print(table) else: console.print(" No watermarks or provenance markers found.") if report.caveats: console.print("\n Caveats:") for c in report.caveats: console.print(f" - {c}") # -- Combined "all" mode ---------------------------------------------- @main.command("all") @click.argument("source", type=click.Path(exists=True, path_type=Path)) @click.option( "-o", "--output", type=click.Path(path_type=Path), default=None, help="Output path (default: _clean.)." ) @click.option("--inpaint/--no-inpaint", default=True, help="Apply inpainting cleanup after visible removal.") @click.option( "--inpaint-method", type=click.Choice(["ns", "telea", "gaussian"]), default="ns", help="Inpainting method." ) @_strength_option @click.option("--steps", type=int, default=50, help="Number of denoising steps for invisible removal.") @_pipeline_option @_model_option @click.option( "--device", type=click.Choice(["auto", "cpu", "mps", "cuda", "xpu"]), default="auto", help="Inference device.", ) @click.option("--seed", type=int, default=None, help="Random seed for reproducibility.") @click.option("--hf-token", type=str, default=None, help="HuggingFace API token.") @click.option( "--humanize", type=float, default=0.0, help="Analog Humanizer film grain intensity (0 = off, typical: 2.0-6.0)." ) @click.option( "--max-resolution", type=int, default=0, help="Cap long side (px) before diffusion; 0 = native (best quality, like raiw.cc). Raise only on GPU/MPS OOM.", ) @_controlnet_scale_option @_min_resolution_option @_unsharp_option @_upscaler_option @_guidance_scale_option @_auto_option @_adaptive_polish_option @click.pass_context def cmd_all( ctx: click.Context, source: Path, output: Path | None, inpaint: bool, inpaint_method: Literal["ns", "telea", "gaussian"], strength: float | None, steps: int, pipeline: str, model: str | None, device: str, seed: int | None, hf_token: str | None, humanize: float, unsharp: float, max_resolution: int, min_resolution: int, controlnet_scale: float, upscaler: str, guidance_scale: float | None, auto: bool, adaptive_polish: bool, ) -> None: """Remove ALL watermarks: visible + invisible + metadata. Runs the full pipeline in order: 1. Visible watermark removal (Gemini sparkle, reverse alpha blending) 2. Invisible watermark removal (SynthID etc., diffusion regeneration) 3. AI metadata stripping (EXIF, PNG text, C2PA) If invisible watermark deps are not installed, skips step 2 with a warning. """ _banner() source = _validate_image(source) _warn_if_esrgan_unavailable(upscaler) adaptive_polish = _resolve_auto_polish(auto, adaptive_polish) if output is None: output = source.with_stem(source.stem + "_clean") t0 = time.monotonic() # Tracks whether step 2 (invisible / SynthID removal) was skipped because the # GPU extra is missing. A skipped step 2 still produces an output file (visible # mark + metadata stripped), so without a loud end-of-run notice + non-zero exit # the user mistakes it for a clean result and ships an image that still carries # the invisible watermark (recurring reports: #14, #47). synthid_skipped = False # Use a temp file for intermediate results so the user doesn't see # a partial output file during long model downloads. import tempfile tmp_fd, tmp_path_str = tempfile.mkstemp(suffix=source.suffix) tmp_path = Path(tmp_path_str) try: import os os.close(tmp_fd) # -- Step 1: Visible watermark -------------------------------- console.print("\n 1) Visible watermark removal") image, alpha = _read_bgr_and_alpha(source) if image is None: console.print(f"Error: Failed to read image: {source}") raise SystemExit(1) h, w = image.shape[:2] 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) if removed_label is not None: console.print(f" Visible watermark removed ({removed_label})") else: console.print(" Skipped (no visible watermark detected)") # Save to temp file for invisible engine input (preserve alpha if present) _write_bgr_with_alpha(tmp_path, result, alpha) # -- Step 2: Invisible watermark ------------------------------ console.print("\n 2) Invisible watermark removal") from remove_ai_watermarks.invisible_engine import is_available as invisible_available if not invisible_available(): synthid_skipped = True console.print( " Warning: Skipped - GPU dependencies not installed.\n" " Install them with: pip install 'remove-ai-watermarks[gpu]'" ) else: from remove_ai_watermarks.invisible_engine import InvisibleEngine device_str = None if device == "auto" else device def progress_cb(msg: str) -> None: console.print(f" {msg}") inv_engine = InvisibleEngine( model_id=model, device=device_str, pipeline=pipeline, hf_token=hf_token, progress_callback=progress_cb, controlnet_conditioning_scale=controlnet_scale, ) # Detect the vendor from the pristine ORIGINAL (`source`); `tmp_path` has # already lost its C2PA to the visible-removal pass, so reading it would # always resolve to the unknown-vendor default. vendor = vendor_for_strength(source) console.print(f" Strength: {resolve_strength(strength, vendor)} Steps: {steps}") inv_engine.remove_watermark( image_path=tmp_path, output_path=tmp_path, strength=strength, num_inference_steps=steps, guidance_scale=guidance_scale, seed=seed, humanize=humanize, unsharp=unsharp, adaptive_polish=adaptive_polish, max_resolution=max_resolution, min_resolution=min_resolution, upscaler=upscaler, vendor=vendor, ) console.print(" Invisible watermark removed") # -- Step 3: Metadata ----------------------------------------- console.print("\n 3) AI metadata stripping") try: from remove_ai_watermarks.metadata import remove_ai_metadata remove_ai_metadata(tmp_path, tmp_path) console.print(" AI metadata stripped") except Exception as e: console.print(f" Warning: Metadata strip failed: {e}") # -- Write final result ---------------------------------------- # The invisible step (and downstream cv2.IMREAD_COLOR paths) drops alpha, # so re-attach the original alpha plane unchanged when writing the final # output for transparent formats. output.parent.mkdir(parents=True, exist_ok=True) final_bgr, _ = _read_bgr_and_alpha(tmp_path) if final_bgr is None: console.print(f"Error: Failed to read intermediate file: {tmp_path}") raise SystemExit(1) _write_bgr_with_alpha(output, final_bgr, alpha) finally: # Clean up temp file if it still exists if tmp_path.exists(): tmp_path.unlink() # -- Done ----------------------------------------------------- elapsed = time.monotonic() - t0 size_kb = output.stat().st_size / 1024 console.print(f"\n Done: {output} ({size_kb:.0f} KB, {elapsed:.1f}s total)") # A skipped invisible step is the single most common "it didn't work" report: # the output looks processed but still carries the SynthID watermark. Make that # impossible to miss -- a prominent banner plus a non-zero exit so scripts and # batch callers can detect the incomplete run instead of trusting the file. if synthid_skipped: console.print( "\n =====================================================================\n" " WARNING: the invisible (SynthID) watermark was NOT removed.\n" " Step 2 was skipped because the GPU dependencies are not installed,\n" " so this output still carries the invisible watermark -- only the\n" " visible mark and metadata were stripped.\n" "\n" " Install the extra and rerun to remove it:\n" " pip install 'remove-ai-watermarks[gpu]'\n" " =====================================================================" ) raise SystemExit(1) # -- Batch command ---------------------------------------------------- def _process_batch_image( ctx: click.Context, img_path: Path, out_path: Path, mode: str, inpaint: bool, strength: float | None, steps: int, pipeline: str, device: str, seed: int | None, hf_token: str | None, humanize: float, unsharp: float = 0.0, max_resolution: int = 0, min_resolution: int = 1024, controlnet_scale: float = 1.0, upscaler: str = "lanczos", model: str | None = None, guidance_scale: float | None = None, adaptive_polish: bool = False, ) -> None: """Process a single image for batch mode. Applies the requested watermark removal steps (visible, invisible, metadata) to *img_path* and writes the result to *out_path*. Raises: ValueError: If the image cannot be opened. """ saved_alpha: NDArray[Any] | None = None if mode in ("visible", "all"): # Always read the ORIGINAL source: the visible pass is the first step, so a # stale out_path from a previous run must not be re-processed as if it were # the input. (The invisible step below reads out_path for `all` -- that chain # is within a single run.) image, alpha = _read_bgr_and_alpha(img_path) if image is None: raise ValueError("Failed to read image") result, _ = _remove_visible_auto(image, inpaint=inpaint) _write_bgr_with_alpha(out_path, result, alpha) saved_alpha = alpha if mode in ("invisible", "all"): from remove_ai_watermarks.invisible_engine import ( is_available as invisible_available, ) if invisible_available(): from remove_ai_watermarks.invisible_engine import InvisibleEngine # Cache the engine in ctx.obj so the batch builds it once (pipeline is a # single CLI value, constant across the run). engines = ctx.obj.setdefault("_inv_engines", {}) if pipeline not in engines: engines[pipeline] = InvisibleEngine( model_id=model, device=None if device == "auto" else device, pipeline=pipeline, hf_token=hf_token, controlnet_conditioning_scale=controlnet_scale, ) engine_inv = engines[pipeline] engine_inv.remove_watermark( img_path if mode == "invisible" else out_path, out_path, strength=strength, num_inference_steps=steps, guidance_scale=guidance_scale, seed=seed, humanize=humanize, unsharp=unsharp, adaptive_polish=adaptive_polish, max_resolution=max_resolution, min_resolution=min_resolution, upscaler=upscaler, # 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), ) if mode in ("metadata", "all"): from remove_ai_watermarks.metadata import remove_ai_metadata remove_ai_metadata(img_path if mode == "metadata" else out_path, out_path) # In "all" mode, the invisible step (color-only OpenCV paths) drops alpha, # so re-attach the cached alpha when the input had transparency. if mode == "all" and saved_alpha is not None: final_bgr, _ = _read_bgr_and_alpha(out_path) if final_bgr is not None: _write_bgr_with_alpha(out_path, final_bgr, saved_alpha) @main.command("batch") @click.argument("directory", type=click.Path(exists=True, file_okay=False, path_type=Path)) @click.option( "-o", "--output-dir", type=click.Path(path_type=Path), default=None, help="Output directory (default: _clean/).", ) @click.option( "--mode", type=click.Choice(["visible", "invisible", "metadata", "all"]), default="visible", help="Processing mode." ) @_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).") @click.option( "--humanize", type=float, default=0.0, help="Analog Humanizer film grain intensity (0 = off, typical: 2.0-6.0)." ) @_pipeline_option @click.option( "--device", type=click.Choice(["auto", "cpu", "mps", "cuda", "xpu"]), default="auto", help="Inference device.", ) @click.option("--seed", type=int, default=None, help="Random seed for reproducibility.") @click.option("--hf-token", type=str, default=None, help="HuggingFace API token.") @click.option( "--max-resolution", type=int, default=0, help="Cap long side (px) before diffusion; 0 = native (best quality, like raiw.cc). Raise only on GPU/MPS OOM.", ) @_min_resolution_option @_unsharp_option @_upscaler_option @_controlnet_scale_option @_model_option @_guidance_scale_option @_auto_option @_adaptive_polish_option @click.pass_context def cmd_batch( ctx: click.Context, directory: Path, mode: str, output_dir: Path | None, strength: float | None, steps: int, pipeline: str, device: str, seed: int | None, hf_token: str | None, inpaint: bool, humanize: float, unsharp: float, max_resolution: int, min_resolution: int, controlnet_scale: float, upscaler: str, model: str | None, guidance_scale: float | None, auto: bool, adaptive_polish: bool, ) -> None: """Process all images in a directory.""" _banner() if output_dir is None: output_dir = directory.parent / (directory.name + "_clean") output_dir.mkdir(parents=True, exist_ok=True) images = sorted(p for p in directory.iterdir() if p.suffix.lower() in SUPPORTED_FORMATS) if not images: console.print(f"No supported images found in {directory}") return console.print(f" Found {len(images)} images in {directory}") console.print(f" Output -> {output_dir}") console.print(f" Mode: {mode}") if mode in ("invisible", "all"): _warn_if_esrgan_unavailable(upscaler) adaptive_polish = _resolve_auto_polish(auto, adaptive_polish) processed = 0 errors = 0 with Progress( SpinnerColumn(), TextColumn("[progress.description]{task.description}"), BarColumn(), TextColumn("[progress.percentage]{task.percentage:>3.0f}%"), TimeElapsedColumn(), console=console, ) as progress: task = progress.add_task("Processing...", total=len(images)) for img_path in images: out_path = output_dir / img_path.name progress.update(task, description=f"{img_path.name}") try: _process_batch_image( ctx=ctx, img_path=img_path, out_path=out_path, mode=mode, inpaint=inpaint, strength=strength, steps=steps, pipeline=pipeline, device=device, seed=seed, hf_token=hf_token, humanize=humanize, unsharp=unsharp, max_resolution=max_resolution, min_resolution=min_resolution, controlnet_scale=controlnet_scale, upscaler=upscaler, model=model, guidance_scale=guidance_scale, adaptive_polish=adaptive_polish, ) processed += 1 except Exception as e: errors += 1 if ctx.obj.get("verbose"): console.print(f" {img_path.name}: {e}") progress.advance(task) console.print(f"\n {processed} processed" + (f" {errors} errors" if errors else "")) if __name__ == "__main__": main()