chore: mark controlnet pipeline + GFPGAN restore-faces as experimental

Both content-preservation features are now flagged EXPERIMENTAL and opt-in.
--pipeline controlnet was already opt-in (default=default); --restore-faces
flips from on-by-default to OFF by default, matching the repo's prior pattern
for experimental preservation passes (the removed protect_text/protect_faces).

- cli.py: --restore-faces/--no-restore-faces default False; EXPERIMENTAL in the
  --restore-faces / --controlnet-scale / --pipeline help; batch default False.
- invisible_engine.py: remove_watermark restore_faces default False + docstring.
- CLAUDE.md / README.md / docs/synthid.md: label both experimental/opt-in.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

src/remove_ai_watermarks/cli.py

@@ -143,7 +143,8 @@ _controlnet_scale_option = click.option(
     "--controlnet-scale",
     type=float,
     default=1.0,
-    help="ControlNet conditioning scale (structure/text preservation strength), controlnet pipeline only.",
+    help="ControlNet conditioning scale (structure/text preservation strength), controlnet pipeline "
+    "only (EXPERIMENTAL).",
 )
 
 
@@ -151,10 +152,10 @@ def _restore_faces_options(f: Any) -> Any:
     """Attach the shared GFPGAN face-restoration flags to an invisible-pipeline command."""
     restore_flag = click.option(
         "--restore-faces/--no-restore-faces",
-        default=True,
-        help="Restore face identity with a GFPGAN post-pass when faces are present "
-        "(needs the 'restore' extra); on by default, auto-skips when no face is detected "
-        "or the extra is absent.",
+        default=False,
+        help="EXPERIMENTAL, opt-in. Restore face identity with a GFPGAN post-pass when "
+        "faces are present (needs the 'restore' extra); off by default, auto-skips when no "
+        "face is detected or the extra is absent.",
     )
     weight_flag = click.option(
         "--restore-faces-weight",
@@ -471,7 +472,7 @@ def cmd_erase(
     type=click.Choice(["default", "controlnet"]),
     default="default",
     help="Pipeline profile (default=SDXL img2img; controlnet=SDXL + canny ControlNet that preserves "
-    "text/faces via edge conditioning while removing SynthID).",
+    "text/faces via edge conditioning while removing SynthID, EXPERIMENTAL).",
 )
 @click.option(
     "--device",
@@ -715,7 +716,7 @@ def cmd_identify(ctx: click.Context, source: Path, no_visible: bool, as_json: bo
     type=click.Choice(["default", "controlnet"]),
     default="default",
     help="Pipeline profile (default=SDXL img2img; controlnet=SDXL + canny ControlNet that preserves "
-    "text/faces via edge conditioning while removing SynthID).",
+    "text/faces via edge conditioning while removing SynthID, EXPERIMENTAL).",
 )
 @click.option("--model", type=str, default=None, help="HuggingFace model ID for invisible removal.")
 @click.option(
@@ -907,7 +908,7 @@ def _process_batch_image(
     hf_token: str | None,
     humanize: float,
     max_resolution: int = 0,
-    restore_faces: bool = True,
+    restore_faces: bool = False,
     restore_faces_weight: float = 0.5,
 ) -> None:
     """Process a single image for batch mode.
@@ -1012,7 +1013,7 @@ def _process_batch_image(
     type=click.Choice(["default", "controlnet"]),
     default="default",
     help="Pipeline profile (default=SDXL img2img; controlnet=SDXL + canny ControlNet that preserves "
-    "text/faces via edge conditioning while removing SynthID).",
+    "text/faces via edge conditioning while removing SynthID, EXPERIMENTAL).",
 )
 @click.option(
     "--device",

src/remove_ai_watermarks/invisible_engine.py

@@ -126,7 +126,7 @@ class InvisibleEngine:
         humanize: float = 0.0,
         max_resolution: int = 0,
         vendor: str | None = None,
-        restore_faces: bool = True,
+        restore_faces: bool = False,
         restore_faces_weight: float = 0.5,
     ) -> Path:
         """Remove invisible watermark from an image.
@@ -140,9 +140,10 @@ class InvisibleEngine:
             guidance_scale: Classifier-free guidance scale.
             seed: Random seed for reproducibility.
             humanize: Intensity of Analog Humanizer film grain (0 = off).
-            restore_faces: Run the optional GFPGAN face-restoration post-pass when
-                faces are present (needs the ``restore`` extra). Auto-skips with a
-                debug log when the extra is absent or no face is detected.
+            restore_faces: EXPERIMENTAL, opt-in (default False). Run the GFPGAN
+                face-restoration post-pass when faces are present (needs the
+                ``restore`` extra). Auto-skips with a debug log when the extra is
+                absent or no face is detected.
             restore_faces_weight: GFPGAN fidelity weight (0-1); lower = more GAN
                 regeneration (cleaner watermark scrub), higher = closer to input.
             max_resolution: Cap the long side (px) before diffusion. 0 (default)