mirror of
https://github.com/wiltodelta/remove-ai-watermarks.git
synced 2026-08-10 08:00:32 +02:00
Victor KuznetsovandClaude Opus 5
52b2c115e8
The CLI still advertised --model, --steps, --guidance-scale, --device and a deprecated --auto. Each pinned a value the two surviving profiles fix -- the model stack, the per-stage distilled schedule, CFG 1.0, CUDA -- so the only outcome any of them had was an error raised several frames below the caller, under a message naming an internal profile. A flag whose sole result is a refusal is worse than no flag: it advertises a capability that does not exist, and it lets a wrapper thread a value that will silently do nothing. They are gone from the parser, from InvisibleEngine, and from WatermarkRemover, so the failure is now a TypeError or a Click "No such option" at the point the caller can act on. The install hint was wrong in the same way. is_available() checked torch and diffusers, then told the user to install [diffusion] -- which contains neither DiffSynth nor the Z-Image face stage both profiles run. Following the advice produced a second, different failure. The module list and the extra name now live once in watermark_profiles (REMOVAL_MODULES, INVISIBLE_EXTRA) and are read by both the CLI gate and the remover's precondition, which cannot drift apart because they are the same tuple. The adaptive-polish default moved out of the argument parser. It was resolved by reading Click's parameter source, which put per-profile data in the CLI layer, left the engine declaring the opposite default (False vs True) so a library caller and a CLI caller on one profile got different output, and lost the polish entirely for anything that supplies the flag non-interactively. The flag is now tri-state (default=None) and resolve_adaptive_polish owns the per-profile answer. The seed follows the same rule: the CLI stopped pre-resolving it. Dead code removed with it: six scan_*_video wrappers and the _scan_video helper none of them had a caller for, PNG_METADATA_KEYS, feather_region_composite and the remover region path that was only reachable from a no-caller convenience wrapper, remove_watermark_batch on both layers, try_empty_device_cache, the _generate/_run_qwen_zimage pass-through pair, self.model_id, and the _internal PEP 562 shim that no caller ever went through. get_device now answers cuda or cpu only: mps and xpu travelled one frame to the same CUDA-only refusal while costing a device probe each, and that refusal now names the resolved device, so device=None on a CUDA-less host says 'cpu' rather than 'None'. The XPU wheel index went with them. Docs: README, cli, installation, python-api, supported-signals, known-limitations and module-internals all still described the removed profiles, the CPU/MPS/XPU ladder, a `default`->`sdxl` alias, and the wrong extra. known-limitations still listed the retired SDXL strength ladder as current. scripts/smoke_matrix.py and real_examples_e2e.py drove --device mps. Next release is 0.25.0, not a patch: this removes public parameters and narrows a published extra on top of the released 0.24.0. pre-commit: 1) maintain.sh - exit 0 (1091 tests, Pyright 0 errors, no vulnerabilities); 2) /simplify - 4 agents, 11 findings applied, 2 skipped (dropping the `device` parameter entirely, which raiw-app pins; folding diffsynth into the `diffusion` extra, which video-only callers do not need); 3) docs sync - grepped every removed identifier across README, docs/, scripts/, .claude/; updated 9 docs; 4) CLAUDE.md - added the no-error-only-knobs rule to .claude/rules/development.md Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
240 lines
8.6 KiB
TOML
240 lines
8.6 KiB
TOML
[project]
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name = "remove-ai-watermarks"
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version = "0.24.0"
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description = "AI watermark remover for visible, invisible, and provenance marks in images and video"
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readme = "README.md"
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requires-python = ">=3.10.1"
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license = {text = "Apache-2.0"}
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keywords = [
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"ai-watermark",
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"ai-watermark-remover",
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"watermark-remover",
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"watermark-removal",
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"remove-watermark",
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"synthid",
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"c2pa",
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"content-credentials",
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"nano-banana",
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"gemini",
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"gemini-watermark",
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"ai-metadata",
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"metadata-removal",
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"provenance",
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"exif",
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"stable-diffusion",
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"comfyui",
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"ai-detection",
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]
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classifiers = [
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"Development Status :: 4 - Beta",
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"Environment :: Console",
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"Intended Audience :: Developers",
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"Intended Audience :: End Users/Desktop",
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"License :: OSI Approved :: Apache Software License",
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"Operating System :: OS Independent",
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"Programming Language :: Python :: 3 :: Only",
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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 :: Multimedia :: Graphics :: Graphics Conversion",
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"Topic :: Scientific/Engineering :: Artificial Intelligence",
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"Topic :: Scientific/Engineering :: Image Processing",
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"Topic :: Security",
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"Topic :: Utilities",
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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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"click>=8.0.0",
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"python-dotenv>=1.0.0",
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# Official C2PA reader (Content Authenticity Initiative, MIT/Apache-2.0). The
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# primary, spec-tracking manifest parser for the identify/metadata path; the
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# hand-rolled caBX/CBOR scanner in _internal/c2pa.py is kept only as a fallback for
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# synthetic/partial blobs the validator rejects. Binary wheel (Rust), but the
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# import is light (no torch/numpy) so it fits the dependency-light identify
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# host. Prebuilt wheels cover the full CI matrix (linux/macos/windows).
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"c2pa-python>=0.35.0",
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]
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[project.optional-dependencies]
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pixels = [
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"numpy>=1.24.0",
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"opencv-python-headless>=4.8.0",
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]
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# Optional HEIC/AVIF pixel decode. Metadata scanning handles these containers
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# without this plugin; combine `heif` with any pixel feature only when needed.
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heif = [
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"pillow-heif>=0.13.0",
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]
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visible = ["remove-ai-watermarks[pixels]"]
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# Video visible removal uses the shared pixel runtime. PyAV packetizes processed
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# VFR frames with explicit PTS before system ffmpeg encodes them. PyAV 18 requires
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# Python 3.11; the 16.x wheel line still covers Python 3.10.
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video = [
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"remove-ai-watermarks[visible]",
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"av>=16,<17; python_version < '3.11'",
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"av>=18,<19; python_version >= '3.11'",
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]
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# Open DWT-DCT watermarks used by Stable Diffusion / SDXL / FLUX. The in-tree
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# decoder avoids the upstream invisible-watermark package's mandatory torch and
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# non-headless OpenCV dependencies.
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detect = [
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"remove-ai-watermarks[pixels]",
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"PyWavelets>=1.1.1",
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]
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diffusion = [
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"remove-ai-watermarks[pixels]",
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# A CUDA-enabled torch build is required: invisible-watermark removal has no
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# CPU, MPS or XPU path. The default PyPI wheel carries CUDA on Linux/Windows;
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# on macOS there is no CUDA build and this extra installs only for the
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# non-diffusion imports it shares.
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"torch>=2.0.0",
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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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# Full two-stage high-fidelity profile: Qwen-Image-2512 Lightning + DiffSynth
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# Canny ControlNet for the frame, then SAM-masked Z-Image Turbo face repair.
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# CUDA-only and intentionally separate from the normal diffusion extra because the
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# additional model stack and DiffSynth runtime are large.
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qwen-zimage = [
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"remove-ai-watermarks[diffusion]",
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"diffsynth>=2.0.17,<3",
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"torchvision>=0.20.0",
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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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"remove-ai-watermarks[visible]",
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# ONNX Runtime 1.24 dropped CPython 3.10 wheels; keep the project's
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# supported 3.10 line on the last compatible release series.
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"onnxruntime>=1.16.0,<1.24; python_version < '3.11'",
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"onnxruntime>=1.16.0; python_version >= '3.11'",
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"huggingface-hub>=0.20.0",
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]
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# Lightweight inpaint backend -- MI-GAN via onnxruntime (andraniksargsyan/migan,
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# MIT). CPU, no torch. Model (~28 MB) downloaded on first use and cached by
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# huggingface_hub; never bundled. ~700-950 MB peak RAM / ~0.19 s/call -- the
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# memory-tight learned tier (vs big-LaMa's ~4.7 GB). Select it explicitly when
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# LaMa, the quality-first `auto` choice, is too large. Same runtime as `lama`.
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migan = [
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"remove-ai-watermarks[visible]",
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"onnxruntime>=1.16.0,<1.24; python_version < '3.11'",
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"onnxruntime>=1.16.0; python_version >= '3.11'",
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"huggingface-hub>=0.20.0",
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]
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dev = [
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"remove-ai-watermarks[video]",
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"remove-ai-watermarks[detect]",
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"pytest>=8.0.0",
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"pytest-cov>=4.1.0",
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"pytest-xdist>=3.5.0",
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"packaging>=24.0",
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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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# maintain.sh helpers; they only support newer Pythons, so gate them by
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# marker to keep the py3.10 resolution (and CI matrix) solvable.
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"uv-outdated>=0.1.0; python_version >= '3.12'",
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"uv-secure>=0.12.0; python_version >= '3.12'",
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]
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# ``qwen-zimage`` already pulls ``diffusion``; naming both would suggest diffusion is
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# independently sufficient for a removal, which it is not.
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all = ["remove-ai-watermarks[video,heif,detect,trustmark,qwen-zimage,lama,migan]"]
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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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requires = ["hatchling"]
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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 and public-safe: ship tracked source and
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# metadata, not corpora or local research/session artifacts. The wheel ships
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# only src/.
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include = [
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"/src",
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"/LICENSE",
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"/README.md",
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"/pyproject.toml",
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]
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exclude = [
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"/data",
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"/tmp",
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"/.sc",
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]
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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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extend-exclude = ["*.md"]
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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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"scripts/*.py" = ["G004", "S108", "S310", "T20"]
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"tests/*.py" = ["ANN", "S101", "S105", "S106", "S108"]
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"src/remove_ai_watermarks/_internal/watermark_remover.py" = ["S603", "S606", "S607"] # nvidia-smi capability probe
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"src/remove_ai_watermarks/_internal/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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