mirror of
https://github.com/wiltodelta/remove-ai-watermarks.git
synced 2026-08-09 23:50:40 +02:00
Victor KuznetsovandClaude Opus 5
b0ca2054f6
qwen-zimage becomes the default and sdxl-zimage the only alternative. The
controlnet, sdxl, qwen and default profiles are gone, and with them the CPU and
MPS paths for invisible-watermark removal: neither matched the two-stage
recipe's face preservation, so keeping them advertised a quality this library no
longer delivers. Visible-mark removal and every identify command still run
anywhere.
Retired names are rejected rather than remapped. Silently routing --pipeline
sdxl onward would run an old script at a different strength, on a different
model, at a different quality, and report success.
CUDA is now checked when the remover is constructed instead of when the model
loads. Auto-detection cheerfully returned mps on a Mac, so the failure arrived
several layers down, after the dependency check and the pipeline import, in a
message naming whichever internal pipeline happened to raise. _DEVICES collapses
to {"cuda"} and the cpu/mps float32 branch goes with it.
resolve_strength stays total. It briefly returned None for qwen-zimage, meaning
"ask the resolution curve", which pushed a branch onto both callers and left one
of the two strength policies outside the strength module; the CLI copy had
already grown an `or 0.0` guarding a path its own comment called unreachable. It
now takes the image size and answers for both profiles, so the displayed value
cannot drift from the executed one.
Deletion fallout removed with it: img2img_runner and progress.py (the MPS
recovery path and its progress monitor had no callers left), viable_steps, the
fp16 degenerate-output retry, the fp16 VAE fix, and the Qwen img2img call
builders. try_empty_device_cache moved into watermark_remover rather than
leaving a module whose docstring outlived its code. _HAS_DIFFUSERS routes
through optional_deps.module_available, which is what the rest of the library
uses and what correctly rejects a pruned namespace remnant.
--steps, --guidance-scale and --model now have exactly one legal value each and
are still accepted at parse time, then rejected in remove(). Their help text
says so, but validating them beside the option would be better.
Not addressed, and worth its own decision: invisible_engine forces
min_resolution to 0 for both profiles, so the --min-resolution floor, --upscaler,
_esrgan_upscale, upscaler.py and the esrgan extra are all unreachable.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
265 lines
9.9 KiB
TOML
265 lines
9.9 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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"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[diffusion]'
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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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# 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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# 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).
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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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"remove-ai-watermarks[pixels]",
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"spandrel>=0.3.0",
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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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all = ["remove-ai-watermarks[video,heif,detect,trustmark,diffusion,qwen-zimage,lama,migan,esrgan]"]
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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 ``diffusion`` 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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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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