mirror of
https://github.com/wiltodelta/remove-ai-watermarks.git
synced 2026-08-09 23:50:40 +02:00
Merge main into video watermark pipeline
This commit is contained in:
Victor Kuznetsov
+40
-32
@@ -1,7 +1,7 @@
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[project]
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name = "remove-ai-watermarks"
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version = "0.20.2"
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description = "Remove visible and invisible AI watermarks from images and provenance metadata from media containers"
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version = "0.22.0"
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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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@@ -46,15 +46,7 @@ classifiers = [
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]
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dependencies = [
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"pillow>=10.0.0",
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# HEIC/AVIF pixel decode for the removal path (iPhone photos, modern exports):
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# OpenCV cannot decode these containers, so image_io.imread falls back to Pillow
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# and pillow-heif (bundled libheif, prebuilt wheels) registers the HEIF+AVIF
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# openers. The metadata path already handles them via a plugin-free binary scan;
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# this closes the same gap for the pixel path so `visible`/`all` work on them.
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"pillow-heif>=0.13.0",
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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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# Official C2PA reader (Content Authenticity Initiative, MIT/Apache-2.0). The
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@@ -64,16 +56,36 @@ dependencies = [
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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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# Timestamped NUT bridge for processed video frames. Rawvideo pipes carry no
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# per-frame PTS, so this is what lets the existing system-ffmpeg encoder
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# preserve VFR timing without buffering a frame sequence on disk. PyAV 18
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# requires Python 3.11; the 16.x wheel line still covers Python 3.10.
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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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[project.optional-dependencies]
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gpu = [
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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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@@ -81,7 +93,7 @@ gpu = [
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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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# 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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@@ -101,23 +113,13 @@ gpu = [
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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 gpu extra because the
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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[gpu]",
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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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# 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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@@ -130,6 +132,7 @@ trustmark = [
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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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"onnxruntime>=1.16.0",
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"huggingface-hub>=0.20.0",
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]
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@@ -139,6 +142,7 @@ lama = [
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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",
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"huggingface-hub>=0.20.0",
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]
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@@ -152,12 +156,16 @@ migan = [
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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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@@ -166,11 +174,11 @@ dev = [
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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[gpu,detect,trustmark,lama,migan,dev]"]
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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 ``gpu`` extra comment), so it does not alter
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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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