Refresh dependencies and compatibility guards

.env.example

@@ -1,3 +1,3 @@
 # HuggingFace token (optional; only needed for gated/private models)
 # Get yours at: https://huggingface.co/settings/tokens
-HF_TOKEN=
+# HF_TOKEN=

pyproject.toml

@@ -133,7 +133,10 @@ trustmark = [
 # eraser backend needs none of this.
 lama = [
     "remove-ai-watermarks[visible]",
-    "onnxruntime>=1.16.0",
+    # ONNX Runtime 1.24 dropped CPython 3.10 wheels; keep the project's
+    # supported 3.10 line on the last compatible release series.
+    "onnxruntime>=1.16.0,<1.24; python_version < '3.11'",
+    "onnxruntime>=1.16.0; python_version >= '3.11'",
     "huggingface-hub>=0.20.0",
 ]
 # Lightweight inpaint backend -- MI-GAN via onnxruntime (andraniksargsyan/migan,
@@ -143,7 +146,8 @@ lama = [
 # LaMa, the quality-first `auto` choice, is too large. Same runtime as `lama`.
 migan = [
     "remove-ai-watermarks[visible]",
-    "onnxruntime>=1.16.0",
+    "onnxruntime>=1.16.0,<1.24; python_version < '3.11'",
+    "onnxruntime>=1.16.0; python_version >= '3.11'",
     "huggingface-hub>=0.20.0",
 ]
 # Optional pre-diffusion super-resolution for small inputs (Real-ESRGAN). Loaded via

src/remove_ai_watermarks/_text_mark_engine.py

@@ -156,7 +156,6 @@ class TextMarkLocation:
     y: int
     w: int
     h: int
-    is_fallback: bool = True  # geometry anchor (no template match) -> always True for now
 
     @property
     def bbox(self) -> tuple[int, int, int, int]:
@@ -512,7 +511,7 @@ class TextMarkEngine:
         y = min(margin_b, max(0, h - wm_h)) if c.corner == "tl" else max(0, h - margin_b - wm_h)
         wm_w = min(wm_w, w - x)
         wm_h = min(wm_h, h - y)
-        return TextMarkLocation(x=x, y=y, w=wm_w, h=wm_h, is_fallback=True)
+        return TextMarkLocation(x=x, y=y, w=wm_w, h=wm_h)
 
     # ── Mask ────────────────────────────────────────────────────────────
 

src/remove_ai_watermarks/cli.py

@@ -22,6 +22,7 @@ import click
 
 from remove_ai_watermarks import __version__, image_io, watermark_registry
 from remove_ai_watermarks._internal.constants import SUPPORTED_FORMATS
+from remove_ai_watermarks._internal.utils import is_supported_format
 from remove_ai_watermarks._internal.watermark_profiles import (
     resolve_seed,
     resolve_steps,
@@ -143,7 +144,7 @@ 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:
+    if not is_supported_format(path):
         console.print(f"Warning: {path.suffix} may not be supported (expected: {', '.join(SUPPORTED_FORMATS)})")
     return path
 
@@ -2092,7 +2093,7 @@ def cmd_batch(
         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)
+    images = sorted(p for p in directory.iterdir() if is_supported_format(p))
 
     if not images:
         console.print(f"No supported images found in {directory}")

src/remove_ai_watermarks/video_invisible.py

@@ -13,7 +13,6 @@ from __future__ import annotations
 import logging
 import math
 from dataclasses import dataclass
-from importlib.util import find_spec
 from typing import TYPE_CHECKING, Any
 
 import cv2
@@ -75,7 +74,9 @@ class VideoVaeRuntime:
 
 def is_available() -> bool:
     """Return whether the optional VAE runtime can be imported."""
-    return find_spec("torch") is not None and find_spec("diffusers") is not None
+    from remove_ai_watermarks.optional_deps import module_available
+
+    return module_available("torch", "diffusers")
 
 
 def _fit_size(width: int, height: int, long_side: int) -> tuple[int, int]:

tests/test_video_invisible.py

@@ -9,7 +9,7 @@ from typing import TYPE_CHECKING, cast
 
 import pytest
 
-from remove_ai_watermarks import video_encoding, video_invisible
+from remove_ai_watermarks import optional_deps, video_encoding, video_invisible
 from remove_ai_watermarks.video_synthid import DEFAULT_VIDEO_SYNTHID_NOISE_STD
 
 if TYPE_CHECKING:
@@ -68,9 +68,9 @@ def test_encoder_redirects_large_stderr_while_frames_are_streaming(
 
 def test_availability_requires_both_optional_packages(monkeypatch: pytest.MonkeyPatch) -> None:
     monkeypatch.setattr(
-        video_invisible,
-        "find_spec",
-        lambda name: object() if name == "torch" else None,
+        optional_deps,
+        "module_available",
+        lambda *names: set(names) <= {"torch"},
     )
 
     assert video_invisible.is_available() is False