fix: address whole-project code review (visible all/batch, engine consolidation, I/O)

Nine findings from a high-effort project-wide review, fixed and verified
(571 passed, ruff/pyright clean):

Correctness:
- all/batch now remove Doubao/Jimeng/Samsung visible text marks: the visible
  step routes through the registry (new cli._remove_visible_auto) instead of a
  hardcoded GeminiEngine, so they no longer leave the wordmark intact.
- batch always reads the original source (dropped the out_path-reuse that
  re-processed already-cleaned outputs on a re-run).
- img2img_runner only retries the diffusion call on the deprecated-callback
  TypeError; any other TypeError now propagates instead of double-running.
- gemini detect/remove and the reverse-alpha engines normalize channels via a
  new image_io.to_bgr, fixing a grayscale/BGRA crash in the FP-gate path.
- _png_late_metadata advances its cursor by the clamped length, so a malformed
  chunk length no longer aborts the late AI-label scan.

Cleanup / efficiency:
- Consolidate the ~90%-identical Doubao/Jimeng/Samsung engines into a shared
  config-driven _text_mark_engine.TextMarkEngine base; each engine is now a thin
  subclass (TextMarkConfig + test shims). Behavior is byte-exact (the three
  engine test suites pass unchanged). Registry adapters collapse to one
  _text_mark(...) row each. Gemini stays a separate engine.
- scan_head is memoized per (path, size, mtime), so identify() reads the file
  head once instead of ~8 times.
- invisible_engine post-processing decodes/encodes the output once (chained in
  memory) instead of 2-4 times across stages.
- Remove the orphaned get_model_id_for_profile (+ CONTROLNET_PROFILE); derive
  the --strength help from the strength constants (strength_default_help) so it
  cannot drift; share the --pipeline/--strength click options; simplify the
  retired --auto resolver.

Net -835 lines. Tests added for the registry-routed visible pass, to_bgr,
the polish/model/guidance wiring, and strength_default_help. CLAUDE.md updated
for the new base module, the engine/registry changes, image_io.to_bgr, and the
scan_head cache.

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

src/remove_ai_watermarks/gemini_engine.py

@@ -28,6 +28,8 @@ from typing import TYPE_CHECKING, Any, Literal
 import cv2
 import numpy as np
 
+from remove_ai_watermarks import image_io
+
 if TYPE_CHECKING:
     from collections.abc import Iterator
 
@@ -290,6 +292,11 @@ class GeminiEngine:
         if image is None or image.size == 0:
             return result
 
+        # Normalize to 3-channel BGR: the multi-scale search tolerates grayscale, but
+        # the FP-gate / alpha-gain helpers (_core_and_bg) reduce over axis=2 and would
+        # crash on a 2D/BGRA input reaching this public entry point (e.g. via the
+        # registry detect adapter or the library API).
+        image = image_io.to_bgr(image)
         h, w = image.shape[:2]
         base_size = force_size or get_watermark_size(w, h)
         result.size = base_size
@@ -481,17 +488,10 @@ class GeminiEngine:
             Cleaned BGR image as numpy array, or an unmodified copy when no
             watermark is detected.
         """
-        result = image.copy()
-
         # Normalize to 3-channel BGR up front: 2D grayscale (no channel axis) and
         # 4-channel BGRA both reach this public entry point and would otherwise
         # crash on the channel-count checks / downstream 3-channel math.
-        if result.ndim == 2:
-            result = cv2.cvtColor(result, cv2.COLOR_GRAY2BGR)
-        elif result.shape[2] == 4:
-            result = cv2.cvtColor(result, cv2.COLOR_BGRA2BGR)
-        elif result.shape[2] == 1:
-            result = cv2.cvtColor(result, cv2.COLOR_GRAY2BGR)
+        result = image_io.to_bgr(image.copy())
 
         size = force_size or get_watermark_size(result.shape[1], result.shape[0])
 
@@ -554,7 +554,9 @@ class GeminiEngine:
         Returns:
             Cleaned BGR image.
         """
-        result = image.copy()
+        # Same channel normalization as remove_watermark: the reverse-alpha blend
+        # assumes 3-channel BGR (a grayscale/BGRA input would mis-broadcast).
+        result = image_io.to_bgr(image.copy())
         x, y, rw, rh = region
 
         # Check standard sizes