mirror of
https://github.com/wiltodelta/remove-ai-watermarks.git
synced 2026-06-10 12:53:56 +02:00
Victor Kuznetsov
6d11c11b52
Three content-quality features for the invisible/all/batch pipeline.
DBNet text detector (auto_config): replace the MSER text heuristic with
PP-OCRv3 differentiable-binarization via cv2.dnn.TextDetectionModel_DB,
using a bundled 2.4 MB Apache-2.0 model (en/cn detection nets are
byte-identical, so it ships language-neutral). cv2.dnn is core OpenCV, so
no new pip dep. MSER stays as the fallback when the model can't load.
Validated on real images: matches MSER everywhere and additionally catches
the Doubao CJK mark MSER missed; routing decisions unchanged otherwise.
Real-ESRGAN upscaler (new upscaler.py, esrgan extra): optional
pre-diffusion super-resolution for the min-resolution floor upscale, loaded
via spandrel (MIT, no basicsr) with BSD-3-Clause weights downloaded on
first use. New --upscaler {lanczos,esrgan} on invisible/all/batch; default
stays lanczos and the engine falls back to lanczos when the extra is absent
or the model errors (never breaks removal). It is a manual opt-in knob (the
auto plan never selects it) -- as a generic GAN it sharpens photo/texture
content strongly but can degrade faces (the diffusion pass regenerates
them) and thin text, documented accordingly.
batch --auto: wire the content-adaptive --auto (+ --adaptive-polish) into
cmd_batch. The plan is recomputed per image and the invisible engine is
cached per resolved pipeline (default/controlnet), so a mixed directory
builds at most one engine of each kind. Verified end-to-end: 3 mixed
images routed correctly with only 2 pipeline loads (controlnet reused).
ruff + strict pyright(src/) clean; 558 tests pass.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
214 lines
9.2 KiB
TOML
214 lines
9.2 KiB
TOML
[project]
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name = "remove-ai-watermarks"
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version = "0.8.9"
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description = "Remove visible and invisible AI watermarks from images (Gemini / Nano Banana, ChatGPT, Stable Diffusion)"
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readme = "README.md"
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requires-python = ">=3.10"
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license = {text = "MIT"}
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classifiers = [
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"License :: OSI Approved :: MIT License",
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"Operating System :: OS Independent",
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"Programming Language :: Python :: 3.10",
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"Programming Language :: Python :: 3.11",
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"Programming Language :: Python :: 3.12",
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"Programming Language :: Python :: 3.13",
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"Topic :: Multimedia :: Graphics",
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"Topic :: Scientific/Engineering :: Image Processing",
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]
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dependencies = [
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"pillow>=10.0.0",
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"piexif>=1.1.3",
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"numpy>=1.24.0",
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"opencv-python-headless>=4.8.0",
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"click>=8.0.0",
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"python-dotenv>=1.0.0",
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]
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[project.optional-dependencies]
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gpu = [
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"torch>=2.0.0",
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# The default PyPI torch wheel is a CPU/CUDA build. To drive an Intel GPU
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# (Arc / Data Center) via ``--device xpu`` you need an XPU-enabled torch
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# from PyTorch's XPU wheel index (Linux/Windows only -- there is no macOS
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# XPU build). Install that build first, then this extra (torch is then
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# already satisfied and won't be re-pulled):
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# pip install torch --index-url https://download.pytorch.org/whl/xpu
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# pip install 'remove-ai-watermarks[gpu]'
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# uv users can target the ``pytorch-xpu`` index declared under [tool.uv]:
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# uv pip install torch --index-url https://download.pytorch.org/whl/xpu
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"diffusers>=0.38.0",
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# diffusers 0.38's auto-pipeline registry imports ``Qwen3VLForConditional
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# Generation`` (its ``nucleusmoe_image`` pipeline), which only exists in
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# transformers 5.x -- so ``from diffusers import AutoPipelineForImage2Image``
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# fails on transformers 4.x. The real SDXL-loading break was NOT transformers
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# 5.x but the tokenizers *release candidate* (0.23.0rc0) that the global
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# ``prerelease = "allow"`` drags in: its CLIP tokenizer raises
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# ``RobertaProcessing.__new__() got an unexpected keyword argument 'cls'``.
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# Cap tokenizers to the stable 0.22 line (transformers 5.x accepts
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# >=0.22,<=0.23.0) so the rc is excluded while SDXL still loads.
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"transformers>=5,<6",
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"tokenizers>=0.22,<0.23",
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"accelerate>=0.25.0",
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"safetensors",
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]
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# Open invisible-watermark (imwatermark) decoder for detecting the DWT-DCT
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# watermarks embedded by Stable Diffusion / SDXL / FLUX. Optional because it
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# pulls non-headless opencv AND torch (invisible-watermark declares torch a hard
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# dependency, and WatermarkDecoder eagerly imports rivaGan -> torch at import
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# time, so the dwtDct-only detect path still needs torch present even though it
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# never runs on GPU). So `detect` alone pulls torch -- no need to add `gpu` for
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# detection. identify() guards the import and skips the signal when absent.
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detect = [
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"invisible-watermark>=0.2.0",
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]
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# Adobe TrustMark decoder -- the open, keyless watermark behind Adobe Durable
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# Content Credentials (soft-binding alg ``com.adobe.trustmark.P``). Optional
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# because it pulls torch and downloads model weights on first use. identify()
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# guards the import and skips the TrustMark signal when absent.
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trustmark = [
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"trustmark>=0.8.0",
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]
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# Universal region eraser backend -- big-LaMa via onnxruntime (Carve/LaMa-ONNX,
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# Apache-2.0). CPU, no torch. Model (~200 MB) is downloaded on first use and
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# cached by huggingface_hub; it is never bundled in this repo. The default cv2
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# eraser backend needs none of this.
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lama = [
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"onnxruntime>=1.16.0",
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"huggingface-hub>=0.20.0",
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]
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# Optional GFPGAN face-restoration post-pass (commercial-safe Apache-2.0 GFPGAN +
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# MIT RetinaFace). Re-synthesizes each face from a StyleGAN2 prior after the
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# diffusion removal pass, so it restores identity while still scrubbing the pixel
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# watermark. The GFPGANv1.4 weights + RetinaFace detector download on first use;
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# they are never bundled. gfpgan/basicsr/facexlib are an OLD ecosystem and must
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# stay on numpy < 2.0 to match the pinned gpu diffusion stack -- scipy is capped
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# < 1.18 (>= 1.18 uses np.long, gone in numpy 1.24-1.26) and numba < 0.60 to keep
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# the whole env on one numpy 1.26 resolution (same trap class as the removed
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# faceid/insightface extra). Kept OUT of `all` (heavy + model download).
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restore = [
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"gfpgan>=1.3.8",
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"facexlib>=0.3.0",
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"basicsr>=1.4.2",
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"scipy<1.18",
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"numba<0.60",
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]
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# Optional pre-diffusion super-resolution for small inputs (Real-ESRGAN). Loaded via
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# spandrel (MIT) -- a pure model-loader with NO basicsr dependency (it pulls only
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# torch / torchvision / safetensors / numpy / einops), which sidesteps the
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# basicsr / torchvision.functional_tensor breakage that the `restore` extra fights.
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# The Real-ESRGAN weights (BSD-3-Clause) download on first use and are cached; they
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# are never bundled. CPU works but is slow on large inputs -- it is meant for the
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# pre-diffusion upscale of SMALL inputs (and the GPU worker). Guarded by
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# upscaler.is_available(); the default upscaler stays Lanczos (cv2, no deps). The
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# weights are fetched with torch.hub (bundled with spandrel's torch), so no extra
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# download dependency is needed.
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esrgan = [
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"spandrel>=0.3.0",
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]
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dev = [
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"pytest>=8.0.0",
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"pytest-cov>=4.1.0",
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"ruff>=0.4.0",
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"pyright>=1.1.0",
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"invisible-watermark>=0.2.0",
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]
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all = ["remove-ai-watermarks[gpu,detect,trustmark,lama,dev]"]
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# diffusers 0.38.0 (security fix for GHSA-98h9-4798-4q5v) declares a dependency
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# on safetensors>=0.8.0rc0 — a pre-release. Allow pre-releases globally so the
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# resolver can satisfy that. Drop once diffusers publishes a release with a
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# stable safetensors pin (or once safetensors 0.8.0 stable is out).
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[tool.uv]
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prerelease = "allow"
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# basicsr 1.4.2 (pulled by the `restore` GFPGAN extra) ships sdist-only and its
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# setup.py get_version() reads basicsr/version.py in a way that newer setuptools
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# (>= 69) breaks with ``KeyError: '__version__'`` under isolated PEP 517 builds.
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# Pin an old setuptools as its build dependency so the sdist builds; this is
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# scoped to basicsr and does not affect the rest of the resolution.
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[tool.uv.extra-build-dependencies]
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basicsr = ["setuptools<69"]
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# PyTorch Intel-GPU (XPU) wheel index. ``explicit = true`` keeps it inert for
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# the default CPU/CUDA install: uv consults it only when a torch install
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# explicitly targets it (see the ``gpu`` extra comment), so it does not alter
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# the locked CPU/CUDA resolution. Linux/Windows only -- no macOS XPU build.
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[[tool.uv.index]]
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name = "pytorch-xpu"
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url = "https://download.pytorch.org/whl/xpu"
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explicit = true
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[project.scripts]
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remove-ai-watermarks = "remove_ai_watermarks.cli:main"
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[project.urls]
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Repository = "https://github.com/wiltodelta/remove-ai-watermarks"
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[build-system]
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# Pin hatchling < 1.31. hatchling 1.30.0 made Metadata-Version 2.5 (PEP 794) the
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# default, which the twine bundled in pypa/gh-action-pypi-publish@release/v1 rejects
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# ("'2.5' is not a valid Metadata-Version"), failing the v0.8.3 PyPI upload
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# (2026-06-01) when unpinned requires = ["hatchling"] pulled 1.30.0. hatchling 1.30.1
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# reverted the default to 2.4 ("kept at 2.4 until more tools support 2.5"), and
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# 1.27-1.29 were always 2.4 -- so < 1.31 keeps `uv build` on a 2.4-emitting hatchling
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# (it resolves to the latest allowed, 1.30.1). The publish workflow now uses
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# `uv publish`, whose uploader accepts 2.5, so this pin is belt-and-suspenders, not
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# load-bearing: keeping it makes the first uv-publish release ship 2.4 metadata
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# (isolating the uploader swap from the metadata-version bump). Drop to
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# `requires = ["hatchling"]` once that release confirms the path.
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requires = ["hatchling<1.31"]
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build-backend = "hatchling.build"
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[tool.hatch.build.targets.wheel]
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packages = ["src/remove_ai_watermarks"]
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[tool.hatch.build.targets.sdist]
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# Keep the source distribution small: ship the package + metadata, not the
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# committed test corpora / calibration captures under data/ (tens of MB --
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# synthid_corpus images + the visible-mark captures), which pushed the 0.8.0
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# sdist past PyPI's per-project file-size limit (the wheel ships only src/).
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exclude = ["/data"]
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[tool.pytest.ini_options]
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testpaths = ["tests"]
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pythonpath = ["src"]
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addopts = "-v --tb=short"
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[tool.ruff]
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target-version = "py310"
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line-length = 120
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exclude = ["_refs"]
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[tool.ruff.lint]
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select = ["E", "F", "B", "I", "S", "UP", "SIM", "RET", "COM", "C4", "G", "PT", "PIE", "T20", "DTZ", "ICN", "TCH", "RUF", "ANN"]
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ignore = [
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"COM812", # missing trailing comma (conflicts with ruff formatter)
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"ANN401", # typing.Any — sometimes unavoidable with third-party libs
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]
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[tool.ruff.lint.per-file-ignores]
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"tests/*.py" = ["ANN", "S101", "S105", "S106", "S108"]
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"src/remove_ai_watermarks/noai/watermark_remover.py" = ["S603", "S606", "S607", "T201"] # subprocess calls for auto-install/CUDA fix
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"src/remove_ai_watermarks/noai/c2pa.py" = ["S110"] # try-except-pass for corrupt file handling
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[tool.ruff.format]
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quote-style = "double"
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indent-style = "space"
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[tool.pyright]
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pythonVersion = "3.10"
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typeCheckingMode = "strict"
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exclude = ["_refs"]
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[[tool.pyright.executionEnvironments]]
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root = "tests"
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extraPaths = ["."]
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reportAttributeAccessIssue = false
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reportOptionalSubscript = false
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reportOptionalMemberAccess = false
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reportArgumentType = false
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reportUnknownMemberType = false
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reportUnknownArgumentType = false
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reportUnknownVariableType = false
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reportMissingTypeArgument = false
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