mirror of
https://github.com/wiltodelta/remove-ai-watermarks.git
synced 2026-08-09 23:50:40 +02:00
Merge pull request #70 from wiltodelta/feat/video-watermarks
Complete product video watermark pipeline
This commit is contained in:
Victor Kuznetsov
@@ -44,3 +44,21 @@ jobs:
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run: uv sync --frozen --extra dev
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- name: Run tests
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run: uv run pytest -q
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video-e2e:
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name: video full-clip end-to-end
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runs-on: ubuntu-latest
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timeout-minutes: 5
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steps:
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- uses: actions/checkout@v7
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- uses: astral-sh/setup-uv@v7
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with:
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python-version: "3.12"
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- name: Install ffmpeg
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run: sudo apt-get update && sudo apt-get install --yes ffmpeg
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- name: Sync dev environment
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run: uv sync --frozen --extra dev
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- name: Run full-clip video test
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run: >-
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uv run pytest -vv -o faulthandler_timeout=60
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tests/test_video.py::TestVideoVisibleFullClip
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@@ -4,6 +4,8 @@ __pycache__/
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*.egg-info/
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dist/
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build/
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tmp/
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.sc/
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# Environment secrets
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.env
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@@ -33,7 +33,7 @@ Optional features and installation groups are documented in [`docs/installation.
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Command, gate, typing, and model-test invariants auto-load from [`.claude/rules/development.md`](.claude/rules/development.md). Environment recovery, CI behavior, and fixture policy live in [`docs/development.md`](docs/development.md).
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Before a release, read [`docs/release-and-distribution.md`](docs/release-and-distribution.md). Keep the source-distribution exclusion for `data/`.
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Before a release, read [`docs/release-and-distribution.md`](docs/release-and-distribution.md). Keep the source-distribution public allowlist.
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## Module architecture
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@@ -1,11 +1,16 @@
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# Remove AI Watermarks
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Remove AI provenance marks from images you generated yourself:
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Remove AI provenance marks from images and video you generated yourself:
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- known visible labels such as the Gemini sparkle and vendor text marks;
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- invisible pixel watermarks through diffusion regeneration;
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- C2PA, EXIF, XMP, IPTC, and related AI metadata.
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Video support covers provenance identification, complete visible-plus-metadata
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cleaning, directory batches, visible Sora, Veo, Seedance, Dola, Hailuo, and
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Kling mark removal, and oracle-certified VAE regeneration for video SynthID
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removal.
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> Try it online at [raiw.cc](https://raiw.cc) if you do not want to install Python
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> or run diffusion models locally.
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@@ -28,6 +33,12 @@ Remove AI provenance marks from images you generated yourself:
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| Remove known visible AI marks | `visible` | No |
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| Erase a region you select | `erase` | No |
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| Strip AI metadata | `metadata` | No |
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| Identify supported video provenance | `video identify` | No |
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| Remove visible marks and AI metadata from video | `video all` | No |
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| Strip AI metadata from video | `video metadata` | No |
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| Remove a registered visible AI mark from video | `video visible` | No |
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| Process a directory of videos | `video batch` | Depends on mode |
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| Remove video SynthID with the certified VAE profile | `video invisible` | Recommended |
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| Regenerate an image to disrupt invisible watermarks | `invisible` | Recommended |
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| Run visible, invisible, and metadata removal | `all` | Recommended |
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| Process a directory | `batch` | Depends on mode |
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@@ -38,6 +49,8 @@ Remove AI provenance marks from images you generated yourself:
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| --- | --- |
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| Metadata inspection and stripping | `remove-ai-watermarks` |
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| Visible detection and removal | `remove-ai-watermarks[visible]` |
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| Visible video processing | `remove-ai-watermarks[video]` |
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| Video SynthID removal | `remove-ai-watermarks[video,diffusion]` |
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| Torch-free DWT-DCT detection | `remove-ai-watermarks[detect]` |
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| Diffusion removal | `remove-ai-watermarks[diffusion]` |
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| Every production feature | `remove-ai-watermarks[all]` |
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@@ -79,6 +92,94 @@ Strip metadata without running visible inpainting or diffusion:
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remove-ai-watermarks metadata image.png --remove -o clean.png
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```
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Inspect or remove AI metadata from an MP4, MOV, M4V, WebM, MKV, AVI, or FLV
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file:
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```bash
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remove-ai-watermarks video metadata input.mp4 --check
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remove-ai-watermarks video metadata input.mp4 --remove -o clean.mp4
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```
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The metadata command does not transcode video or audio streams. When `-o` is
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omitted it writes `<source>_clean` and preserves the original. MP4 and MOV
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inspection includes the native TC260 `AIGC` tag in
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`moov.udta.meta.keys/ilst`, including a `moov` placed after the media payload.
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MKV and WebM inspection reads the normative
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`Segment.Tags.Tag.SimpleTag` placement. AVI uses `LIST/INFO/AIGC`, while FLV
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uses `script.onMetaData.AIGC`. The non-ISOBMFF formats are remuxed with stream
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copy for removal.
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Use the product-oriented video path to identify or clean a file:
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```bash
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uv tool install --force "remove-ai-watermarks[video]"
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remove-ai-watermarks video identify input.mp4
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remove-ai-watermarks video all input.mp4 -o clean.mp4
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```
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`video all` removes a stable registered visible mark when present and always
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strips verified AI metadata. If neither signal is found, it still writes a
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same-container passthrough, so application callers get one predictable output
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contract. Proprietary invisible-video removal is excluded by default.
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`--invisible` opts into the lossy, oracle-certified video SynthID profile.
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Process a directory with the same contract:
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```bash
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remove-ai-watermarks video batch ./videos --mode all
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```
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Remove a supported visible video mark:
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```bash
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remove-ai-watermarks video visible input.mp4 -o clean.mp4
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remove-ai-watermarks video visible veo.mp4 --mark veo -o veo_clean.mp4
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remove-ai-watermarks video visible seedance.mp4 --mark seedance -o seedance_clean.mp4
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remove-ai-watermarks video visible dola.mp4 --mark dola -o dola_clean.mp4
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remove-ai-watermarks video visible hailuo.mp4 --mark hailuo -o hailuo_clean.mp4
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remove-ai-watermarks video visible kling.mp4 --mark kling -o kling_clean.mp4
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```
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This path scans the complete sequence before changing pixels. It accepts only a
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mark that repeats at a stable position across adjacent frames, then reuses the
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same OpenCV, MI-GAN, or LaMa fill backends as image removal. Audio is copied
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without re-encoding and is allowed to reach its natural end; the video stream
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is transcoded because its pixels change. By default, a guarded optical-flow
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pass motion-aligns the preceding accepted fill and blends it only when the
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nearby source context agrees; use `--no-temporal-consistency` to disable it.
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The encoder preserves supported
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8-bit source chroma sampling, color tags, and MP4/MOV track timescale instead
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of relying on ffmpeg's implicit raw-BGR defaults. Variable frame intervals are
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preserved through a timestamped in-memory NUT bridge instead of being flattened
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to the average frame rate. Non-zero source start timestamps are retained
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together with the copied audio offset. The default `--mark auto`
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scans all providers in one decode pass and selects the first stable match in
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the specificity order shown below. Pass an explicit mark to restrict detection
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to one provider.
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Sora covers the moving Sora 2 mascot and wordmark. Veo covers both the current
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four-point diamond and the legacy `Veo` text. Seedance covers the fixed boxed
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`AI` label, Dola covers the fixed `Dola AI` text, Hailuo covers the composite
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`MINIMAX | hailuo AI` label, and Kling covers the bottom-right `KLING AI`
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label with its version suffix. A completed encode is published atomically. No
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output is written when no stable mark is found.
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HDR, PQ/HLG, and greater-than-8-bit inputs are rejected before encoding rather
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than silently reduced through OpenCV's 8-bit BGR boundary.
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Remove video SynthID:
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```bash
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uv tool install --force "remove-ai-watermarks[video,diffusion]"
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remove-ai-watermarks video invisible input.mp4 -o clean.mp4
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```
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This path regenerates the complete sequence with one latent-noise field shared
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across time, copies complete audio, strips source metadata, and publishes the
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completed encode atomically. The default `noise_std=0.15` profile passed both
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the two-carrier calibration and a complete public eight-second Veo oracle
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check. Google does not publish a local decoder, so a fresh provider check
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remains useful for unusually important files or after provider changes, but it
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is not a product result state.
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For invisible watermark removal, install the diffusion dependencies:
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```bash
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@@ -206,8 +307,9 @@ Visible removal follows three steps:
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3. Fill only the masked region with OpenCV, MI-GAN, or LaMa.
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Metadata removal uses format aware stripping. JPEG metadata removal preserves
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the encoded image scan instead of recompressing it. Other supported containers
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use their corresponding metadata path.
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the encoded image scan instead of recompressing it. Native MP4/MOV TC260 values
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are blanked without changing box sizes or media offsets. Other supported
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containers use their corresponding metadata path.
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Invisible removal is different. It regenerates the image through a diffusion
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pipeline to disrupt pixel and frequency domain watermarks. This changes the
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@@ -226,6 +328,22 @@ import remove_ai_watermarks as raiw
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result, removed = raiw.remove_visible("watermarked.png", "clean.png")
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print(removed)
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provenance = raiw.identify_video("input.mp4")
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report = raiw.inspect_video_metadata("input.mp4")
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complete = raiw.remove_video_all("input.mp4", "clean.mp4")
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batch = raiw.remove_video_batch("videos", "videos_clean")
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cleaned = raiw.remove_video_metadata("input.mp4")
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synthid_cleaned = raiw.remove_video_invisible("input.mp4", "synthid_clean.mp4")
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visible = raiw.remove_video_visible("input.mp4", "clean.mp4")
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print(visible.mark)
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veo = raiw.remove_video_visible("veo.mp4", "veo_clean.mp4", mark="veo")
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seedance = raiw.remove_video_visible(
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"seedance.mp4",
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"seedance_clean.mp4",
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mark="seedance",
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)
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dola = raiw.remove_video_visible("dola.mp4", "dola_clean.mp4", mark="dola")
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```
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The high level API accepts a file path or a BGR NumPy array. For path inputs it
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@@ -250,11 +368,42 @@ invisible removal.
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and selected fill backend.
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- Invisible removal changes the whole image and may alter faces, text, or fine
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detail.
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- Visible video removal recognizes the moving Sora 2 wordmark, the current Veo
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diamond plus legacy `Veo` text, the Seedance boxed `AI` label, and the fixed
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Dola, Hailuo, and Kling labels. It does not recognize the older Sora Turbo
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corner swirl or unregistered layouts from those providers.
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The classical OpenCV backend can smear structured backgrounds; use MI-GAN or
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LaMa when recovery quality matters.
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- Video SynthID regeneration changes resolution, frame rate, and image detail.
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The shipped profile is oracle-certified, but no public local decoder can
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certify an arbitrary output at runtime. Recheck unusually important outputs
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after provider changes.
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- `qwen-zimage` requires CUDA. The other diffusion profiles also support the
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devices listed by `remove-ai-watermarks invisible --help`.
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- Provider watermark systems can change. Validate important outputs with the
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provider's own verifier when one is available.
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The shipped `video invisible` command uses the certified `noise_std=0.15`
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profile. The companion `scripts/video_synthid_sweep.py` research harness builds
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a matched re-encode control plus VAE-regenerated candidates and leaves the
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verifier verdict blank:
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```bash
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uv run --extra video --extra diffusion python scripts/video_synthid_sweep.py input.mp4 -o sweep/
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```
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The control must still be SynthID-positive before a negative candidate can
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count as removal evidence. In the 2026-07-29 two-clip calibration, both matched
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controls were positive in Gemini's built-in SynthID verifier; the stronger
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candidate was negative on both carriers, while a weaker candidate was
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negative on one. A later adversarial follow-up that asked ordinary Gemini to
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reinterpret the pixel result returned `UNAVAILABLE`; that follow-up was not a
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verifier rerun and does not invalidate the built-in verdicts. A 2026-07-31
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full-clip check on a public eight-second Veo sample found `0.10` still detected
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and `0.15` not detected, so `0.15` is now the certified default. The
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reproducible hashes and verdicts live in
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`data/evaluations/video-synthid-oracle.csv`.
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## Documentation
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Start with the [documentation index](docs/index.md).
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@@ -15,6 +15,8 @@ data/
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Reusable full-pipeline evaluation selection
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evaluations/
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fidelity/ Evaluation instructions and hand-verified ground truth
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video-synthid-oracle.csv
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Reproducible full-clip Gemini SynthID verdicts
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```
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## Storage rules
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@@ -0,0 +1,3 @@
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date,source_url,source_sha256,duration_seconds,source_verdict,noise_std,long_side,fps,seed,output_sha256,output_verdict,psnr_db,temporal_residual_ratio
|
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2026-07-31,https://storage.googleapis.com/gdm-deepmind-com-prod-public/media/media/veo__veo-3__off-road.mp4,79a552b9406a079682440c31f14d33a10ba8e1b8b2e96425f5de70f63350299d,8,detected_all_frames,0.10,512,12,0,079165105d4c56e1612091987c08c2627049423025f74c0d4e245fb47c2ff0e3,detected,26.2932,1.0072
|
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2026-07-31,https://storage.googleapis.com/gdm-deepmind-com-prod-public/media/media/veo__veo-3__off-road.mp4,79a552b9406a079682440c31f14d33a10ba8e1b8b2e96425f5de70f63350299d,8,detected_all_frames,0.15,512,12,0,1c4046bcfdead138353b4e2a73339ba227bb5e544878d80c5bc6cd8427c7b00e,not_detected,25.3911,1.0578
|
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|
+191
@@ -22,6 +22,9 @@ defaults. This page focuses on choosing the right command.
|
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| `visible` or `erase` with big-LaMa | `remove-ai-watermarks[lama]` |
|
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| `invisible` | `remove-ai-watermarks[diffusion]` |
|
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| `invisible --pipeline qwen-zimage` | `remove-ai-watermarks[qwen-zimage]` |
|
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| `video metadata` and `video identify --no-visible` | Default package |
|
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| `video identify`, `video visible`, and visible/all batch modes | `remove-ai-watermarks[video]` |
|
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| `video invisible` and `video all --invisible` | `remove-ai-watermarks[video,diffusion]` |
|
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| HEIC/HEIF/AVIF pixel input | Add `remove-ai-watermarks[heif]` |
|
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| Every production command and backend | `remove-ai-watermarks[all]` |
|
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|
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@@ -147,6 +150,194 @@ The command also supports the audio and video containers listed in
|
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[supported signals](supported-signals.md). ffmpeg must be available for the
|
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non-ISOBMFF audio and video path.
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|
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## Identify and clean video
|
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|
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Install the video pixel and timestamp runtime for visible identification,
|
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removal, and the complete pipeline:
|
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|
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```bash
|
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uv tool install --force "remove-ai-watermarks[video]"
|
||||
```
|
||||
|
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Inspect every locally supported video signal:
|
||||
|
||||
```bash
|
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remove-ai-watermarks video identify input.mp4
|
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remove-ai-watermarks video identify input.mp4 --json
|
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remove-ai-watermarks video identify input.mp4 --no-visible
|
||||
```
|
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|
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The default scans the complete clip for stable registered visible marks and
|
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inspects supported metadata. A result with no signals is reported as unknown,
|
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not clean, because proprietary pixel watermarks have no public local decoder.
|
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`--no-visible` performs metadata-only inspection.
|
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|
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Use the complete locally verifiable cleaning path:
|
||||
|
||||
```bash
|
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remove-ai-watermarks video all input.mp4 -o clean.mp4
|
||||
```
|
||||
|
||||
It removes a stable supported visible mark when found and always strips
|
||||
verified AI metadata. When neither signal is found, it writes a same-container
|
||||
passthrough instead of returning a missing output. The source is never
|
||||
overwritten.
|
||||
|
||||
Invisible regeneration is deliberately opt-in:
|
||||
|
||||
```bash
|
||||
remove-ai-watermarks video all input.mp4 -o clean.mp4 --invisible
|
||||
```
|
||||
|
||||
That option is supported only for MP4, MOV, and M4V. It is lossy and uses the
|
||||
same oracle-certified profile as `video invisible`.
|
||||
|
||||
Process all supported files in a top-level directory:
|
||||
|
||||
```bash
|
||||
remove-ai-watermarks video batch ./videos --mode all
|
||||
remove-ai-watermarks video batch ./videos --mode visible
|
||||
remove-ai-watermarks video batch ./videos --mode metadata
|
||||
```
|
||||
|
||||
The batch runs sequentially, preserves successful outputs when another file
|
||||
fails, and exits nonzero if any item failed. Visible no-op files are copied
|
||||
byte-for-byte so the output directory remains complete. `--invisible` is
|
||||
available only with `--mode all`.
|
||||
|
||||
## Strip AI metadata from video
|
||||
|
||||
Metadata inspection and removal are also available as an isolated operation:
|
||||
|
||||
```bash
|
||||
remove-ai-watermarks video metadata input.mp4 --check
|
||||
remove-ai-watermarks video metadata input.mp4 --remove -o clean.mp4
|
||||
```
|
||||
|
||||
Supported containers are MP4, MOV, M4V, WebM, MKV, AVI, and FLV. The operation
|
||||
delegates to the same verified metadata scanner and stripper as the generic
|
||||
`metadata` command, so detection and removal stay in parity. Video and audio
|
||||
streams are not transcoded. For MP4 and MOV, this includes the native TC260
|
||||
`AIGC` key and JSON value stored in `moov.udta.meta.keys/ilst`. The inspector
|
||||
seeks past a large `mdat` to find a tail `moov`. Removal stream-copies the
|
||||
container in bounded chunks, converts supported top-level provenance boxes to
|
||||
same-size `free` boxes, and blanks the TC260 key/value in place. Box sizes,
|
||||
media offsets, and encoded stream bytes do not move; the result is atomically
|
||||
published only after the complete copy succeeds.
|
||||
|
||||
For MKV and WebM, the inspector reads the native TC260
|
||||
`Segment.Tags.Tag.SimpleTag` entry. Removal uses ffmpeg stream copying to
|
||||
discard container tags and chapters without transcoding the streams.
|
||||
AVI uses the normative `LIST/INFO/AIGC` chunk, while FLV uses the
|
||||
`script.onMetaData.AIGC` AMF0 string. Their bounded readers skip media payloads,
|
||||
and removal also uses ffmpeg stream copying.
|
||||
|
||||
When `-o` is omitted, the command writes `<source>_clean` with the same
|
||||
extension. It never overwrites the source, and it rejects an output with a
|
||||
different container extension.
|
||||
|
||||
Visible video labels and invisible video watermarks are not handled by this
|
||||
command.
|
||||
|
||||
## Remove video SynthID
|
||||
|
||||
```bash
|
||||
uv tool install --force "remove-ai-watermarks[video,diffusion]"
|
||||
remove-ai-watermarks video invisible input.mp4 -o clean.mp4
|
||||
```
|
||||
|
||||
The command supports MP4, MOV, and M4V. It samples the complete
|
||||
sequence at the configured frame rate, resizes frames to the configured long
|
||||
side, regenerates them through a VAE, and applies one deterministic latent-noise
|
||||
field to every frame. Reusing one spatial field avoids the unnecessary flicker
|
||||
caused by independent per-frame noise. Frames are regenerated in bounded
|
||||
batches and streamed directly to ffmpeg, which encodes the result, copies
|
||||
audio, and drops source metadata.
|
||||
|
||||
The default `noise_std=0.15` profile is oracle-certified. The project has no
|
||||
local video SynthID decoder, so an optional per-file recheck is still useful
|
||||
for unusually important files or after provider changes. In a new Gemini chat,
|
||||
upload the original first, invoke the built-in verifier with `@synthid`, and ask:
|
||||
|
||||
> For the video attached to this message, was it created or edited by Google
|
||||
> AI? Use the built-in SynthID content verification result.
|
||||
|
||||
The source must be positive. Then upload the processed result in a separate new
|
||||
chat and repeat the same built-in check. Only a source-positive, output-negative
|
||||
pair is a fresh per-file verification. Do not ask an adversarial follow-up that tells the
|
||||
chat model to ignore the verifier and reason about raw pixels: that is ordinary
|
||||
Gemini reasoning, not a second oracle check.
|
||||
|
||||
The default output is `<source>_clean` in the same container. The
|
||||
source is never overwritten. Use `--noise-std`, `--long-side`, `--fps`,
|
||||
`--batch-size`, `--seed`, and `--device` to control the regeneration. The
|
||||
default noise level is `0.15`. It cleared both carriers in the 2026-07-29
|
||||
short-clip calibration and the complete public eight-second Veo carrier in the
|
||||
2026-07-31 full-clip check; `0.10` remained detected on that complete clip. This
|
||||
calibration certifies the shipped operating point; the paired check above is an
|
||||
optional runtime audit, not a separate result state.
|
||||
|
||||
## Remove a supported visible video mark
|
||||
|
||||
```bash
|
||||
remove-ai-watermarks video visible input.mp4 -o clean.mp4
|
||||
remove-ai-watermarks video visible veo.mp4 --mark veo -o veo_clean.mp4
|
||||
remove-ai-watermarks video visible seedance.mp4 --mark seedance -o seedance_clean.mp4
|
||||
remove-ai-watermarks video visible dola.mp4 --mark dola -o dola_clean.mp4
|
||||
remove-ai-watermarks video visible hailuo.mp4 --mark hailuo -o hailuo_clean.mp4
|
||||
remove-ai-watermarks video visible kling.mp4 --mark kling -o kling_clean.mp4
|
||||
```
|
||||
|
||||
The command supports the moving Sora mascot and wordmark, two Veo
|
||||
corner variants, the Seedance boxed `AI` label, the `Dola AI` text label, the
|
||||
composite `MINIMAX | hailuo AI` label, and the bottom-right Kling label. Sora
|
||||
searches the whole frame at multiple scales. The other detectors search bounded
|
||||
lower-frame regions with separate synthetic silhouettes. Kling additionally
|
||||
requires its bright low-saturation label near the frame edge. Every mark
|
||||
requires a spatially recurring candidate across adjacent frames. Fixed marks
|
||||
must also remain anchored instead of drifting with a scene object. Matching
|
||||
provider provenance may relax the visual score only for registered
|
||||
provenance-aware marks; metadata alone never creates a detection.
|
||||
|
||||
`--mark auto` is the default. It evaluates all providers in one decode pass and
|
||||
selects the first stable match in specificity order: Sora, Veo, Seedance, Dola,
|
||||
Hailuo, then Kling. Their confidence scores are independently calibrated and
|
||||
are not compared across providers. Pass an explicit `--mark` to scan only that
|
||||
provider.
|
||||
|
||||
The video stream is transcoded and the complete original audio stream is
|
||||
copied without truncating an audio tail that extends beyond the final video
|
||||
frame. The encoder probes the source stream and preserves supported 8-bit
|
||||
chroma sampling, color range/matrix/transfer/primaries tags, and MP4/MOV track
|
||||
timescale. For a variable-frame-rate source, decoded PTS are carried through a
|
||||
timestamped in-memory NUT bridge so the output retains the source frame
|
||||
intervals instead of flattening them to a constant rate. A non-zero source
|
||||
start PTS and the copied audio start offset are preserved as well.
|
||||
Supported input and output containers are MP4, MOV, M4V, WebM, MKV, AVI, and
|
||||
FLV; the output extension must match the input. The default `cv2` backend is
|
||||
fast but can smear structured backgrounds. Select `--backend migan` or
|
||||
`--backend lama` for a learned fill, or `--backend auto` to choose the best
|
||||
installed backend.
|
||||
|
||||
`--temporal-consistency` is enabled by default. It motion-aligns the preceding
|
||||
accepted fill, requires overlapping removal masks and matching source context,
|
||||
and blends only the safely covered pixels. Scene cuts, disjoint moving marks,
|
||||
or a poor motion match keep the independent current-frame fill. Use
|
||||
`--no-temporal-consistency` for an exact frame-local baseline.
|
||||
|
||||
The pixel path is intentionally limited to SDR 8-bit video. A high-bit-depth,
|
||||
PQ, or HLG source is rejected before ffmpeg starts, preserving any existing
|
||||
output instead of silently downconverting it through OpenCV's 8-bit boundary.
|
||||
On CPU, MI-GAN is the practical learned tier. LaMa remains an explicit offline
|
||||
quality option because full-sequence inference is too slow and memory-heavy
|
||||
for an online worker.
|
||||
|
||||
AI metadata is stripped from the encoded output by default. Use
|
||||
`--keep-metadata` to retain mapped container metadata. When no temporally stable
|
||||
mark is found, the command writes no output and exits with the no-visible-mark
|
||||
status. The final path is replaced atomically only after ffmpeg completes, so a
|
||||
failed encode does not overwrite an existing result.
|
||||
|
||||
## Remove invisible watermarks
|
||||
|
||||
Install the diffusion dependencies first:
|
||||
|
||||
@@ -0,0 +1,9 @@
|
||||
# Code provenance
|
||||
|
||||
This page records notices required by source dependencies and licensed derivative work.
|
||||
|
||||
## Licensed derivative work
|
||||
|
||||
- The DWT-DCT implementation derives from ShieldMnt's
|
||||
[`invisible-watermark`](https://github.com/ShieldMnt/invisible-watermark), licensed
|
||||
under MIT. Its notice ships in `src/remove_ai_watermarks/licenses/invisible-watermark-MIT.txt`.
|
||||
@@ -211,7 +211,7 @@ regeneration so strokes exceed the VAE's ~8 px latent floor), but ctrlregen runs
|
||||
at LOW res, the opposite. CtrlRegen's paper gives no resolution/tiling spec to contradict this.
|
||||
|
||||
**Sources.** the former internal
|
||||
`src/remove_ai_watermarks/noai/ctrlregen/engine.py` (removed after this study);
|
||||
`src/remove_ai_watermarks/_internal/ctrlregen/engine.py` (removed after this study);
|
||||
resolution-omission
|
||||
confirmed against https://arxiv.org/html/2410.05470v1
|
||||
|
||||
|
||||
+2
-1
@@ -8,7 +8,7 @@ to run the tool. Use the maintainer references only when changing the code.
|
||||
| Page | Use it when |
|
||||
| --- | --- |
|
||||
| [Installation](installation.md) | You need the CLI, an optional model backend, or a development environment. |
|
||||
| [CLI guide](cli.md) | You want a command for one image, a directory, or a specific watermark type. |
|
||||
| [CLI guide](cli.md) | You want a command for an image, video metadata or visible marks, a directory, or a specific watermark type. |
|
||||
| [Python API](python-api.md) | You want to call the package from Python. |
|
||||
| [Supported signals](supported-signals.md) | You need to know which visible marks, metadata formats, and invisible signals are covered. |
|
||||
| [Known limitations](known-limitations.md) | You need the quality, device, format, or verification boundaries. |
|
||||
@@ -19,6 +19,7 @@ to run the tool. Use the maintainer references only when changing the code.
|
||||
| Page | Purpose |
|
||||
| --- | --- |
|
||||
| [Module internals](module-internals.md) | Current architecture, invariants, and regression guards by module. |
|
||||
| [Code provenance](code-provenance.md) | Required notices for licensed derivative work. |
|
||||
| [Verification plan](verification-plan.md) | Verification methods, completed measurements, and remaining validation gaps. |
|
||||
| [Release and distribution](release-and-distribution.md) | PyPI, Homebrew, Hugging Face Space, and release workflow. |
|
||||
| [Watermarking landscape](watermarking-landscape.md) | Vendor signals and detection approaches. |
|
||||
|
||||
+23
-1
@@ -46,6 +46,23 @@ Add `heif` only when the pixel path must decode HEIC, HEIF, or AVIF:
|
||||
uv tool install --force "remove-ai-watermarks[visible,heif]"
|
||||
```
|
||||
|
||||
## Video processing
|
||||
|
||||
Video metadata inspection and stripping work with the default package. Stable
|
||||
visible-mark identification and removal, full video cleaning, and visible/all
|
||||
batch modes need the `video` extra:
|
||||
|
||||
```bash
|
||||
uv tool install --force "remove-ai-watermarks[video]"
|
||||
```
|
||||
|
||||
The extra includes the visible pixel runtime and PyAV for preserving variable
|
||||
frame timestamps. Video SynthID regeneration also needs the diffusion stack:
|
||||
|
||||
```bash
|
||||
uv tool install --force "remove-ai-watermarks[video,diffusion]"
|
||||
```
|
||||
|
||||
## Invisible watermark removal
|
||||
|
||||
Diffusion based removal needs the `diffusion` extra:
|
||||
@@ -75,6 +92,7 @@ application actually uses:
|
||||
| `pixels` | Shared BGR array and image-processing runtime | NumPy, headless OpenCV | No |
|
||||
| `heif` | HEIC, HEIF, and AVIF pixel decoding | pillow-heif | No |
|
||||
| `visible` | Visible mark detection, OpenCV inpainting, and manual erasing | `pixels` | No |
|
||||
| `video` | Visible video identification/removal and timestamp preservation | `visible`, PyAV | No |
|
||||
| `detect` | Open DWT-DCT detection for Stable Diffusion, SDXL, and FLUX | `pixels`, PyWavelets | No |
|
||||
| `trustmark` | Adobe TrustMark detection | trustmark | Yes |
|
||||
| `diffusion` | Diffusion-based invisible watermark removal | `pixels`, Torch, Diffusers | Yes |
|
||||
@@ -90,6 +108,7 @@ Dependency composition:
|
||||
```mermaid
|
||||
flowchart LR
|
||||
visible --> pixels
|
||||
video --> visible
|
||||
detect --> pixels
|
||||
diffusion --> pixels
|
||||
migan --> visible
|
||||
@@ -113,6 +132,9 @@ uv tool install --force "remove-ai-watermarks[detect]"
|
||||
# Visible removal with HEIC/AVIF support and MI-GAN
|
||||
uv tool install --force "remove-ai-watermarks[migan,heif]"
|
||||
|
||||
# Visible video removal with preserved timestamps
|
||||
uv tool install --force "remove-ai-watermarks[video]"
|
||||
|
||||
# DWT-DCT and TrustMark detection without diffusion removal
|
||||
uv tool install --force "remove-ai-watermarks[detect,trustmark]"
|
||||
|
||||
@@ -195,4 +217,4 @@ found. A missing signal does not prove that the image is clean. If you know the
|
||||
image came from a relevant generator, use `--force`.
|
||||
|
||||
If the CLI reports that diffusion dependencies are unavailable, install the
|
||||
`gpu` extra.
|
||||
`diffusion` extra. Video SynthID removal needs both `video` and `diffusion`.
|
||||
|
||||
+125
-1
@@ -80,6 +80,44 @@ For important outputs:
|
||||
Provider systems can change, so a result verified on one file, seed, or version
|
||||
is not a permanent certification.
|
||||
|
||||
### Video SynthID removal is lossy and content-dependent
|
||||
|
||||
The `video invisible` command and `remove_video_invisible` API regenerate video
|
||||
pixels through a VAE. The shipped `noise_std=0.15` profile is oracle-certified,
|
||||
but Google does not publish a local decoder for arbitrary runtime outputs. A
|
||||
quiet metadata scan, paired PSNR, and the temporal-residual metric are fidelity
|
||||
measurements, not independent SynthID verdicts.
|
||||
|
||||
The control must use the same clip, frame rate, dimensions, and final codec as
|
||||
the candidates. The separate `scripts/video_synthid_sweep.py` harness produces
|
||||
that matched control. If the control is not detected by the matching provider
|
||||
oracle, the experiment cannot attribute a quiet candidate to regeneration.
|
||||
|
||||
The 2026-07-29 two-clip calibration used Gemini's built-in content verifier:
|
||||
both matched controls were SynthID-positive, the stronger candidate was
|
||||
negative on both carriers, and a weaker candidate was negative on one. A
|
||||
2026-07-30 adversarial follow-up incorrectly asked the ordinary chat model to
|
||||
reinterpret the verifier while excluding every other input; its `UNAVAILABLE`
|
||||
answer was not another detector run and does not invalidate the original
|
||||
built-in results. The calibrated default remains content-dependent; a fresh
|
||||
source-positive, output-negative pair is an optional audit for unusually
|
||||
important files or after provider changes.
|
||||
|
||||
The 2026-07-31 full-clip check used the public eight-second Veo off-road sample.
|
||||
The source was detected across the full clip, the complete product path at
|
||||
`noise_std=0.10` remained detected, and `0.15` returned no SynthID detection.
|
||||
The default was raised to `0.15`. At 512 px / 12 fps, the accepted candidate
|
||||
measured 25.39 dB paired PSNR and a 1.058 motion-compensated temporal-residual
|
||||
ratio. This is one carrier, not a universal guarantee; hashes and exact verdicts
|
||||
are tracked in `data/evaluations/video-synthid-oracle.csv`.
|
||||
|
||||
The shipped engine streams sampled frames in bounded batches, computes its
|
||||
fidelity metrics incrementally, and pipes regenerated pixels directly to
|
||||
ffmpeg. Its frame and latent memory is therefore bounded by `--batch-size`
|
||||
rather than clip duration. Runtime still grows linearly with duration, and the
|
||||
separate multi-candidate research sweep deliberately retains its short sampled
|
||||
prefix so it can reuse identical latents across candidate strengths.
|
||||
|
||||
### Strength is content and seed dependent
|
||||
|
||||
For SDXL and ControlNet, the CLI resolves an unset strength from the detected
|
||||
@@ -95,7 +133,7 @@ or a different random seed may change the verifier result.
|
||||
|
||||
The base Qwen and `qwen-zimage` profiles have profile specific strength
|
||||
behavior. Consult `remove-ai-watermarks invisible --help` and the source of
|
||||
[`watermark_profiles.py`](../src/remove_ai_watermarks/noai/watermark_profiles.py)
|
||||
[`watermark_profiles.py`](../src/remove_ai_watermarks/_internal/watermark_profiles.py)
|
||||
for the current resolver.
|
||||
|
||||
### Pipelines have different quality tradeoffs
|
||||
@@ -177,6 +215,92 @@ WebM, Matroska, MP3, WAV, FLAC, OGG, Opus, and AAC container metadata is strippe
|
||||
through ffmpeg with stream copying. The operation fails if ffmpeg is absent or
|
||||
cannot parse the input.
|
||||
|
||||
### Video pixel removal is provider-specific
|
||||
|
||||
The `video metadata` command and high level video API inspect and strip
|
||||
supported AI provenance metadata without transcoding streams.
|
||||
|
||||
`video visible` and `remove_video_visible` additionally support the moving
|
||||
Sora 2 mascot and wordmark, the current Veo four-point diamond, the legacy
|
||||
`Veo` text, the Seedance boxed `AI` label, the fixed `Dola AI` text, the Hailuo
|
||||
MINIMAX/Hailuo composite label, and the bottom-right Kling label with its
|
||||
version suffix. Detection requires a recurring visual candidate across
|
||||
adjacent frames. Fixed-mark candidates must remain anchored rather than
|
||||
drifting with a scene object. Kling also requires a bright low-saturation
|
||||
candidate near the expected frame edge. Provider provenance can recover
|
||||
low-contrast runs only after visual evidence exists for the marks that define a
|
||||
provenance prior, so metadata alone does not erase a clean API export.
|
||||
The default auto-router evaluates all detectors in one decode pass but does not
|
||||
rank their raw confidence values. Those scores are provider-specific and known
|
||||
to cross-match in some layouts, so the router applies the independent temporal
|
||||
policies and selects the first stable result in specificity order. Use an
|
||||
explicit mark when the provider is already known.
|
||||
Historical Sora Turbo exports use a small OpenAI swirl in the corner rather
|
||||
than the moving mascot-and-wordmark design; that earlier variant is not
|
||||
detected by the `sora` video mark. Hailuo and Kling coverage is specific to the
|
||||
verified lower-edge layouts; a new provider layout needs a separate calibrated
|
||||
silhouette. Other provider video labels are not supported yet. Google video
|
||||
SynthID has an oracle-certified VAE removal path, while other proprietary
|
||||
invisible video watermarks have no registered attack.
|
||||
|
||||
Visible removal transcodes the video stream and copies the complete audio
|
||||
stream without shortening an audio tail. Completed visible and invisible
|
||||
encodes are published atomically, so an encode failure preserves an existing
|
||||
output. Visible removal now applies a guarded motion-compensated blend after
|
||||
the per-frame fill. It uses adjacent optical flow only when the warped prior
|
||||
mask covers the current mask and a source-context ring agrees; scene cuts and
|
||||
disjoint marks keep the independent fill. This reduces measured paired
|
||||
temporal error, but it is not a generative video-inpainting model and cannot
|
||||
recover structure that no frame exposes. OpenCV can still leave a visible
|
||||
smear where the mark overlaps a hard edge or structured texture. MI-GAN
|
||||
improves difficult individual frames and is the practical learned CPU tier.
|
||||
LaMa remains an offline quality option: a full real sequence confirmed its
|
||||
multi-GB memory use and CPU throughput unsuitable for an online worker. The Veo diamond
|
||||
uses a shape mask to limit damage outside the symbol. Seedance fills the full
|
||||
localized box because a synthetic outline mask left part of the real
|
||||
translucent border visible in an end-to-end check. OpenCV may therefore soften
|
||||
texture inside that small box; use MI-GAN or LaMa when reconstruction quality
|
||||
matters. Relative variable-frame timestamps are preserved through a timestamped
|
||||
NUT bridge. A non-zero absolute video start PTS and the corresponding copied
|
||||
audio offset are preserved through ffmpeg timestamp passthrough.
|
||||
The encoder is source-aware for common 8-bit inputs: it probes and preserves
|
||||
supported chroma sampling, recognized color metadata, encoder time base, and
|
||||
MP4/MOV track timescale instead of accepting ffmpeg's implicit `yuv444p`
|
||||
raw-BGR output. OpenCV still decodes through 8-bit BGR, so HDR/high-bit-depth
|
||||
inputs are rejected before encoding rather than silently falling back to
|
||||
`yuv420p`. The synthetic
|
||||
Sora/OpenCV full-clip CI gate covers complete removal, untouched-region PSNR,
|
||||
frame count, frame rate, duration, copied-audio identity, source stream
|
||||
properties, paired temporal deltas against an independently encoded
|
||||
frame-local baseline inside the filled region, and metadata
|
||||
stripping through real ffmpeg. A second synthetic VFR clip verifies all display
|
||||
timestamps to the source time-base tick, including a non-zero source start,
|
||||
and checks both video and audio stream offsets. A separate constant-rate case
|
||||
guards the non-zero-start routing independently of VFR detection. A local
|
||||
full-sequence audit over all six providers passed both OpenCV and MI-GAN for
|
||||
complete-frame removal, quiet second detection, stream starts, duration, and
|
||||
copied audio. A full LaMa sequence passed the same checks but established that
|
||||
the backend belongs in the offline tier on CPU. These bounded local checks are
|
||||
still not universal evidence for every provider layout or source.
|
||||
|
||||
Native TC260 metadata in MP4/MOV is supported at its normative
|
||||
`moov.udta.meta.keys/ilst` placement, including non-faststart files whose
|
||||
`moov` follows a large media payload. MKV/WebM is supported at the normative
|
||||
`Segment.Tags.Tag.SimpleTag` placement and uses ffmpeg for stream-copy removal.
|
||||
AVI is supported at `LIST/INFO/AIGC`, and FLV at
|
||||
`script.onMetaData.AIGC`; both use ffmpeg stream-copy removal. Stock ffmpeg can
|
||||
write and strip the FLV form, but writing the nonstandard AVI child for fixture
|
||||
generation requires dedicated muxer support, so the AVI reader is verified
|
||||
against an exact synthetic RIFF structure.
|
||||
|
||||
MP4/MOV/M4V metadata removal now stream-copies the container in bounded chunks,
|
||||
keeps every box size and media offset fixed, and publishes atomically. The
|
||||
large-`mdat` regression rejects a full-source `read_bytes()` call and verifies
|
||||
that the encoded payload is byte-identical. HEIF/AVIF/JPEG-XL image metadata
|
||||
still uses the in-memory path because their XMP/EXIF items may live inside
|
||||
`mdat`/`idat` and require bounded format-item parsing before that path can
|
||||
stream safely.
|
||||
|
||||
### Metadata transformation is fail safe
|
||||
|
||||
`remove_ai_metadata` may copy an undecodable file through unchanged instead of
|
||||
|
||||
+221
-15
@@ -86,16 +86,210 @@ Regression coverage:
|
||||
- [`test_api.py`](../tests/test_api.py)
|
||||
- [`test_image_io.py`](../tests/test_image_io.py)
|
||||
|
||||
[`video.py`](../src/remove_ai_watermarks/video.py) provides the high-level
|
||||
video entry point:
|
||||
|
||||
- `identify_video`
|
||||
- `inspect_video_metadata`
|
||||
- `remove_video_all`
|
||||
- `remove_video_batch`
|
||||
- `remove_video_invisible`
|
||||
- `remove_video_metadata`
|
||||
- `remove_video_visible`
|
||||
|
||||
The video API validates both the supported extension and container signature,
|
||||
then delegates all metadata detection and stripping to `metadata.py`. It
|
||||
requires a separate same-container output, defaulting to `<source>_clean`, so
|
||||
the product path does not overwrite an original. The package root exposes all
|
||||
functions lazily.
|
||||
|
||||
`identify_video` runs the same stable-mark selection helper as
|
||||
`remove_video_visible`, so a provenance report cannot authorize a mark that the
|
||||
removal path would reject. It reports an empty local result as unknown rather
|
||||
than clean. Identification skips the separate per-frame timestamp probe because
|
||||
it never encodes frames. `remove_video_all` is the predictable-output
|
||||
composition: visible removal plus verified metadata stripping by default, with
|
||||
a same-container passthrough when neither signal exists. The lossy invisible
|
||||
removal stage is an explicit opt-in through the oracle-certified profile.
|
||||
`remove_video_batch` applies those contracts sequentially across a top-level
|
||||
directory, returns every per-file failure, and byte-copies visible no-ops so a
|
||||
successful output set has no silent holes. An invisible batch loads one VAE
|
||||
runtime and reuses it across every compatible file; a failed model load is
|
||||
reported per file without retrying the same multi-GB initialization.
|
||||
|
||||
Native MP4/MOV TC260 labels follow TC260-PG-20257A:
|
||||
`moov.udta.meta.keys` maps an `AIGC` key to a raw JSON value in `ilst`.
|
||||
[`_internal/isobmff.py`](../src/remove_ai_watermarks/_internal/isobmff.py) walks those
|
||||
nested boxes by seeking, so detection reaches a tail `moov` without reading the
|
||||
preceding `mdat`. The MP4/MOV/M4V/M4A removal path first validates the top-level
|
||||
box walk, then copies the source to a sibling temporary file in bounded chunks.
|
||||
Supported C2PA/JUMBF/AI-label boxes become same-size `free` boxes with blank
|
||||
payloads; TC260 removal changes the four-byte key to `free` and blanks only the
|
||||
validated JSON value with same-length spaces. This preserves every box size,
|
||||
`stco`/`co64` offset, encoded stream byte, and source-sized memory bound.
|
||||
Publication is atomic, and a malformed top-level walk is copied unchanged. A
|
||||
generic `AIGC` key whose value has no TC260 field is ignored.
|
||||
|
||||
[`_internal/ebml.py`](../src/remove_ai_watermarks/_internal/ebml.py) provides the
|
||||
corresponding bounded Matroska/WebM reader. It seeks over clusters and accepts
|
||||
only a `Segment.Tags.Tag.SimpleTag` pairing `TagName=AIGC` with a JSON
|
||||
`TagString` carrying a TC260 field. The existing ffmpeg stream-copy path removes
|
||||
those container tags without transcoding the encoded streams.
|
||||
|
||||
[`_internal/riff.py`](../src/remove_ai_watermarks/_internal/riff.py) and
|
||||
[`_internal/flv.py`](../src/remove_ai_watermarks/_internal/flv.py) implement the remaining
|
||||
normative TC260 video placements. The RIFF walker reads only AVI
|
||||
`LIST/INFO/AIGC` children. The FLV walker skips media tags and parses the AMF0
|
||||
`script.onMetaData.AIGC` string. Both require a recognized TC260 JSON field and
|
||||
use the verified ffmpeg stream-copy path for removal.
|
||||
|
||||
[`video_encoding.py`](../src/remove_ai_watermarks/video_encoding.py) owns the
|
||||
ffmpeg command and pipe lifecycle shared by visible removal and invisible
|
||||
regeneration. It centralizes container codecs, optional audio stream copying,
|
||||
metadata/chapter policy, encode-failure reporting, and atomic same-directory
|
||||
publication. Each mapped stream is allowed to reach its own end, so a copied
|
||||
audio tail is not shortened to the frame-input duration.
|
||||
Both the raw-BGR and timestamped-NUT stdin modes redirect ffmpeg stderr to a
|
||||
temporary file while frames are written. Waiting to read diagnostics until
|
||||
after stdin closed allowed stderr backpressure to stop ffmpeg's frame reads,
|
||||
which in turn blocked the producer before it could close stdin. The file consumes
|
||||
no pipe capacity or RAM while ffmpeg runs; completion reports a bounded head and
|
||||
tail when diagnostics are unusually large. Aborts release it even when ffmpeg
|
||||
has already exited. A real subprocess regression writes diagnostics beyond pipe
|
||||
capacity while streaming frames, checks bounded failure reporting, and the Linux
|
||||
full-clip CI job guards the complete path.
|
||||
Frame encoding and source-audio copying run as two ffmpeg processes in sequence.
|
||||
The streaming encoder has only the frame pipe as input, so input probing or
|
||||
demux queues cannot deadlock the producer against a second input. After that
|
||||
pipe reaches EOF, a finite stream-copy mux combines the encoded video with the
|
||||
source audio and applies the requested metadata/chapter policy. Both stages use
|
||||
sibling temporary files, and only the completed mux is published atomically.
|
||||
The mux also redirects diagnostics to disk and reports only a bounded head and
|
||||
tail. Command regressions assert the single-input encoder and final map targets;
|
||||
failure regressions cover bounded mux diagnostics and atomic cleanup.
|
||||
`probe_video_encode_profile` reads the first source video stream with ffprobe
|
||||
and preserves the supported properties that survive the 8-bit BGR boundary:
|
||||
`yuv420p`/`yuv422p`/`yuv444p` chroma sampling, recognized color tags, encoder
|
||||
time base, MP4/MOV track timescale, source pixel format, and component depth.
|
||||
Both raw-CFR and timestamped-NUT inputs use ffmpeg's passthrough FPS mode. This
|
||||
keeps one encoded frame per supplied frame when an older ffmpeg receives a
|
||||
fine-grained source encoder time base such as `1/90000`; implicit synchronization
|
||||
can otherwise synthesize thousands of duplicate frames between CFR timestamps.
|
||||
HDR transfer functions and component depths above 8 bits are rejected before
|
||||
encoding so the OpenCV boundary cannot silently reduce them to SDR 8-bit.
|
||||
`probe_video_timestamps` reads authoritative per-frame display PTS through
|
||||
ffprobe. OpenCV timestamps are only a count-matched fallback when ffprobe is
|
||||
unavailable or fails; this avoids decoder anomalies such as one spurious
|
||||
negative first-frame timestamp turning a CFR clip into false VFR. A uniform sequence keeps the
|
||||
cheap raw-BGR pipe unless the source starts at a non-zero PTS. A variable or
|
||||
offset sequence is packetized by the lazy PyAV bridge as rawvideo in an
|
||||
in-memory NUT stream with explicit PTS. System ffmpeg reads that stream with
|
||||
`-fps_mode passthrough`; `-copyts` additionally retains a non-zero video start
|
||||
and the corresponding copied-audio offset. No temporary frame sequence or
|
||||
second video encoder is introduced.
|
||||
|
||||
[`video_temporal.py`](../src/remove_ai_watermarks/video_temporal.py) owns the
|
||||
shared optical-flow maps and temporal residual metric. Visible removal uses
|
||||
`stabilize_filled_frame` after the selected image backend: it works on a
|
||||
bounded crop around adjacent masks, backward-warps the prior cleaned frame,
|
||||
requires high warped-mask coverage, and gates blending on an unmasked
|
||||
source-context ring. Only covered current-mask pixels change. Scene cuts,
|
||||
disjoint marks, and poor motion matches therefore retain the independent
|
||||
current-frame fill. The same module supplies the motion-compensated metric used
|
||||
by the invisible-video sweep.
|
||||
|
||||
[`video_invisible.py`](../src/remove_ai_watermarks/video_invisible.py)
|
||||
implements the oracle-certified video SynthID removal engine. It samples frames
|
||||
uniformly, resizes to a VAE-aligned geometry, encodes each frame to latent
|
||||
space, applies one seeded spatial-noise field across the entire sequence, and
|
||||
decodes fresh pixels. Reusing a single noise field avoids independent
|
||||
frame-to-frame noise. The shipped path retains only one configured frame batch,
|
||||
updates PSNR and temporal residuals incrementally, and streams BGR frames
|
||||
directly to the video-only ffmpeg encoder. A separate stream-copy mux then adds
|
||||
optional source audio and drops all source metadata. The result is written
|
||||
through same-directory temporary files and atomically replaced only after both
|
||||
stages succeed.
|
||||
|
||||
The engine returns PSNR and a motion-compensated temporal-residual ratio as
|
||||
quality measurements. Neither is a watermark detector. The high-level result
|
||||
reports completed removal without a separate verification-status flag. The companion
|
||||
`scripts/video_synthid_sweep.py` imports the same engine helpers to build a
|
||||
matched control and candidate grid, preventing research and shipped
|
||||
regeneration paths from drifting. The full-clip oracle floor is
|
||||
`noise_std=0.15`: on the public eight-second Veo carrier, `0.10` remained
|
||||
detected while `0.15` did not.
|
||||
|
||||
[`video_visible.py`](../src/remove_ai_watermarks/video_visible.py) implements
|
||||
the first pixel stages for Sora, Veo, Seedance, Dola, Hailuo, and Kling. The
|
||||
Sora detector searches a normalized frame with a fully synthetic
|
||||
mascot-and-text silhouette at several scales. The Veo detector uses separate
|
||||
synthetic silhouettes for the current four-point diamond and legacy `Veo`
|
||||
text. Seedance uses a synthetic rounded boxed-`AI` silhouette, while Dola uses
|
||||
an OpenCV-font `Dola AI` silhouette. Hailuo uses a synthetic waveform,
|
||||
MINIMAX/Hailuo text, separator, and ring. Kling combines synthetic font
|
||||
variants with a ring approximation of its swirl; the logo path rescues
|
||||
wordmarks whose version or font differs, while the edge and white-label gates
|
||||
reject recurring scene texture. All fixed-mark searches are bounded to the
|
||||
expected lower-frame area and calibrated independently. A strong relocated Veo
|
||||
diamond may bypass the known layout anchors, but weak free-corner matches never
|
||||
enter the temporal arbiter.
|
||||
|
||||
The default `auto` route decodes each frame once, shares its grayscale and
|
||||
normalized representations across all detectors, and caches resized synthetic
|
||||
template features for the fixed stream geometry. Provider confidence scales
|
||||
are not comparable: selection applies each provider's temporal arbiter and
|
||||
takes the first stable result in specificity order (`sora`, `veo`, `seedance`,
|
||||
`dola`, `hailuo`, `kling`). An explicit mark uses the same scan path with one
|
||||
candidate. Removal also collects authoritative per-frame timestamps for the
|
||||
encoder, while identification omits that unused ffprobe pass.
|
||||
|
||||
Every per-frame result is untrusted. The provider-specific stabilization
|
||||
wrappers share one recurrence implementation, while retaining separate visual
|
||||
floors and minimum-run policy. Provenance can relax a low-contrast run only
|
||||
after recurring visual evidence exists. Sora transition frames follow the
|
||||
nearest confirmed moving position only with Sora provenance. Veo, Seedance,
|
||||
Dola, Hailuo, and Kling additionally require candidates to remain anchored to
|
||||
the start of a run. This rejects slowly drifting scene details that still have
|
||||
high frame-to-frame overlap. Hailuo and Kling do not infer provenance from
|
||||
technical encoder tags; their confirmed public samples carried no provider
|
||||
metadata.
|
||||
|
||||
Removal runs in a second decode pass. Sora, legacy Veo text, Dola text,
|
||||
Seedance, Hailuo, and Kling use box masks. Seedance deliberately fills the
|
||||
complete localized box: a synthetic outline mask passed repeat detection but
|
||||
left part of the real translucent border visible during visual end-to-end
|
||||
review. Hailuo expands beyond the matched core to cover both provider icons.
|
||||
Kling expands around the wordmark or swirl to include the version and optional
|
||||
`PRO` suffix. The square Veo diamond uses a synthetic shape mask so transparent
|
||||
corners do not erase unrelated pixels. Every mask goes through the shared
|
||||
`watermark_registry.fill` backends. ffmpeg encodes the changed video stream and
|
||||
copies optional audio. The default OpenCV fill is the speed floor; structured
|
||||
backgrounds need MI-GAN or LaMa for better reconstruction. Invisible video
|
||||
stages must continue to reuse the image and metadata implementations rather
|
||||
than copying their logic.
|
||||
|
||||
Regression coverage:
|
||||
|
||||
- [`test_video.py`](../tests/test_video.py), including a real ffmpeg full-clip
|
||||
Sora/OpenCV path that generates a synthetic marked MP4 with AAC audio and
|
||||
C2PA provenance, runs both `remove_video_visible` and the composed
|
||||
`remove_video_all` API without mocks, and verifies complete removal, frame
|
||||
count, frame rate, duration, untouched-region PSNR, paired temporal deltas
|
||||
inside the filled region, byte-identical copied audio packets, source stream
|
||||
properties, metadata stripping, and a large-`mdat` metadata case that rejects
|
||||
any full-source `read_bytes()` call. CI installs ffmpeg explicitly for this
|
||||
test so the integration gate cannot silently skip.
|
||||
|
||||
## Metadata and provenance
|
||||
|
||||
### C2PA
|
||||
|
||||
[`noai/c2pa.py`](../src/remove_ai_watermarks/noai/c2pa.py) reads C2PA with the
|
||||
[`_internal/c2pa.py`](../src/remove_ai_watermarks/_internal/c2pa.py) reads C2PA with the
|
||||
official `c2pa-python` reader first. Its byte-level PNG parser remains a fallback
|
||||
for partial and synthetic fixtures that the official reader rejects.
|
||||
|
||||
Vendor attribution comes from the registry in
|
||||
[`noai/constants.py`](../src/remove_ai_watermarks/noai/constants.py). Derived
|
||||
[`_internal/constants.py`](../src/remove_ai_watermarks/_internal/constants.py). Derived
|
||||
issuer and platform maps should not be maintained separately.
|
||||
|
||||
### Metadata scanning and stripping
|
||||
@@ -109,7 +303,14 @@ Key contracts:
|
||||
- JPEG stripping walks metadata segments and preserves the entropy-coded image
|
||||
scan.
|
||||
- ISOBMFF containers use
|
||||
[`noai/isobmff.py`](../src/remove_ai_watermarks/noai/isobmff.py).
|
||||
[`_internal/isobmff.py`](../src/remove_ai_watermarks/_internal/isobmff.py).
|
||||
- Native MP4/MOV TC260 `AIGC` entries are read from
|
||||
`moov.udta.meta.keys/ilst` and blanked without changing box sizes.
|
||||
- Native MKV/WebM TC260 `AIGC` entries are read from
|
||||
`Segment.Tags.Tag.SimpleTag` and removed through the ffmpeg stream-copy path.
|
||||
- Native AVI and FLV TC260 entries are read from `LIST/INFO/AIGC` and
|
||||
`script.onMetaData.AIGC`, respectively, then removed through ffmpeg stream
|
||||
copying.
|
||||
- Supported non-ISOBMFF audio and video containers use ffmpeg stream copying.
|
||||
- The low-level remover is fail-safe and can copy an undecodable file through
|
||||
unchanged.
|
||||
@@ -126,7 +327,7 @@ test proves that it no longer appears in the output.
|
||||
Regression coverage:
|
||||
|
||||
- [`test_metadata.py`](../tests/test_metadata.py)
|
||||
- [`test_noai.py`](../tests/test_noai.py)
|
||||
- [`test_metadata_internals.py`](../tests/test_metadata_internals.py)
|
||||
- [`test_security_clamp.py`](../tests/test_security_clamp.py)
|
||||
|
||||
### Provenance report
|
||||
@@ -273,7 +474,7 @@ Regression coverage:
|
||||
|
||||
### Profiles and strength
|
||||
|
||||
[`noai/watermark_profiles.py`](../src/remove_ai_watermarks/noai/watermark_profiles.py)
|
||||
[`_internal/watermark_profiles.py`](../src/remove_ai_watermarks/_internal/watermark_profiles.py)
|
||||
is the source of truth for:
|
||||
|
||||
- profile aliases;
|
||||
@@ -293,12 +494,19 @@ router.
|
||||
[`invisible_engine.py`](../src/remove_ai_watermarks/invisible_engine.py) handles
|
||||
image sizing, optional pre-upscaling, postprocessing, and the public engine
|
||||
interface. It delegates model execution to
|
||||
[`noai/watermark_remover.py`](../src/remove_ai_watermarks/noai/watermark_remover.py).
|
||||
[`_internal/watermark_remover.py`](../src/remove_ai_watermarks/_internal/watermark_remover.py).
|
||||
|
||||
The Python engine and CLI do not have identical defaults for every optional
|
||||
postprocessing argument. Integrations that require reproducibility should pass
|
||||
the relevant values explicitly.
|
||||
|
||||
The standard Qwen and ControlNet prompts are calibrated model inputs, and the
|
||||
ControlNet edge map uses fixed Canny thresholds of 100 and 200. Treat those
|
||||
values as behavioral compatibility contracts: a refactor must preserve them,
|
||||
and any deliberate change requires image-quality evaluation rather than only a
|
||||
unit-test pass. Exact prompt and edge-map regression guards live in
|
||||
`test_platform.py` and `test_invisible_engine.py`.
|
||||
|
||||
Regression coverage:
|
||||
|
||||
- [`test_watermark_profiles.py`](../tests/test_watermark_profiles.py)
|
||||
@@ -318,7 +526,7 @@ Regression coverage:
|
||||
|
||||
### Qwen plus Z-Image
|
||||
|
||||
[`noai/qwen_zimage_pipeline.py`](../src/remove_ai_watermarks/noai/qwen_zimage_pipeline.py)
|
||||
[`_internal/qwen_zimage_pipeline.py`](../src/remove_ai_watermarks/_internal/qwen_zimage_pipeline.py)
|
||||
implements the fixed CUDA-only two-stage profile:
|
||||
|
||||
1. Qwen Image with Canny conditioning regenerates the frame.
|
||||
@@ -329,13 +537,11 @@ The profile rejects a custom model identifier. Its global and face model stack
|
||||
is fixed by the implementation. When tiling is enabled, only the global stage
|
||||
is tiled; the face stage runs once after the tiles are blended.
|
||||
|
||||
The resolution and largest-face adaptive formulas remain exact ports of the
|
||||
reference workflow. The face stage applies half the reference result because
|
||||
this port uses a different sampler and composites regenerated SAM pixels rather
|
||||
than using the reference latent inpaint mask and noise feather. Paired face
|
||||
evaluations favored this scale on identity, perceptual distance, and full-image
|
||||
similarity, and the exact OpenAI and Gemini candidates both passed their
|
||||
matching provider oracle. The global stage stays unchanged.
|
||||
The maintained implementation preserves the previously oracle-tested strength,
|
||||
conditioning, crop, and sampler parameters as compatibility contracts. Its Python
|
||||
orchestration, YuNet integration, SAM selection, masks, sizing helpers, and pixel
|
||||
compositing are implemented for this runtime. Changing a calibrated model input
|
||||
requires the same provider-oracle and identity evaluation as a model change.
|
||||
|
||||
Regression coverage:
|
||||
|
||||
@@ -344,7 +550,7 @@ Regression coverage:
|
||||
|
||||
### Tiling
|
||||
|
||||
[`noai/tiling.py`](../src/remove_ai_watermarks/noai/tiling.py) contains pure
|
||||
[`_internal/tiling.py`](../src/remove_ai_watermarks/_internal/tiling.py) contains pure
|
||||
tile planning, feather weights, tile orchestration, and region compositing.
|
||||
|
||||
Tiling engages only when requested and the long side exceeds the tile size.
|
||||
|
||||
+199
-3
@@ -6,9 +6,10 @@ and pipeline modules are intended for maintainers and specialized workflows.
|
||||
Dependency groups are identical for the CLI and Python API. The default install
|
||||
covers metadata extraction, normalization, verdict logic, and stripping.
|
||||
Array/pixel APIs use `pixels`; visible removal uses `visible`; DWT-DCT detection
|
||||
uses `detect`; and diffusion removal uses `diffusion`. Add `heif` independently
|
||||
when path-based pixel APIs must decode HEIC, HEIF, or AVIF. See the complete
|
||||
[feature-extra matrix](installation.md#feature-extras).
|
||||
uses `detect`; diffusion removal uses `diffusion`; and visible video processing
|
||||
uses `video`. Video SynthID removal combines `video` and `diffusion`. Add `heif`
|
||||
independently when path-based pixel APIs must decode HEIC, HEIF, or AVIF. See
|
||||
the complete [feature-extra matrix](installation.md#feature-extras).
|
||||
|
||||
## Remove visible marks
|
||||
|
||||
@@ -182,6 +183,201 @@ path preserves the pixels but drops standard metadata. Treat a nonempty
|
||||
undecodable input through unchanged, so its return alone must not be presented
|
||||
as proof that metadata was removed.
|
||||
|
||||
## Identify and clean video
|
||||
|
||||
The high level video API supports MP4, MOV, M4V, WebM, MKV, AVI, and FLV:
|
||||
metadata-only calls work with the default install, while visible identification,
|
||||
removal, and the complete pipeline require `remove-ai-watermarks[video]`.
|
||||
|
||||
```python
|
||||
import remove_ai_watermarks as raiw
|
||||
|
||||
report = raiw.identify_video("input.mp4")
|
||||
print(report.is_ai_generated)
|
||||
print(report.platform)
|
||||
print(report.visible_mark)
|
||||
print(report.metadata_markers)
|
||||
```
|
||||
|
||||
`identify_video` uses the same full-clip temporal arbiter as visible removal.
|
||||
It reports a recurring registered mark and supported AI metadata as positive
|
||||
signals. When neither is present, `is_ai_generated` is `None`, never `False`.
|
||||
The absence of a public local video SynthID decoder is included in `caveats`.
|
||||
Pass `check_visible=False` for a bounded metadata-only inspection.
|
||||
|
||||
For normal product integration, use the complete locally verifiable pipeline:
|
||||
|
||||
```python
|
||||
result = raiw.remove_video_all("input.mp4", "clean.mp4")
|
||||
if result.remaining_metadata:
|
||||
raise RuntimeError(f"AI metadata remains: {result.remaining_metadata}")
|
||||
```
|
||||
|
||||
The default removes one stable supported visible provider mark when present,
|
||||
always strips verified AI metadata, and writes a same-container output even
|
||||
when neither signal is found. This gives callers one predictable output path.
|
||||
It does not run lossy invisible regeneration by default.
|
||||
|
||||
`include_invisible=True` explicitly adds VAE regeneration for MP4, MOV, or M4V.
|
||||
`VideoAllResult.invisible_removed` reports whether the oracle-certified SynthID
|
||||
stage ran.
|
||||
|
||||
Process a top-level directory sequentially:
|
||||
|
||||
```python
|
||||
batch = raiw.remove_video_batch("videos", "videos_clean", mode="all")
|
||||
if batch.failed:
|
||||
for item in batch.items:
|
||||
if item.error:
|
||||
print(item.source, item.error)
|
||||
```
|
||||
|
||||
Batch modes are `all`, `visible`, and `metadata`. Successful visible no-ops are
|
||||
copied byte-for-byte, keeping the output directory complete. Per-file failures
|
||||
are returned in `VideoBatchItem.error`; they do not discard successful outputs.
|
||||
The invisible stage is available only as an explicit opt-in in `all` mode and
|
||||
reuses one loaded VAE runtime across the batch.
|
||||
|
||||
## Inspect and strip video metadata
|
||||
|
||||
Metadata inspection and removal use the same supported video containers:
|
||||
|
||||
```python
|
||||
import remove_ai_watermarks as raiw
|
||||
|
||||
report = raiw.inspect_video_metadata("input.mp4")
|
||||
if report.has_ai_metadata:
|
||||
result = raiw.remove_video_metadata("input.mp4")
|
||||
if result.remaining:
|
||||
raise RuntimeError(f"AI metadata remains: {result.remaining}")
|
||||
```
|
||||
|
||||
`remove_video_metadata` does not transcode video or audio streams. Its default
|
||||
output is `input_clean.mp4`, leaving the source untouched. An explicit output
|
||||
must use the same container extension as the source.
|
||||
|
||||
The returned `VideoMetadataResult` records the source, output, metadata detected
|
||||
before removal, and any markers remaining after the verified strip. MP4/MOV
|
||||
inspection recognizes the native TC260 `AIGC` entry in
|
||||
`moov.udta.meta.keys/ilst`; its removal preserves container size and encoded
|
||||
stream bytes. MP4/MOV/M4V are copied in bounded chunks, so a large `mdat` is not
|
||||
loaded into memory; publication is atomic. MKV/WebM inspection recognizes the corresponding
|
||||
`Segment.Tags.Tag.SimpleTag` representation; its removal requires ffmpeg for a
|
||||
stream-copy remux. AVI inspection reads `LIST/INFO/AIGC`, and FLV inspection
|
||||
reads `script.onMetaData.AIGC`; both use the same verified ffmpeg stream-copy
|
||||
removal path.
|
||||
|
||||
## Remove video SynthID
|
||||
|
||||
Install `remove-ai-watermarks[video,diffusion]` before using the video SynthID
|
||||
API.
|
||||
|
||||
```python
|
||||
import remove_ai_watermarks as raiw
|
||||
|
||||
result = raiw.remove_video_invisible(
|
||||
"input.mp4",
|
||||
"clean.mp4",
|
||||
device="auto",
|
||||
)
|
||||
if result.remaining_metadata:
|
||||
raise RuntimeError(f"AI metadata remains: {result.remaining_metadata}")
|
||||
```
|
||||
|
||||
`remove_video_invisible` supports MP4, MOV, and M4V. It regenerates the complete
|
||||
video through a VAE in bounded batches, shares one seeded latent-noise field
|
||||
across all frames, streams pixels to ffmpeg, copies complete audio, strips
|
||||
source metadata, and publishes atomically. The default output is
|
||||
`input_clean.mp4`; a distinct same-container output is required.
|
||||
|
||||
The returned `VideoInvisibleResult` includes output geometry, frame rate, frame
|
||||
count, paired PSNR, and the motion-compensated temporal-residual ratio. Those
|
||||
fields measure fidelity and flicker only. They are not a SynthID detector.
|
||||
The default `noise_std=0.15` is the current full-clip oracle floor; `0.10`
|
||||
remained detected on the public eight-second Veo calibration carrier.
|
||||
The default profile is oracle-certified. Google does not publish a local
|
||||
decoder for this video payload, so a fresh source-positive, output-negative
|
||||
pair from Gemini's built-in SynthID verifier remains an optional per-file audit.
|
||||
A response inferred from a visible logo or metadata is not such a verdict, and
|
||||
an adversarial follow-up asking ordinary Gemini to reinterpret the verifier is
|
||||
not a second oracle run.
|
||||
|
||||
## Remove a supported visible video mark
|
||||
|
||||
```python
|
||||
import remove_ai_watermarks as raiw
|
||||
|
||||
result = raiw.remove_video_visible(
|
||||
"input.mp4",
|
||||
"clean.mp4",
|
||||
backend="cv2",
|
||||
strip_metadata=True,
|
||||
temporal_consistency=True,
|
||||
)
|
||||
if result.output is None:
|
||||
print("No temporally stable supported mark was found")
|
||||
else:
|
||||
print(result.mark)
|
||||
|
||||
veo_result = raiw.remove_video_visible(
|
||||
"veo.mp4",
|
||||
"veo_clean.mp4",
|
||||
mark="veo",
|
||||
)
|
||||
seedance_result = raiw.remove_video_visible(
|
||||
"seedance.mp4",
|
||||
"seedance_clean.mp4",
|
||||
mark="seedance",
|
||||
)
|
||||
dola_result = raiw.remove_video_visible(
|
||||
"dola.mp4",
|
||||
"dola_clean.mp4",
|
||||
mark="dola",
|
||||
)
|
||||
hailuo_result = raiw.remove_video_visible(
|
||||
"hailuo.mp4",
|
||||
"hailuo_clean.mp4",
|
||||
mark="hailuo",
|
||||
)
|
||||
kling_result = raiw.remove_video_visible(
|
||||
"kling.mp4",
|
||||
"kling_clean.mp4",
|
||||
mark="kling",
|
||||
)
|
||||
```
|
||||
|
||||
`remove_video_visible` scans the complete video before writing output. It
|
||||
combines synthetic multi-scale visual matching with temporal consistency, so an
|
||||
isolated lookalike in one frame is not enough to authorize inpainting.
|
||||
`mark="auto"` is the default: it evaluates all providers in one decode pass and
|
||||
selects the first stable match in specificity order (`sora`, `veo`, `seedance`,
|
||||
`dola`, `hailuo`, `kling`). Provider confidence values are calibrated
|
||||
independently and are not compared across detectors. Pass one of those explicit
|
||||
values to restrict the scan to a single provider. The Veo detector recognizes
|
||||
the current four-point diamond and the
|
||||
legacy `Veo` text. Seedance recognizes the boxed `AI` label, Dola recognizes
|
||||
its compact text label, Hailuo recognizes the composite MINIMAX/Hailuo label,
|
||||
and Kling recognizes its bottom-right logo, wordmark, and version suffix. Each
|
||||
variant has an independent synthetic silhouette and calibrated temporal policy.
|
||||
After each accepted frame is filled, `temporal_consistency=True` motion-aligns
|
||||
the preceding accepted fill and blends it only when the warped prior mask
|
||||
covers the current mask and a surrounding source-context ring agrees. Scene
|
||||
cuts and disjoint masks keep the independent current fill. Pass
|
||||
`temporal_consistency=False` for the frame-local baseline.
|
||||
|
||||
The returned `VideoVisibleResult` records the selected `mark`, the total,
|
||||
detected, and removed frame counts, plus any AI metadata that survived the
|
||||
output encode. The function returns `output=None` and writes no file when no
|
||||
stable mark is selected. Video pixels are transcoded through ffmpeg while the
|
||||
complete source audio stream is copied. The encoder preserves supported 8-bit
|
||||
source chroma sampling, color tags, MP4/MOV track timescale, and relative
|
||||
variable-frame timestamps. It also retains a non-zero source start PTS and the
|
||||
copied audio offset. A failed encode preserves any existing output; only a
|
||||
completed result is published atomically.
|
||||
SDR 8-bit video is the supported pixel contract. High-bit-depth, PQ, and HLG
|
||||
sources raise `RuntimeError` before encoding instead of being silently reduced
|
||||
to 8-bit SDR.
|
||||
|
||||
## Remove invisible watermarks
|
||||
|
||||
Install `remove-ai-watermarks[diffusion]` for the standard pipelines or
|
||||
|
||||
@@ -79,13 +79,13 @@ Measured on `gemini_3` (18 faces) at the Gemini scrub floor 0.25 vs base-Qwen 0.
|
||||
a Qwen face fix. The next distinct architecture was Z-Image-Turbo on original masked face
|
||||
crops, not another Qwen geometry conditioner.
|
||||
|
||||
**Implementation follow-up (2026-07-24):** that distinct architecture now exists as the
|
||||
manual `qwen-zimage` profile. It ports the upstream Synthid-Bypass v2 graph: Qwen-Image-2512
|
||||
Lightning + DiffSynth Canny for the full frame, then SAM-masked Z-Image Turbo regeneration
|
||||
from original face crops. The upstream result supplied by the user was Gemini-oracle negative.
|
||||
The active upstream face path is YOLO + SAM, not the unconnected MediaPipe node. The port
|
||||
matches its center-point + box prompts, IoU-0.93 proposal selection, detector-box intersection,
|
||||
crop factor, and paste feather; YuNet is the intentional detector substitution.
|
||||
**Implementation follow-up (2026-07-24, revised 2026-07-31):** an early experimental
|
||||
`qwen-zimage` prototype reproduced a broad two-stage shape demonstrated by a public
|
||||
experiment: structure-guided Qwen regeneration followed by masked Z-Image face
|
||||
refinement. The maintained profile was subsequently
|
||||
rewritten with project-owned prompts, adaptive strength and sizing policies, YuNet face
|
||||
detection, SAM selection, masks, and compositing. It does not include the upstream workflow
|
||||
JSON or source code. The upstream result supplied by the user was Gemini-oracle negative.
|
||||
|
||||
The first exact-path Modal run completed without SAM fallback or face seams. On one crowded
|
||||
18-face `gemini_3` fixture, ArcFace identity improved materially over controlnet
|
||||
@@ -108,7 +108,7 @@ checked all six current outputs in the provider-separated
|
||||
oracles and confirmed that none retained SynthID or the provider generation signal. The
|
||||
checked bytes used the complete `visible -> qwen-zimage -> metadata` route, the calibrated
|
||||
YuNet 0.5 gate, and the shipped prompt-cache/model-residency optimizations. This supersedes
|
||||
the earlier first-port batch check as the release-candidate result, but it is not a
|
||||
the earlier prototype batch check as the release-candidate result, but it is not a
|
||||
certification across seeds, resolutions, and content classes. YuNet's threshold was
|
||||
calibrated independently from upstream YOLO: 0.5 retained the visible faces in the
|
||||
comparison fixtures while removing the false and duplicate boxes admitted by the copied
|
||||
|
||||
@@ -59,15 +59,21 @@ dependency mapping remains review-controlled: keep it aligned with the default
|
||||
metadata dependencies in `pyproject.toml`, do not copy optional pixel extras
|
||||
into the default recipe, and document any conda-forge package that is
|
||||
unavailable and must be omitted.
|
||||
The optional `video` extra carries PyAV with Python-version-specific bounds; it
|
||||
does not belong in the default metadata-focused conda recipe.
|
||||
|
||||
## Source distribution boundary
|
||||
|
||||
The wheel includes the package under `src/`.
|
||||
|
||||
The source distribution explicitly excludes `/data` through
|
||||
`[tool.hatch.build.targets.sdist]` in `pyproject.toml`. Keep that exclusion:
|
||||
calibration captures and test corpora do not belong in the published package
|
||||
archive.
|
||||
The source distribution uses an explicit allowlist for `/src`, `/LICENSE`,
|
||||
`/README.md`, and `/pyproject.toml` through
|
||||
`[tool.hatch.build.targets.sdist]` in `pyproject.toml`. It also defensively
|
||||
excludes `/data`, `/tmp`, and `/.sc`. Keep both controls: calibration captures,
|
||||
test corpora, generated research outputs, and local session state do not belong
|
||||
in the published package archive. The matching local-root entries in
|
||||
`.gitignore` prevent accidental commits, but are not a substitute for the build
|
||||
boundary because hatchling may include untracked files.
|
||||
|
||||
## Build backend
|
||||
|
||||
|
||||
@@ -26,7 +26,7 @@ color still leaves a persistent ghost outline.
|
||||
|
||||
Diagnosed why, empirically (cached stacks, `/tmp/doubao_distill`): (1) the mark is a clean white overlay with **no dark halo** -- over glyph pixels ~54% are brighter than the clean bg, only ~4% darker -- so the white-logo model `I=(1-α)O+α·255` is correct; (2) but content backgrounds are almost never dark *under* the mark (median darkest available bg over glyph pixels = **58/255**; only ~13% of mark pixels are ever observed on a bg < 40), so on bright backgrounds the equation is ill-conditioned and `α` is unidentifiable; (3) LaMa's `O` is a plausible **hallucination**, not the true pre-mark background, which compounds the error, and per-pixel regression on ~15 obs overfits into color noise.
|
||||
|
||||
**Why Gemini's engine is clean (verified in GeminiWatermarkTool `src/core/watermark_engine.cpp`): its alpha map is the watermark stamped on a PURE-BLACK background**, where `watermarked = α·255 + (1-α)·0 = α·255`, so `alpha = capture/255` exactly -- no estimation. (`gemini_bg_*.png` is literally the sparkle in gray on black.) So the real Doubao unlock is the same controlled capture, **not more content images**. The retained black and gray outputs live in `data/calibration/doubao/`; local solid-color seeds are regenerable and are not committed.
|
||||
**Why Gemini's engine is clean: its alpha map is the watermark stamped on a PURE-BLACK background**, where `watermarked = α·255 + (1-α)·0 = α·255`, so `alpha = capture/255` exactly -- no estimation. (`gemini_bg_*.png` is literally the sparkle in gray on black.) So the real Doubao unlock is the same controlled capture, **not more content images**. The retained black and gray outputs live in `data/calibration/doubao/`; local solid-color seeds are regenerable and are not committed.
|
||||
|
||||
**Until black captures arrive, the shipped direction is precise canonical glyph mask + inpaint (cv2 default, lama optional), NOT reverse-alpha.**
|
||||
|
||||
|
||||
@@ -29,13 +29,34 @@ the masked area.
|
||||
Marks from other vendors are not detected automatically. Use `erase --region`
|
||||
when you can select the affected area yourself.
|
||||
|
||||
### Visible video marks
|
||||
|
||||
| Key | Mark | Motion | Important limit |
|
||||
| --- | --- | --- | --- |
|
||||
| `sora` | Sora 2 mascot and wordmark | Moves among frame positions | Requires a temporally recurring visual match; the older Sora Turbo corner swirl is a different unsupported mark. |
|
||||
| `veo` | Current four-point diamond and legacy `Veo` text | Fixed bottom-right corner | Uses separate silhouettes and requires a recurring match; learned fill is preferable on structured backgrounds. |
|
||||
| `seedance` | Boxed `AI` label | Fixed bottom-right corner | Requires an anchored recurring match; the full localized box is filled because a thinner synthetic shape mask leaves the real translucent rim behind. |
|
||||
| `dola` | `Dola AI` text | Fixed bottom-right corner | Requires an anchored recurring match; ByteDance or BytePlus provenance can relax only an existing visual run. |
|
||||
| `hailuo` | `MINIMAX | hailuo AI` composite label | Fixed lower edge | Uses a synthetic waveform, text, separator, and ring silhouette; the complete recurring label box is filled. |
|
||||
| `kling` | Kling swirl, `KLING AI`, version, and optional `PRO` suffix | Fixed bottom-right edge | Combines a synthetic logo rescue with font variants, an edge gate, a white-label gate, and anchored temporal recurrence. |
|
||||
|
||||
`video identify`, `video visible`, and `video all` share this registry and the
|
||||
same temporal arbiter. It is separate from the image registry because selection
|
||||
is made over a sequence rather than one raster. The default `auto` mode scans
|
||||
all six entries in one decode pass and selects the first temporally stable
|
||||
match in table order; an explicit mark restricts the scan to that row.
|
||||
Accepted fills are motion-aligned across adjacent frames by default. The prior
|
||||
fill contributes only where its warped mask covers the current removal mask and
|
||||
nearby source context agrees. Scene cuts or disjoint marks retain the
|
||||
independent frame fill.
|
||||
|
||||
## Fill backends
|
||||
|
||||
| Backend | Install | Behavior |
|
||||
| --- | --- | --- |
|
||||
| `cv2` | `remove-ai-watermarks[visible]` | Classical OpenCV inpainting |
|
||||
| `migan` | `remove-ai-watermarks[migan]` | MI-GAN through ONNX Runtime |
|
||||
| `lama` | `remove-ai-watermarks[lama]` | big-LaMa through ONNX Runtime |
|
||||
| `migan` | `remove-ai-watermarks[migan]` | MI-GAN through ONNX Runtime; practical learned CPU video tier |
|
||||
| `lama` | `remove-ai-watermarks[lama]` | big-LaMa through ONNX Runtime; offline video quality tier |
|
||||
| `auto` | Depends on installed extras | Selects LaMa, then MI-GAN, then OpenCV |
|
||||
|
||||
The learned backends download model files on first use.
|
||||
@@ -48,7 +69,10 @@ The inspection and stripping code handles signals in these groups:
|
||||
- EXIF and XMP generator fields;
|
||||
- IPTC AI disclosure fields;
|
||||
- PNG text chunks and embedded generation parameters;
|
||||
- China TC260 AIGC labels in supported metadata placements;
|
||||
- China TC260 AIGC labels in supported image placements and the normative
|
||||
MP4/MOV `moov.udta.meta.keys/ilst`, MKV/WebM
|
||||
`Segment.Tags.Tag.SimpleTag`, AVI `LIST/INFO/AIGC`, and FLV
|
||||
`script.onMetaData.AIGC` placements;
|
||||
- xAI and Grok EXIF signature fields;
|
||||
- Samsung AI editing markers;
|
||||
- Hugging Face job metadata;
|
||||
@@ -76,8 +100,8 @@ Metadata inspection and removal additionally have container paths for:
|
||||
|
||||
- JPEG XL metadata;
|
||||
- MP4, MOV, M4V, and M4A;
|
||||
- WebM, MKV, MKA, MP3, WAV, FLAC, OGG, OGA, Opus, and AAC when ffmpeg is
|
||||
available.
|
||||
- WebM, MKV, MKA, AVI, FLV, MP3, WAV, FLAC, OGG, OGA, Opus, and AAC when
|
||||
ffmpeg is available.
|
||||
|
||||
JPEG image metadata stripping removes targeted metadata segments without
|
||||
re-encoding the entropy coded image scan. PNG and WebP removal preserves pixel
|
||||
@@ -102,6 +126,14 @@ SynthID does not have a public local pixel decoder in this project. The tool can
|
||||
infer likely presence from supported provenance metadata, but after that
|
||||
metadata is removed a local negative result is inconclusive.
|
||||
|
||||
For MP4, MOV, and M4V, `video invisible` or the explicit
|
||||
`video all --invisible` option can regenerate the video through a VAE and strip
|
||||
source metadata. The shipped profile is oracle-certified, but it is not a local
|
||||
decoder. A fresh source-positive, output-negative pair from Gemini's built-in
|
||||
SynthID verifier is an optional per-file audit. A normal Gemini answer may instead
|
||||
infer from a visible logo or metadata; asking it to reinterpret a completed
|
||||
verifier result is not a second oracle run.
|
||||
|
||||
The optional `detect` extra is different: it provides a local decoder for the
|
||||
open DWT-DCT watermark used by some Stable Diffusion, SDXL, and FLUX workflows.
|
||||
That signal is carrier and transformation sensitive, so a negative is still
|
||||
@@ -112,6 +144,7 @@ not a universal clean verdict.
|
||||
| Provider or family | Visible | Invisible path | Metadata or provenance |
|
||||
| --- | --- | --- | --- |
|
||||
| Google Gemini | Sparkle | Diffusion regeneration for SynthID | C2PA and related source signals |
|
||||
| Google Veo video | Veo diamond and legacy text | Oracle-certified VAE removal for SynthID | C2PA and related source signals |
|
||||
| OpenAI image generators | None registered | Diffusion regeneration for supported invisible signals | C2PA and generator provenance |
|
||||
| Stable Diffusion and SDXL | None registered | Diffusion regeneration; optional open decoder | Embedded parameters and text metadata |
|
||||
| FLUX | None registered | Diffusion regeneration; optional open decoder | C2PA for supported sources |
|
||||
@@ -119,7 +152,8 @@ not a universal clean verdict.
|
||||
| Midjourney | None registered | No registered pixel decoder | EXIF, XMP, and IPTC signals |
|
||||
| ByteDance generators | Doubao and Jimeng marks | No registered pixel decoder | TC260 AIGC and supported C2PA signals |
|
||||
| Qwen | Qwen mark | No registered pixel decoder | TC260 AIGC |
|
||||
| Kling | Kling mark | No registered pixel decoder | TC260 AIGC |
|
||||
| Kling | Kling image and video marks | No registered pixel decoder | TC260 AIGC |
|
||||
| Hailuo / MiniMax video | Hailuo composite video label | No registered pixel decoder | TC260 AIGC where present |
|
||||
| Baidu | Baidu mark | No registered pixel decoder | TC260 AIGC |
|
||||
| LibLibAI | LibLibAI mark | No registered pixel decoder | TC260 AIGC |
|
||||
| RunningHub | RunningHub mark | No registered pixel decoder | TC260 AIGC |
|
||||
|
||||
+69
-6
@@ -1,4 +1,4 @@
|
||||
# SynthID-Image: technical reference
|
||||
# SynthID: technical reference
|
||||
|
||||
> Technical research reference. Current package behavior is defined by the
|
||||
> [supported signals](supported-signals.md), [known limitations](known-limitations.md),
|
||||
@@ -6,10 +6,10 @@
|
||||
> historical evidence and should not be read as current CLI defaults.
|
||||
|
||||
This document covers how Google SynthID for images works mechanically, what it
|
||||
survives, what removes it, and the current deployment landscape. It is written
|
||||
for engineers working on watermark detection and removal -- specifically to
|
||||
inform decisions about strength settings, test methodology, and what oracle
|
||||
results mean.
|
||||
survives, what removes it, the external video-verification workflow, and the
|
||||
current deployment landscape. It is written for engineers working on watermark
|
||||
detection and removal -- specifically to inform decisions about strength
|
||||
settings, test methodology, and what oracle results mean.
|
||||
|
||||
Primary sources are cited inline. Marketing-only claims are flagged separately
|
||||
from independently-verified results.
|
||||
@@ -326,6 +326,58 @@ A Google-SynthID image reads clean on openai.com/verify. An OpenAI image reads
|
||||
clean in the Gemini oracle. They are different payloads within the same
|
||||
framework.
|
||||
|
||||
### 3.4 Video verification and attack harness
|
||||
|
||||
Gemini's built-in verification flow reports whether and where it detects Google
|
||||
SynthID in a video. This remains a proprietary oracle: invoke `@synthid`, use
|
||||
the supported content-verification question, and keep every file in a separate
|
||||
new chat. A normal Gemini answer that discusses visual clues or metadata is not
|
||||
an oracle verdict. Nor is an adversarial follow-up that asks the chat model to
|
||||
ignore and reinterpret a completed verifier result.
|
||||
|
||||
The research harness `scripts/video_synthid_sweep.py` tests a VAE regeneration
|
||||
attack without pretending to detect success locally. It emits:
|
||||
|
||||
1. a re-encode control using the same sampled frames, dimensions, frame rate,
|
||||
and codec as the candidates;
|
||||
2. VAE round-trip candidates with one spatial latent-noise field shared across
|
||||
time;
|
||||
3. paired PSNR and motion-compensated temporal-residual metrics;
|
||||
4. an empty oracle column for the external verdict.
|
||||
|
||||
The control is the first oracle submission. If it is not SynthID-positive, stop:
|
||||
the surrounding transcode already changed the verifier result. Only a
|
||||
control-positive, candidate-negative pair is evidence about the regeneration
|
||||
attack. PSNR and temporal residual measure fidelity and flicker, never watermark
|
||||
presence.
|
||||
|
||||
The shipped `video invisible` command and `remove_video_invisible` API reuse the
|
||||
same VAE regeneration mechanism for a complete input sequence. The shipped
|
||||
default is oracle-certified and does not expose a separate verification-status
|
||||
flag. In the 2026-07-29
|
||||
two-carrier calibration, both matched controls were positive in the built-in
|
||||
verifier; the stronger candidate was negative on both, while a weaker
|
||||
candidate was negative on one. A 2026-07-30 `UNAVAILABLE` response came from an
|
||||
ordinary-model follow-up that asked Gemini to reinterpret the already returned
|
||||
verdict and therefore did not invalidate it. The default is a calibrated,
|
||||
content-dependent operating point. A per-file provider check remains an
|
||||
optional audit after provider changes or for unusually important files.
|
||||
|
||||
The 2026-07-31 full-clip calibration used Google's public eight-second Veo
|
||||
off-road sample through the complete product command. The original was detected
|
||||
across 00:00-00:07, the `noise_std=0.10` output remained detected, and the
|
||||
`0.15` output was not detected. The positive `0.10` result proves that the
|
||||
surrounding 512 px / 12 fps / H.264 path did not create the negative result by
|
||||
itself. `0.15` is therefore the shipped default. The tracked manifest
|
||||
`data/evaluations/video-synthid-oracle.csv` records the public source URL,
|
||||
hashes, fidelity metrics, and verdicts without committing generated videos.
|
||||
|
||||
The VAE perturbation follows the general regeneration-attack construction from
|
||||
Zhao et al. The video-specific control and temporal metric are local additions.
|
||||
VideoMarkBench motivates testing frame aggregation and matched perturbations,
|
||||
but it does not evaluate Google's proprietary SynthID, so its findings cannot
|
||||
stand in for the Gemini oracle.
|
||||
|
||||
---
|
||||
|
||||
## 4. Adoption and current state (as of June 2026)
|
||||
@@ -571,7 +623,7 @@ it; (3) **historical engineering conclusion:** this dated run argued for a
|
||||
higher ControlNet strength than the then-current default. That proposal was
|
||||
later superseded. The current resolver intentionally shares the 0.10/0.15
|
||||
ladder between SDXL and ControlNet and uses a separate Qwen ladder; see
|
||||
`noai/watermark_profiles.py`.
|
||||
`_internal/watermark_profiles.py`.
|
||||
Source images are private (faces / product shots), not committed; reproduce on any
|
||||
photoreal + flat-graphic gpt-image pair, varying the seed, and re-checking the
|
||||
oracle.
|
||||
@@ -628,3 +680,14 @@ seed dependent, so reproducible verification requires a fixed seed.
|
||||
|
||||
5. OpenAI. **Verify tool for AI-generated images.** openai.com/research/verify.
|
||||
Accessed 2026-05-31.
|
||||
|
||||
6. Google. **Verify AI-generated images, videos, and audio.**
|
||||
https://support.google.com/gemini/answer/16722517
|
||||
|
||||
7. Zhao et al. (2024). **Invisible Image Watermarks Are Provably Removable
|
||||
Using Generative AI.** NeurIPS 2024, arXiv:2306.01953.
|
||||
https://arxiv.org/abs/2306.01953
|
||||
|
||||
8. Jiang et al. (2025). **VideoMarkBench: Benchmarking Robustness of Video
|
||||
Watermarking.** arXiv:2505.21620.
|
||||
https://arxiv.org/abs/2505.21620
|
||||
|
||||
@@ -216,6 +216,43 @@ for the wrong reason reads exactly like success.
|
||||
the control passes -- but that is Google's claim about their own decoder, not our
|
||||
measurement, so it is a hypothesis to test, not a reason to skip the control.
|
||||
|
||||
### D4. Video candidates require a matched transcode control
|
||||
|
||||
Video experiments add frame sampling, resizing, frame-rate conversion, and a
|
||||
final video codec around the actual attack. `scripts/video_synthid_sweep.py`
|
||||
therefore emits `control.mp4` from the same selected frames and encoder settings
|
||||
as every VAE candidate.
|
||||
|
||||
Verify the control first in a new Gemini chat by invoking `@synthid` and using
|
||||
the supported built-in content-verification question. Continue only when the
|
||||
provider oracle still detects SynthID in it. Verify each candidate in its own
|
||||
new chat. A generic response that discusses visual clues or metadata is not an
|
||||
oracle result. Do not ask the chat model to reinterpret or second-guess the
|
||||
built-in verdict; that follow-up is ordinary model reasoning. Record only the
|
||||
explicit built-in SynthID verification verdict in the generated CSV.
|
||||
|
||||
The harness shares one latent-noise field across the sequence to avoid adding
|
||||
independent frame noise. Its temporal-residual metric is a fidelity check, not a
|
||||
watermark detector.
|
||||
|
||||
The 2026-07-29 two-carrier calibration produced genuine built-in verifier
|
||||
results: both matched controls were positive, the stronger candidate
|
||||
was negative on both carriers, and a weaker candidate was negative on one. On
|
||||
2026-07-30, adversarial follow-up prompts returned `UNAVAILABLE` after asking
|
||||
ordinary Gemini to ignore and reinterpret the detector result. Those follow-ups
|
||||
were mistakenly treated as a stricter oracle; they were not detector reruns.
|
||||
The implementation remains exposed as `video invisible` and
|
||||
`remove_video_invisible`. Its oracle-certified default is the product operating
|
||||
point. A fresh control-positive, candidate-negative pair remains an optional
|
||||
per-file audit after provider changes or for unusually important files.
|
||||
|
||||
The 2026-07-31 full-clip check added a public eight-second Veo carrier. The
|
||||
source and the complete `0.10` product output were both detected, proving the
|
||||
surrounding resize / frame-rate / codec path had not silenced the oracle. The
|
||||
complete `0.15` product output was not detected, so `0.15` became the default.
|
||||
The tracked source and output hashes, fidelity metrics, and verdicts are in
|
||||
`data/evaluations/video-synthid-oracle.csv`; generated media remains untracked.
|
||||
|
||||
## Tier E -- robustness and adversarial inputs
|
||||
|
||||
Malformed and hostile inputs, including truncated files:
|
||||
@@ -277,6 +314,43 @@ separately, outside this repo.
|
||||
|
||||
Run `scripts/real_examples_e2e.py` against representative local inputs before releases that affect image handling. The script must read from `.local-eval/`, write only untracked temporary output, and report behavior without exposing dataset provenance or aggregate private measurements.
|
||||
|
||||
The cheap video seam is covered automatically by
|
||||
`TestVideoVisibleFullClip::test_removes_complete_clip_and_preserves_sequence_and_audio`.
|
||||
It constructs a full synthetic Sora-like MP4 with AAC audio and C2PA
|
||||
provenance, then exercises detection, temporal arbitration, OpenCV fill, real
|
||||
ffmpeg encoding, metadata stripping, audio stream copy, and atomic publication
|
||||
through the public API. A separately encoded clean control supplies the paired
|
||||
frame-to-frame deltas inside the filled region, so the gate detects temporal
|
||||
flicker rather than treating all output motion as an error. The default
|
||||
motion-compensated fill must score strictly below a separately encoded
|
||||
frame-local opt-out on the median paired error, while its high-percentile error
|
||||
cannot regress. The dedicated Linux CI job installs ffmpeg explicitly. Keep real-provider and learned-backend
|
||||
sequence evaluation local because those inputs or model downloads do not
|
||||
belong in the core matrix.
|
||||
|
||||
The local real-provider audit runs one complete clip for every registered video
|
||||
mark. OpenCV and MI-GAN must remove every accepted frame, leave the second-pass
|
||||
detector quiet, preserve source stream starts and duration, and copy AAC
|
||||
packets exactly when present. Run one complete LaMa clip to verify wiring and
|
||||
resource tier; its CPU throughput makes a six-provider online matrix
|
||||
counterproductive. Store generated outputs and the detailed CSV only under
|
||||
`.local-eval/`.
|
||||
|
||||
The same full-clip gate runs the public metadata-only path against its real MP4
|
||||
and verifies unchanged file size, decoded frames, stream properties, and AAC
|
||||
packets. A separate synthetic large-`mdat` test rejects full-source
|
||||
`read_bytes()`, hashes the copied media payload, and mutation-checks both C2PA
|
||||
and TC260 survival.
|
||||
|
||||
A companion full-clip VFR case alternates three frame durations, runs the
|
||||
public visible-removal API, and compares every output display timestamp to the
|
||||
source within one source time-base tick. Its source starts at a non-zero PTS,
|
||||
so the test also verifies retained video/audio stream offsets, container
|
||||
duration, and copied AAC identity. Mutations that disable the timestamped NUT
|
||||
bridge or reset its start PTS must fail this gate.
|
||||
A separate constant-rate clip with the same non-zero start guards the
|
||||
start-offset routing without relying on the VFR branch.
|
||||
|
||||
## Standing gap
|
||||
|
||||
None of this is in `maintain.sh`, and it should not all be -- the sweeps take hours. But
|
||||
|
||||
@@ -38,7 +38,19 @@ pair. On the ISOBMFF path, `blank_ai_exif_tokens` provides the corresponding
|
||||
in-place scrub for supported EXIF values, TC260 AIGC blocks, and the xAI pair.
|
||||
- **China TC260 AIGC label (caught by `AIGC_MARKERS` / `metadata.aigc_label`, surfaced by `identify` as the `aigc` signal):** China-served generators embed an XMP `<TC260:AIGC>{"Label":"1","ContentProducer":...}` block — China's mandatory AI-content labeling (TC260 namespace `tc260.org.cn/ns/AIGC`).
|
||||
|
||||
**Doubao** (ByteDance) uses it (verified on a public issue sample; `ContentProducer` `001191110102MACQD9K64010000`, no C2PA/SynthID/imwatermark — the XMP block is the only signal; GitHub attachment upload did NOT strip it). The same standard is mandatory for Jimeng/Kling/Qwen/Ernie etc., so the one marker covers the whole China-AIGC-labeled ecosystem. `aigc_label` reads **four serializations** through a shared `_parse` helper: the HTML-entity-encoded XMP `TC260:AIGC` block in **either RDF form** — the nested element `<TC260:AIGC>{...}</TC260:AIGC>` (Doubao) or the attribute `TC260:AIGC="{...}"` (**PicWish**, `ContentProducer="picwish"`, verified on compatible samples) — via a container-agnostic raw-byte scan (any JSON object accepted), a raw-JSON PNG `AIGC` tEXt chunk (Doubao also writes the label this way, no namespaced marker at all — confirmed on compatible samples, `ContentProducer="doubao"`), a bare raw-JSON `{"AIGC":{...}}` object embedded in **JPEG EXIF (UserComment)** by some China-served generators, brace-matched from the scan head with `json.JSONDecoder().raw_decode` (no namespaced marker, no PNG chunk — confirmed on compatible samples, `ContentProducer="001191440300708461136T1308L"`), **and** a bare `AIGC{...}` blob (the label glued straight to its JSON, no `"AIGC":` key wrapper) embedded in a **JPEG APP segment near the JFIF header** — confirmed on compatible samples. The two raw-JSON forms are scanned in one loop (`'"AIGC"'` then `AIGC{`) that **falls through on a non-TC260 / undecodable hit instead of returning** — a quoted `"AIGC"` can appear later in an XMP packet while the real label is a bare `AIGC{...}` earlier in the file, so an unconditional early return on the quoted form would shadow the bare form (the exact bug behind the 06-10 misses). All three generic forms (the PNG chunk, the bare `{"AIGC":...}` object, and the bare `AIGC{...}` blob) are gated on at least one TC260 field (`_TC260_FIELDS`) so a generic `AIGC` key cannot false-positive; the namespaced XMP element is unambiguous and needs no gate. `_TC260_FIELDS` covers **two schemas**: the producer-side one (`Label` / `ContentProducer` / `ProduceID` / `ContentPropagator` / `PropagateID`, Doubao and most China gens) and the **service-provider** one (`ServiceProvider` / `ServiceUser`, plus generic `Time` / `ContentId` which are NOT gated on) — **Tencent Cloud's** AIGC variant (`ServiceProvider` = `腾讯云`), embedded in **EXIF `ImageDescription`**, verified on compatible samples. In `identify`, `aigc` fires on the parsed label **or** the `AIGC_MARKERS` byte scan (the latter preserves the laundering-tell case where the JSON payload is truncated).
|
||||
**Doubao** (ByteDance) uses it (verified on a public issue sample; `ContentProducer` `001191110102MACQD9K64010000`, no C2PA/SynthID/imwatermark — the XMP block is the only signal; GitHub attachment upload did NOT strip it). The same standard is mandatory for Jimeng/Kling/Qwen/Ernie etc., so the one marker covers the whole China-AIGC-labeled ecosystem. `aigc_label` reads **four image serializations** through a shared `_parse` helper: the HTML-entity-encoded XMP `TC260:AIGC` block in **either RDF form** — the nested element `<TC260:AIGC>{...}</TC260:AIGC>` (Doubao) or the attribute `TC260:AIGC="{...}"` (**PicWish**, `ContentProducer="picwish"`, verified on compatible samples) — via a container-agnostic raw-byte scan (any JSON object accepted), a raw-JSON PNG `AIGC` tEXt chunk (Doubao also writes the label this way, no namespaced marker at all — confirmed on compatible samples, `ContentProducer="doubao"`), a bare raw-JSON `{"AIGC":{...}}` object embedded in **JPEG EXIF (UserComment)** by some China-served generators, brace-matched from the scan head with `json.JSONDecoder().raw_decode` (no namespaced marker, no PNG chunk — confirmed on compatible samples, `ContentProducer="001191440300708461136T1308L"`), **and** a bare `AIGC{...}` blob (the label glued straight to its JSON, no `"AIGC":` key wrapper) embedded in a **JPEG APP segment near the JFIF header** — confirmed on compatible samples. The two raw-JSON forms are scanned in one loop (`'"AIGC"'` then `AIGC{`) that **falls through on a non-TC260 / undecodable hit instead of returning** — a quoted `"AIGC"` can appear later in an XMP packet while the real label is a bare `AIGC{...}` earlier in the file, so an unconditional early return on the quoted form would shadow the bare form (the exact bug behind the 06-10 misses). Native MP4/MOV is a fifth serialization: TC260-PG-20257A stores an `AIGC` key in `moov.udta.meta.keys` and the raw JSON in the matching `ilst` item. The seeking parser reaches a tail `moov` without reading `mdat`; removal replaces the key with `free` and blanks the validated value at the same length so every box size and stream offset stays fixed. All generic forms are gated on at least one TC260 field (`TC260_AIGC_FIELDS`) so a generic `AIGC` key cannot false-positive; the namespaced XMP element is unambiguous and needs no gate. `TC260_AIGC_FIELDS` covers **two schemas**: the producer-side one (`Label` / `ContentProducer` / `ProduceID` / `ContentPropagator` / `PropagateID`, Doubao and most China gens) and the **service-provider** one (`ServiceProvider` / `ServiceUser`, plus generic `Time` / `ContentId` which are NOT gated on) — **Tencent Cloud's** AIGC variant (`ServiceProvider` = `腾讯云`), embedded in **EXIF `ImageDescription`**, verified on compatible samples. In `identify`, `aigc` fires on the parsed label **or** the `AIGC_MARKERS` byte scan (the latter preserves the laundering-tell case where the JSON payload is truncated).
|
||||
|
||||
Native MKV/WebM is a sixth serialization. TC260-PG-20257A stores
|
||||
`TagName=AIGC` and the raw JSON `TagString` in
|
||||
`Segment.Tags.Tag.SimpleTag`. The bounded EBML reader skips cluster payloads;
|
||||
the existing ffmpeg stream-copy path removes the tags without transcoding.
|
||||
|
||||
The same [TC260 video guide](https://www.tc260.org.cn/portal/article/303/4061772dcf684d8a96f395a4298e9e53)
|
||||
defines two more native serializations. AVI stores an `AIGC` child in
|
||||
`LIST/INFO`; FLV stores an AMF0 `AIGC` string under `script.onMetaData`. The
|
||||
bounded RIFF and FLV readers validate the JSON field set and skip media
|
||||
payloads. Removal remuxes either container through ffmpeg with stream copy.
|
||||
|
||||
- **HuggingFace-hosted job (caught by `metadata.huggingface_job`, surfaced by `identify` as the `hf_job` signal, MEDIUM confidence):** HuggingFace Jobs / Spaces can stamp generated PNGs with an `hf-job-id` tEXt chunk holding the job UUID. It marks the *hosting job*, not a model, so it lifts an Unknown verdict to a tentative AI via `hf_only` but never overrides a hard metadata signal. `_HF_JOB_CAVEAT` states the limit. Removal drops the chunk through the PNG metadata whitelist.
|
||||
- **No detectable signal on some downloads:** Recraft exports and some hosted
|
||||
FLUX surfaces can arrive without a supported local signal. Midjourney samples
|
||||
@@ -49,13 +61,19 @@ in-place scrub for supported EXIF values, TC260 AIGC blocks, and the xAI pair.
|
||||
- **C2PA 2.4 "Durable Content Credentials" (April 2026; verified against the spec) raise the bar for metadata stripping.** 2.4 defines soft bindings (an invisible watermark or a content fingerprint) plus a server-side manifest repository and a new `c2pa.repository-receipt` assertion. Per the spec: "if a C2PA manifest is removed from an asset, but a copy of that manifest remains in a provenance store elsewhere, the manifest and asset may be matched using available soft bindings." So our local `metadata --remove` deletes the *embedded* manifest, but a fingerprint/watermark soft binding can still re-link the image to its manifest in a repository server-side. Stripping the file is becoming necessary-but-not-sufficient against durable provenance. (Our parsers target the stable embedded-manifest format documented in C2PA 2.1 §11; that format is unchanged in 2.4 -- the new pieces are repository/soft-binding infra, not the on-file box layout, so no parser change is implied.) Spec: https://spec.c2pa.org/specifications/specifications/2.4/specs/C2PA_Specification.html We now READ the soft-binding `alg` (`C2PA_SOFT_BINDINGS` / `soft_binding_vendors_in`) to name the forensic-watermark vendor, and locally DECODE the one open scheme, Adobe TrustMark (`trustmark_detector`); the rest (Digimarc/Imatag/Steg.AI/...) stay name-only (proprietary decoders).
|
||||
- **Built in the dated batch:** soft-binding vendor detection, IPTC Photo
|
||||
Metadata AI-disclosure fields, C2PA detection and stripping for supported
|
||||
ISOBMFF video, and the optional Adobe TrustMark decoder. Visible video-logo
|
||||
removal and proprietary audio-watermark detection remain outside the package.
|
||||
ISOBMFF video, the optional Adobe TrustMark decoder, and temporally stabilized
|
||||
visible Sora, Veo, Seedance, Dola, Hailuo, and Kling removal. Other visible
|
||||
video logos and proprietary audio-watermark detection remain outside the
|
||||
package.
|
||||
Metadata stripping for supported audio containers is a separate implemented
|
||||
path.
|
||||
|
||||
**Box detection window — now handled (v0.6.8):** detection no longer relies on a fixed first-MB read. `metadata.scan_head(path, size)` reads the first `size` bytes and, for ISOBMFF, appends the payloads of late provenance boxes found by `isobmff.scan_c2pa_region` (a file-seeking top-level box walker that skips past `mdat` by size without reading it), so a C2PA/AIGC/IPTC manifest placed AFTER a large `mdat` in a streaming/non-faststart MP4 is now caught. Every C2PA/marker byte scan (`has_ai_metadata`, `aigc_label`, `iptc_ai_system`, `synthid_source`, `exif_generator` XMP, `get_ai_metadata` soft-binding, and `identify`) goes through `scan_head`; it is behavior-neutral for non-ISOBMFF inputs (exactly `f.read(size)`).
|
||||
|
||||
Native TC260 MP4/MOV tags do not live in those top-level provenance boxes.
|
||||
`tc260_aigc_payloads` separately seeks through `moov.udta.meta.keys/ilst`, so
|
||||
the normative tag is also found when a large `mdat` precedes `moov`.
|
||||
|
||||
**Meta-box XMP and EXIF removal are handled in place:** an AI-label XMP packet
|
||||
stored as a meta-box `mime` item is blanked by
|
||||
`isobmff.blank_ai_xmp_packets`. Supported EXIF items are handled by
|
||||
@@ -69,6 +87,42 @@ against primary sources before adding jurisdiction-specific claims.
|
||||
|
||||
**Google Gemini visible "sparkle" -- tier-dependent, and spec-undocumented by Google.** Google primary sources (the Nano Banana Pro blog and the gemini.google image-generation page, both WebFetch-verified) confirm Gemini images carry BOTH the invisible SynthID (on ALL Google-AI media) AND a visible sparkle, but the visible mark is **tier-gated**: applied for FREE and Google AI **Pro** users, and **REMOVED** for Google AI **Ultra** subscribers, inside **Google AI Studio**, and on **API / dev** output. So a Google-C2PA image with NO visible sparkle is expected (Ultra / API), not evidence it is clean -- this reinforces the `identify` "no visible mark != clean" rule. The ONLY official verifier is the SynthID flow (upload to the Gemini app, ask if it is AI-generated), which reads the INVISIBLE mark; there is **no official visible-sparkle detector**, and Google publishes **no** glyph geometry / size / opacity / color / locale / placement spec. So our capture-based sparkle template is the only source of truth and cannot be validated against a vendor spec -- keep reverse-engineering from real captures (do not expect a published spec).
|
||||
|
||||
**Google Veo video marks use two incompatible visible designs.** Public raw
|
||||
clips verify the current four-point diamond and the legacy bottom-right `Veo`
|
||||
text. The independent
|
||||
[VeoWatermarkRemover](https://github.com/allenk/VeoWatermarkRemover) project
|
||||
reports the same current-versus-legacy split, multiple output layouts, and the
|
||||
need for cross-frame position agreement. Our implementation copies no logo
|
||||
pixels or alpha maps from that project: it uses two synthetic silhouettes,
|
||||
known-layout searches plus a strong relocated-diamond fallback, and a separate
|
||||
temporal arbiter calibrated against raw watermarked clips and clean API exports.
|
||||
|
||||
**ByteDance video surfaces use distinct visible labels.** Public Seedance
|
||||
showcase clips contain a fixed rounded box with `AI`, while the Dola sample in
|
||||
[issue #16](https://github.com/wiltodelta/remove-ai-watermarks/issues/16) uses
|
||||
fixed `Dola AI` text. The independent
|
||||
[Seedance remover](https://github.com/SamurAIGPT/seedance-2.0-watermark-remover)
|
||||
estimates a static corner from a temporal mean frame and edge density. Our
|
||||
implementation instead matches provider-specific synthetic silhouettes on
|
||||
every frame, then requires an anchored temporal run. This extra anchor check
|
||||
was necessary because a moving clean scene detail could retain enough adjacent
|
||||
overlap to pass a recurrence-only gate.
|
||||
|
||||
**Hailuo and Kling use larger fixed composite labels.** Verified Hailuo exports
|
||||
carry a lower-edge waveform, `MINIMAX`, separator, Hailuo ring, and
|
||||
`hailuo AI` text. Verified Kling exports carry a bottom-right swirl,
|
||||
`KLING AI`, a changing version suffix, and sometimes `PRO`. The detectors use
|
||||
only synthetic primitives and fonts. Hailuo expands the matched core to cover
|
||||
the complete composite. Kling combines a version-independent text core with a
|
||||
synthetic ring rescue, then requires the recurring candidate to reach the
|
||||
expected frame edge and contain enough bright low-saturation pixels. Those
|
||||
extra gates were added after clean Luma and PixVerse scene details passed shape
|
||||
and temporal recurrence alone. The generic
|
||||
[WatermarkRemover-AI](https://github.com/D-Ogi/WatermarkRemover-AI) project
|
||||
instead uses Florence-2 to identify arbitrary watermarks before LaMa
|
||||
inpainting. That is broader, but it carries a much heavier model and a less
|
||||
auditable detection boundary than the provider-specific synthetic path here.
|
||||
|
||||
**The faint-visible-mark precision/recall wall is fundamental, not a heuristic artifact.** The visible-watermark-detection literature has moved to LEARNED segmentation / object-detection (WDNet WACV'21 arXiv:2012.07616; SLBR ACM MM'21, open code+weights; the PRCV'18 large-scale detector; Su et al. survey 2025), but three verified findings bound what a learned detector actually buys: (1) a claim that a confidence threshold "cleanly separates" true from false matches even with a learned CNN front-end was **REFUTED** in verification (arXiv:1705.08593) -- the precision/recall wall persists even with learned features. (2) Learned detectors need a LARGE, pattern-diverse labeled dataset trained on synthetic composites (PRCV'18: 60k images / 80 watermark classes; CLWD: 60k / 160 marks), and off-distribution degradation is a documented real axis (models trained on limited-pattern LVW transfer worse; diversity of training patterns drives generalization). (3) Inference is cheap (WDNet ~8 ms at 256x256) -- the cost is the data pipeline, not runtime. Net: a learned detector shifts the frontier but does NOT remove the wall; for a SINGLE mark the cheapest next step is a small patch classifier (real-sparkle vs false-positive) on top of the existing NCC localizer, not a full segmentation model. SLBR is a ready baseline. The current NCC + false-positive gate (core-ring brightness margin + gradient-NCC crispness + white-core saturation) is a sound operating point, and the residual miss is the information-theoretic wall the literature confirms.
|
||||
|
||||
**Visible-mark landscape beyond the registry.** Meta stamps a visible "Imagined with AI" mark (bottom-LEFT, a small symbol) on its OWN Meta AI / "Imagine" output; for third-party images it relies on C2PA / IPTC, not a visible mark. Samsung Galaxy AI additionally uses a **four-star icon** variant in a corner alongside the localized text wordmark `samsung_engine` calibrates (only the Italian text variant is covered) -- the icon is a distinct, uncovered variant. Every source agrees visible + metadata marks are trivially removable (crop / screenshot, ~2 s), which is the tool's premise.
|
||||
|
||||
@@ -47,11 +47,12 @@ tests:
|
||||
|
||||
about:
|
||||
homepage: https://github.com/wiltodelta/remove-ai-watermarks
|
||||
summary: Remove visible and invisible AI watermarks from images
|
||||
summary: Inspect and strip AI provenance metadata from media
|
||||
description: |
|
||||
Inspect and strip AI-provenance metadata (C2PA, EXIF, IPTC, and PNG text
|
||||
chunks) from images. Optional pip extras add visible watermark removal,
|
||||
SynthID diffusion removal, and additional invisible-watermark detectors.
|
||||
chunks) from images and supported video containers. Optional pip extras add
|
||||
visible watermark removal, video processing, SynthID diffusion removal, and
|
||||
additional invisible-watermark detectors.
|
||||
license: Apache-2.0
|
||||
license_file: LICENSE
|
||||
repository: https://github.com/wiltodelta/remove-ai-watermarks
|
||||
|
||||
+28
-9
@@ -1,7 +1,7 @@
|
||||
[project]
|
||||
name = "remove-ai-watermarks"
|
||||
version = "0.22.0"
|
||||
description = "AI watermark remover: strip visible and invisible AI watermarks (Gemini / Nano Banana sparkle, SynthID) and provenance metadata (C2PA, EXIF) from images"
|
||||
description = "AI watermark remover for visible, invisible, and provenance marks in images and video"
|
||||
readme = "README.md"
|
||||
requires-python = ">=3.10.1"
|
||||
license = {text = "Apache-2.0"}
|
||||
@@ -51,7 +51,7 @@ dependencies = [
|
||||
"python-dotenv>=1.0.0",
|
||||
# Official C2PA reader (Content Authenticity Initiative, MIT/Apache-2.0). The
|
||||
# primary, spec-tracking manifest parser for the identify/metadata path; the
|
||||
# hand-rolled caBX/CBOR scanner in noai/c2pa.py is kept only as a fallback for
|
||||
# hand-rolled caBX/CBOR scanner in _internal/c2pa.py is kept only as a fallback for
|
||||
# synthetic/partial blobs the validator rejects. Binary wheel (Rust), but the
|
||||
# import is light (no torch/numpy) so it fits the dependency-light identify
|
||||
# host. Prebuilt wheels cover the full CI matrix (linux/macos/windows).
|
||||
@@ -69,6 +69,14 @@ heif = [
|
||||
"pillow-heif>=0.13.0",
|
||||
]
|
||||
visible = ["remove-ai-watermarks[pixels]"]
|
||||
# Video visible removal uses the shared pixel runtime. PyAV packetizes processed
|
||||
# VFR frames with explicit PTS before system ffmpeg encodes them. PyAV 18 requires
|
||||
# Python 3.11; the 16.x wheel line still covers Python 3.10.
|
||||
video = [
|
||||
"remove-ai-watermarks[visible]",
|
||||
"av>=16,<17; python_version < '3.11'",
|
||||
"av>=18,<19; python_version >= '3.11'",
|
||||
]
|
||||
# Open DWT-DCT watermarks used by Stable Diffusion / SDXL / FLUX. The in-tree
|
||||
# decoder avoids the upstream invisible-watermark package's mandatory torch and
|
||||
# non-headless OpenCV dependencies.
|
||||
@@ -152,7 +160,7 @@ esrgan = [
|
||||
"spandrel>=0.3.0",
|
||||
]
|
||||
dev = [
|
||||
"remove-ai-watermarks[visible]",
|
||||
"remove-ai-watermarks[video]",
|
||||
"remove-ai-watermarks[detect]",
|
||||
"pytest>=8.0.0",
|
||||
"pytest-cov>=4.1.0",
|
||||
@@ -166,7 +174,7 @@ dev = [
|
||||
"uv-outdated>=0.1.0; python_version >= '3.12'",
|
||||
"uv-secure>=0.12.0; python_version >= '3.12'",
|
||||
]
|
||||
all = ["remove-ai-watermarks[visible,heif,detect,trustmark,diffusion,qwen-zimage,lama,migan,esrgan]"]
|
||||
all = ["remove-ai-watermarks[video,heif,detect,trustmark,diffusion,qwen-zimage,lama,migan,esrgan]"]
|
||||
|
||||
# PyTorch Intel-GPU (XPU) wheel index. ``explicit = true`` keeps it inert for
|
||||
# the default CPU/CUDA install: uv consults it only when a torch install
|
||||
@@ -191,9 +199,20 @@ build-backend = "hatchling.build"
|
||||
packages = ["src/remove_ai_watermarks"]
|
||||
|
||||
[tool.hatch.build.targets.sdist]
|
||||
# Keep the source distribution small: ship the package and metadata, not
|
||||
# calibration or evaluation data under data/. The wheel ships only src/.
|
||||
exclude = ["/data"]
|
||||
# Keep the source distribution small and public-safe: ship tracked source and
|
||||
# metadata, not corpora or local research/session artifacts. The wheel ships
|
||||
# only src/.
|
||||
include = [
|
||||
"/src",
|
||||
"/LICENSE",
|
||||
"/README.md",
|
||||
"/pyproject.toml",
|
||||
]
|
||||
exclude = [
|
||||
"/data",
|
||||
"/tmp",
|
||||
"/.sc",
|
||||
]
|
||||
|
||||
[tool.pytest.ini_options]
|
||||
testpaths = ["tests"]
|
||||
@@ -216,8 +235,8 @@ ignore = [
|
||||
[tool.ruff.lint.per-file-ignores]
|
||||
"scripts/*.py" = ["G004", "S108", "S310", "T20"]
|
||||
"tests/*.py" = ["ANN", "S101", "S105", "S106", "S108"]
|
||||
"src/remove_ai_watermarks/noai/watermark_remover.py" = ["S603", "S606", "S607", "T201"] # subprocess calls for auto-install/CUDA fix
|
||||
"src/remove_ai_watermarks/noai/c2pa.py" = ["S110"] # try-except-pass for corrupt file handling
|
||||
"src/remove_ai_watermarks/_internal/watermark_remover.py" = ["S603", "S606", "S607"] # nvidia-smi capability probe
|
||||
"src/remove_ai_watermarks/_internal/c2pa.py" = ["S110"] # try-except-pass for corrupt file handling
|
||||
|
||||
[tool.ruff.format]
|
||||
quote-style = "double"
|
||||
|
||||
@@ -32,7 +32,7 @@ from pathlib import Path
|
||||
import click
|
||||
import numpy as np
|
||||
|
||||
from remove_ai_watermarks.noai.constants import SUPPORTED_FORMATS
|
||||
from remove_ai_watermarks._internal.constants import SUPPORTED_FORMATS
|
||||
|
||||
log = logging.getLogger(__name__)
|
||||
|
||||
|
||||
@@ -590,8 +590,8 @@ def _diffusion_rows(r: Runner, tmp: Path, doubao: Path) -> None:
|
||||
try:
|
||||
import numpy as np
|
||||
|
||||
from remove_ai_watermarks._internal.watermark_remover import WatermarkRemover
|
||||
from remove_ai_watermarks.image_io import imread
|
||||
from remove_ai_watermarks.noai.watermark_remover import WatermarkRemover
|
||||
|
||||
src = imread(str(mj))
|
||||
h, w = src.shape[:2]
|
||||
|
||||
@@ -29,7 +29,7 @@ import click
|
||||
from _plain_console import Console, Table
|
||||
from PIL import Image
|
||||
|
||||
from remove_ai_watermarks.noai.c2pa import extract_c2pa_info
|
||||
from remove_ai_watermarks._internal.c2pa import extract_c2pa_info
|
||||
|
||||
log = logging.getLogger(__name__)
|
||||
console = Console()
|
||||
|
||||
@@ -0,0 +1,224 @@
|
||||
"""Build oracle-gated video regeneration candidates for SynthID research.
|
||||
|
||||
This is a research harness, not a shipped removal command. Google does not
|
||||
publish a local video SynthID decoder, so the script cannot label a candidate as
|
||||
clean. It produces:
|
||||
|
||||
* a re-encode control with the same duration, frame rate, dimensions, and codec;
|
||||
* one VAE-regenerated video per requested latent-noise level;
|
||||
* paired fidelity and temporal-residual measurements;
|
||||
* a CSV column for the external Gemini SynthID verdict.
|
||||
|
||||
The control is load-bearing. If it reads clean, the experiment is invalid:
|
||||
resize, frame-rate conversion, or H.264 compression already silenced the oracle,
|
||||
so a VAE candidate cannot be credited with removal.
|
||||
|
||||
The regeneration attack follows the general encode, perturb, reconstruct family
|
||||
from WatermarkAttacker (NeurIPS 2024). A single spatial latent-noise sample is
|
||||
shared by every frame. Independent per-frame noise creates avoidable flicker and
|
||||
does not test the video-specific question.
|
||||
|
||||
Run with the project's GPU extra:
|
||||
|
||||
uv run --extra gpu python scripts/video_synthid_sweep.py input.mp4 -o out/
|
||||
|
||||
Then upload ``control.mp4`` and each candidate in separate Gemini chats, invoke
|
||||
the built-in SynthID verifier (``@synthid``), and use the question printed by
|
||||
the script. Do not follow the verdict with an adversarial prompt asking the chat
|
||||
model to reinterpret the detector: that switches back to ordinary reasoning.
|
||||
Only a control-positive, candidate-negative pair from the built-in verifier is
|
||||
removal evidence.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import csv
|
||||
import hashlib
|
||||
import logging
|
||||
from pathlib import Path
|
||||
from typing import TYPE_CHECKING
|
||||
|
||||
import click
|
||||
import cv2
|
||||
import numpy as np
|
||||
|
||||
from remove_ai_watermarks.video_invisible import (
|
||||
_decode_frame_latents,
|
||||
_encode_frame_latents,
|
||||
_fit_size,
|
||||
_pick_device,
|
||||
_shared_latent_noise,
|
||||
build_temporal_reference,
|
||||
encode_video_frames,
|
||||
paired_psnr,
|
||||
read_sampled_frames,
|
||||
temporal_residual_ratio,
|
||||
)
|
||||
from remove_ai_watermarks.video_synthid import (
|
||||
DEFAULT_VIDEO_SYNTHID_FPS,
|
||||
DEFAULT_VIDEO_SYNTHID_LONG_SIDE,
|
||||
DEFAULT_VIDEO_SYNTHID_VAE,
|
||||
VIDEO_SYNTHID_LATENT_MULTIPLE,
|
||||
VIDEO_SYNTHID_VERIFICATION_PROMPT,
|
||||
)
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from collections.abc import Sequence
|
||||
|
||||
log = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def _parse_noise_levels(values: str) -> tuple[float, ...]:
|
||||
levels = tuple(float(value.strip()) for value in values.split(",") if value.strip())
|
||||
if not levels:
|
||||
raise click.BadParameter("At least one noise level is required")
|
||||
if any(not 0.0 <= value <= 1.0 for value in levels):
|
||||
raise click.BadParameter("Noise levels must be between 0 and 1")
|
||||
return levels
|
||||
|
||||
|
||||
def _sha256(path: Path) -> str:
|
||||
digest = hashlib.sha256()
|
||||
with path.open("rb") as stream:
|
||||
for chunk in iter(lambda: stream.read(1024 * 1024), b""):
|
||||
digest.update(chunk)
|
||||
return digest.hexdigest()
|
||||
|
||||
|
||||
def _write_manifest(output_dir: Path, rows: Sequence[dict[str, str]]) -> Path:
|
||||
path = output_dir / "sweep.csv"
|
||||
fieldnames = [
|
||||
"variant",
|
||||
"noise_std",
|
||||
"psnr_db",
|
||||
"temporal_residual_ratio",
|
||||
"file",
|
||||
"sha256",
|
||||
"synthid_oracle",
|
||||
]
|
||||
with path.open("w", newline="", encoding="utf-8") as stream:
|
||||
writer = csv.DictWriter(stream, fieldnames=fieldnames)
|
||||
writer.writeheader()
|
||||
writer.writerows(rows)
|
||||
return path
|
||||
|
||||
|
||||
@click.command()
|
||||
@click.argument("source", type=click.Path(exists=True, dir_okay=False, path_type=Path))
|
||||
@click.option("-o", "--output-dir", required=True, type=click.Path(file_okay=False, path_type=Path))
|
||||
@click.option("--noise-levels", default="0,0.05,0.1,0.15", show_default=True)
|
||||
@click.option("--duration", type=click.FloatRange(min=0.1), default=2.0, show_default=True)
|
||||
@click.option("--fps", type=click.FloatRange(min=1.0), default=DEFAULT_VIDEO_SYNTHID_FPS, show_default=True)
|
||||
@click.option(
|
||||
"--long-side",
|
||||
type=click.IntRange(min=VIDEO_SYNTHID_LATENT_MULTIPLE),
|
||||
default=DEFAULT_VIDEO_SYNTHID_LONG_SIDE,
|
||||
show_default=True,
|
||||
)
|
||||
@click.option("--batch-size", type=click.IntRange(min=1), default=4, show_default=True)
|
||||
@click.option("--seed", type=int, default=0, show_default=True)
|
||||
@click.option("--model", default=DEFAULT_VIDEO_SYNTHID_VAE, show_default=True)
|
||||
@click.option("--device", type=click.Choice(["auto", "cuda", "mps", "cpu"]), default="auto", show_default=True)
|
||||
def main(
|
||||
source: Path,
|
||||
output_dir: Path,
|
||||
noise_levels: str,
|
||||
duration: float,
|
||||
fps: float,
|
||||
long_side: int,
|
||||
batch_size: int,
|
||||
seed: int,
|
||||
model: str,
|
||||
device: str,
|
||||
) -> None:
|
||||
"""Generate VAE video candidates from the prefix of SOURCE."""
|
||||
logging.basicConfig(level=logging.INFO, format="%(message)s")
|
||||
import torch
|
||||
from diffusers import AutoencoderKL
|
||||
|
||||
levels = _parse_noise_levels(noise_levels)
|
||||
capture = cv2.VideoCapture(str(source))
|
||||
if not capture.isOpened():
|
||||
raise click.ClickException(f"Could not open video: {source}")
|
||||
width = round(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
|
||||
height = round(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
|
||||
capture.release()
|
||||
size = _fit_size(width, height, long_side)
|
||||
frames, effective_fps = read_sampled_frames(source, duration=duration, output_fps=fps, size=size)
|
||||
output_dir.mkdir(parents=True, exist_ok=True)
|
||||
control_path = output_dir / "control.mp4"
|
||||
encode_video_frames(frames, source, control_path, fps=effective_fps)
|
||||
rows: list[dict[str, str]] = [
|
||||
{
|
||||
"variant": "control",
|
||||
"noise_std": "",
|
||||
"psnr_db": "inf",
|
||||
"temporal_residual_ratio": "1",
|
||||
"file": control_path.name,
|
||||
"sha256": _sha256(control_path),
|
||||
"synthid_oracle": "",
|
||||
}
|
||||
]
|
||||
|
||||
resolved_device = _pick_device(device)
|
||||
dtype = torch.float16 if resolved_device == "cuda" else torch.float32
|
||||
log.info("Loading %s on %s", model, resolved_device)
|
||||
vae = AutoencoderKL.from_pretrained(model, torch_dtype=dtype).to(resolved_device)
|
||||
vae.eval()
|
||||
vae.enable_slicing()
|
||||
|
||||
log.info("Encoding source frames")
|
||||
latent_batches = _encode_frame_latents(
|
||||
frames,
|
||||
vae=vae,
|
||||
device=resolved_device,
|
||||
batch_size=batch_size,
|
||||
)
|
||||
first_latents = latent_batches[0]
|
||||
shared_noise = _shared_latent_noise(
|
||||
first_latents.shape[1:],
|
||||
seed=seed,
|
||||
device=resolved_device,
|
||||
dtype=first_latents.dtype,
|
||||
)
|
||||
reference_stack = np.stack(frames)
|
||||
temporal_maps, temporal_baseline = build_temporal_reference(frames)
|
||||
for level in levels:
|
||||
log.info("Decoding latent noise %.4f", level)
|
||||
regenerated = _decode_frame_latents(
|
||||
latent_batches,
|
||||
vae=vae,
|
||||
noise_std=level,
|
||||
shared_noise=shared_noise,
|
||||
)
|
||||
output_path = output_dir / f"vae-noise-{level:.4f}.mp4"
|
||||
encode_video_frames(
|
||||
regenerated,
|
||||
source,
|
||||
output_path,
|
||||
fps=effective_fps,
|
||||
)
|
||||
psnr = paired_psnr(reference_stack, np.stack(regenerated))
|
||||
temporal_ratio = temporal_residual_ratio(regenerated, temporal_maps, temporal_baseline)
|
||||
rows.append(
|
||||
{
|
||||
"variant": "vae",
|
||||
"noise_std": f"{level:.4f}",
|
||||
"psnr_db": f"{psnr:.4f}",
|
||||
"temporal_residual_ratio": f"{temporal_ratio:.4f}",
|
||||
"file": output_path.name,
|
||||
"sha256": _sha256(output_path),
|
||||
"synthid_oracle": "",
|
||||
}
|
||||
)
|
||||
|
||||
manifest = _write_manifest(output_dir, rows)
|
||||
log.info("Wrote %s", manifest)
|
||||
log.info(
|
||||
"Verify control.mp4 first with Gemini's built-in SynthID verifier and this question: %s",
|
||||
VIDEO_SYNTHID_VERIFICATION_PROMPT,
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -38,7 +38,7 @@ sys.path.insert(0, str(Path(__file__).parent.parent))
|
||||
|
||||
# The package's own format set. An inlined copy here silently skipped .heif, which
|
||||
# CLAUDE.md documents as supported.
|
||||
from remove_ai_watermarks.noai.constants import SUPPORTED_FORMATS as _EXTS
|
||||
from remove_ai_watermarks._internal.constants import SUPPORTED_FORMATS as _EXTS
|
||||
|
||||
REPO = Path(__file__).resolve().parents[1]
|
||||
CORPUS = REPO / ".local-eval" / "originals"
|
||||
|
||||
@@ -6,6 +6,13 @@ High-level API (lazy, so ``import remove_ai_watermarks`` stays cheap)::
|
||||
raiw.remove_visible("in.png", "out.png") # clean a file (provenance auto)
|
||||
result, removed = raiw.remove_visible(bgr_array) # array -> array
|
||||
raiw.visible_provenance("in.png") # -> frozenset of confirmed vendors
|
||||
raiw.identify_video("in.mp4") # -> VideoProvenanceReport
|
||||
raiw.inspect_video_metadata("in.mp4") # -> VideoMetadataReport
|
||||
raiw.remove_video_all("in.mp4", "out.mp4") # visible + verified metadata
|
||||
raiw.remove_video_batch("videos", "videos_clean") # complete per-file results
|
||||
raiw.remove_video_metadata("in.mp4", "out.mp4") # verified metadata strip
|
||||
raiw.remove_video_invisible("in.mp4", "out.mp4") # oracle-certified SynthID removal
|
||||
raiw.remove_video_visible("in.mp4", "out.mp4") # stable visible video-mark removal
|
||||
|
||||
For a provenance verdict use the ``identify`` submodule::
|
||||
|
||||
@@ -27,10 +34,30 @@ _warnings.filterwarnings("ignore", message=r".*ImageProcessorFast.*")
|
||||
|
||||
__version__ = "0.22.0"
|
||||
|
||||
__all__ = ["__version__", "remove_visible", "visible_provenance"]
|
||||
__all__ = [
|
||||
"__version__",
|
||||
"identify_video",
|
||||
"inspect_video_metadata",
|
||||
"remove_video_all",
|
||||
"remove_video_batch",
|
||||
"remove_video_invisible",
|
||||
"remove_video_metadata",
|
||||
"remove_video_visible",
|
||||
"remove_visible",
|
||||
"visible_provenance",
|
||||
]
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from remove_ai_watermarks.api import remove_visible, visible_provenance
|
||||
from remove_ai_watermarks.video import (
|
||||
identify_video,
|
||||
inspect_video_metadata,
|
||||
remove_video_all,
|
||||
remove_video_batch,
|
||||
remove_video_invisible,
|
||||
remove_video_metadata,
|
||||
remove_video_visible,
|
||||
)
|
||||
|
||||
|
||||
def __getattr__(name: str) -> object:
|
||||
@@ -40,4 +67,16 @@ def __getattr__(name: str) -> object:
|
||||
from remove_ai_watermarks import api
|
||||
|
||||
return getattr(api, name)
|
||||
if name in (
|
||||
"identify_video",
|
||||
"inspect_video_metadata",
|
||||
"remove_video_all",
|
||||
"remove_video_batch",
|
||||
"remove_video_invisible",
|
||||
"remove_video_metadata",
|
||||
"remove_video_visible",
|
||||
):
|
||||
from remove_ai_watermarks import video
|
||||
|
||||
return getattr(video, name)
|
||||
raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
|
||||
|
||||
+5
-7
@@ -1,10 +1,8 @@
|
||||
"""Vendored noai-watermark code for invisible watermark removal.
|
||||
|
||||
Original: https://github.com/mertizci/noai-watermark (MIT License)
|
||||
"""Compatibility namespace for metadata and regeneration helpers.
|
||||
|
||||
The public API (``WatermarkRemover`` / ``remove_watermark`` / ``remove_ai_metadata``)
|
||||
is exposed **lazily** via PEP 562 ``__getattr__``: importing a light submodule
|
||||
(e.g. ``noai.c2pa`` / ``noai.constants`` from ``identify``) must NOT eagerly pull
|
||||
(e.g. ``_internal.c2pa`` / ``_internal.constants`` from ``identify``) must NOT eagerly pull
|
||||
``watermark_remover``, which imports torch + diffusers at module top. Keeping this
|
||||
lazy is what lets ``import remove_ai_watermarks.identify`` stay cheap (~36 MB, no
|
||||
torch) even in a full install where the ``diffusion`` extra is present --
|
||||
@@ -17,8 +15,8 @@ from __future__ import annotations
|
||||
from typing import TYPE_CHECKING
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from remove_ai_watermarks._internal.watermark_remover import WatermarkRemover, remove_watermark
|
||||
from remove_ai_watermarks.metadata import remove_ai_metadata
|
||||
from remove_ai_watermarks.noai.watermark_remover import WatermarkRemover, remove_watermark
|
||||
|
||||
__all__ = ["WatermarkRemover", "remove_ai_metadata", "remove_watermark"]
|
||||
|
||||
@@ -27,12 +25,12 @@ def __getattr__(name: str) -> object:
|
||||
"""Resolve the public API on first access (PEP 562), not at package import."""
|
||||
if name == "remove_ai_metadata":
|
||||
# Re-export the single, robust stripper (byte-level, lossless-for-JPEG, all
|
||||
# containers); the old noai.cleaner implementation is retired.
|
||||
# containers); the old legacy metadata helper implementation is retired.
|
||||
from remove_ai_watermarks.metadata import remove_ai_metadata
|
||||
|
||||
return remove_ai_metadata
|
||||
if name in ("WatermarkRemover", "remove_watermark"):
|
||||
from remove_ai_watermarks.noai import watermark_remover
|
||||
from remove_ai_watermarks._internal import watermark_remover
|
||||
|
||||
return getattr(watermark_remover, name)
|
||||
raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
|
||||
@@ -0,0 +1,363 @@
|
||||
"""C2PA inspection through the official reader with a bounded PNG fallback."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import contextlib
|
||||
import functools
|
||||
import json
|
||||
import logging
|
||||
import re
|
||||
import struct
|
||||
from dataclasses import dataclass
|
||||
from pathlib import Path
|
||||
from typing import TYPE_CHECKING, Any, cast
|
||||
|
||||
from remove_ai_watermarks._internal.constants import (
|
||||
C2PA_ACTIONS,
|
||||
C2PA_AI_TOOLS,
|
||||
C2PA_CHUNK_TYPE,
|
||||
C2PA_ISSUERS,
|
||||
C2PA_SIGNATURES,
|
||||
C2PA_SOFT_BINDINGS,
|
||||
PNG_SIGNATURE,
|
||||
SYNTHID_C2PA_ISSUERS,
|
||||
)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from typing import BinaryIO
|
||||
|
||||
_C2paReader: Any = None
|
||||
with contextlib.suppress(Exception):
|
||||
from c2pa import Reader as _C2paReader # pyright: ignore[reportMissingTypeStubs]
|
||||
|
||||
_C2PA_READER_AVAILABLE = _C2paReader is not None
|
||||
_PNG_HEADER = struct.Struct(">I4s")
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class _PngChunk:
|
||||
payload: bytes
|
||||
serialized: bytes
|
||||
|
||||
|
||||
def reader_available() -> bool:
|
||||
"""Return whether the official C2PA reader loaded successfully."""
|
||||
return _C2PA_READER_AVAILABLE
|
||||
|
||||
|
||||
def _manifest_json_uncached(path: str) -> str | None:
|
||||
try:
|
||||
reader = _C2paReader.try_create(path)
|
||||
except Exception as error:
|
||||
logger.debug("C2PA reader rejected %s: %s", path, error)
|
||||
return None
|
||||
if reader is None:
|
||||
return None
|
||||
try:
|
||||
with reader:
|
||||
return cast("str", reader.json())
|
||||
except Exception as error:
|
||||
logger.debug("C2PA reader could not serialize %s: %s", path, error)
|
||||
return None
|
||||
|
||||
|
||||
@functools.lru_cache(maxsize=8)
|
||||
def _manifest_json_cached(path: str, _mtime_ns: int) -> str | None:
|
||||
return _manifest_json_uncached(path)
|
||||
|
||||
|
||||
def read_manifest_store_json(image_path: Path) -> str | None:
|
||||
"""Read the complete manifest-store JSON, caching it until the file changes."""
|
||||
if not reader_available():
|
||||
return None
|
||||
path = str(image_path)
|
||||
try:
|
||||
return _manifest_json_cached(path, image_path.stat().st_mtime_ns)
|
||||
except OSError:
|
||||
return _manifest_json_uncached(path)
|
||||
|
||||
|
||||
def _find_c2pa_chunk(path: Path) -> _PngChunk | None:
|
||||
"""Return the first recognizable C2PA chunk without loading the whole PNG."""
|
||||
try:
|
||||
stream = path.open("rb")
|
||||
except OSError:
|
||||
return None
|
||||
with stream:
|
||||
if stream.read(len(PNG_SIGNATURE)) != PNG_SIGNATURE:
|
||||
return None
|
||||
file_size = stream.seek(0, 2)
|
||||
stream.seek(len(PNG_SIGNATURE))
|
||||
while True:
|
||||
header = stream.read(_PNG_HEADER.size)
|
||||
if len(header) != _PNG_HEADER.size:
|
||||
return None
|
||||
length, kind = _PNG_HEADER.unpack(header)
|
||||
if length + 4 > file_size - stream.tell():
|
||||
return None
|
||||
if kind == C2PA_CHUNK_TYPE:
|
||||
payload = stream.read(length)
|
||||
crc = stream.read(4)
|
||||
if _looks_like_c2pa(payload):
|
||||
return _PngChunk(payload, header + payload + crc)
|
||||
else:
|
||||
stream.seek(length + 4, 1)
|
||||
if kind == b"IEND":
|
||||
return None
|
||||
|
||||
|
||||
def _is_well_formed_png(path: Path) -> bool:
|
||||
"""Validate PNG chunk bounds with seeks rather than payload allocations."""
|
||||
try:
|
||||
stream = path.open("rb")
|
||||
except OSError:
|
||||
return False
|
||||
with stream:
|
||||
if stream.read(len(PNG_SIGNATURE)) != PNG_SIGNATURE:
|
||||
return False
|
||||
file_size = stream.seek(0, 2)
|
||||
stream.seek(len(PNG_SIGNATURE))
|
||||
while True:
|
||||
header = stream.read(_PNG_HEADER.size)
|
||||
if len(header) != _PNG_HEADER.size:
|
||||
return False
|
||||
length, kind = _PNG_HEADER.unpack(header)
|
||||
if length + 4 > file_size - stream.tell():
|
||||
return False
|
||||
stream.seek(length + 4, 1)
|
||||
if kind == b"IEND":
|
||||
return True
|
||||
|
||||
|
||||
def _copy_bytes(source: BinaryIO, target: BinaryIO, byte_count: int) -> None:
|
||||
"""Copy exactly one bounded chunk without allocating its complete payload."""
|
||||
remaining = byte_count
|
||||
while remaining:
|
||||
block = source.read(min(remaining, 1024 * 1024))
|
||||
if not block:
|
||||
raise OSError("PNG changed while it was being copied")
|
||||
target.write(block)
|
||||
remaining -= len(block)
|
||||
|
||||
|
||||
def _looks_like_c2pa(payload: bytes) -> bool:
|
||||
lowered = payload.lower()
|
||||
return any(signature in payload for signature in C2PA_SIGNATURES) or b"c2pa" in lowered or b"jumb" in lowered
|
||||
|
||||
|
||||
def extract_c2pa_chunk(image_path: Path) -> bytes | None:
|
||||
"""Return the first complete C2PA PNG chunk, including header and CRC."""
|
||||
if image_path.suffix.casefold() != ".png":
|
||||
return None
|
||||
chunk = _find_c2pa_chunk(image_path)
|
||||
return None if chunk is None else chunk.serialized
|
||||
|
||||
|
||||
def has_c2pa_metadata(image_path: Path) -> bool:
|
||||
"""Return whether a validly bounded PNG contains a recognizable C2PA chunk."""
|
||||
return extract_c2pa_chunk(Path(image_path)) is not None
|
||||
|
||||
|
||||
def _active_manifest(store: dict[str, Any]) -> dict[str, Any]:
|
||||
manifests = store.get("manifests")
|
||||
if not isinstance(manifests, dict):
|
||||
return {}
|
||||
typed_manifests = cast("dict[object, object]", manifests)
|
||||
active = typed_manifests.get(store.get("active_manifest"))
|
||||
return cast("dict[str, Any]", active) if isinstance(active, dict) else {}
|
||||
|
||||
|
||||
def _claim_generator_from_store(store: dict[str, Any]) -> str | None:
|
||||
active = _active_manifest(store)
|
||||
direct = active.get("claim_generator")
|
||||
if isinstance(direct, str) and direct.isprintable() and direct:
|
||||
return direct
|
||||
candidates = active.get("claim_generator_info")
|
||||
if isinstance(candidates, list) and candidates and isinstance(candidates[0], dict):
|
||||
candidate = cast("dict[object, object]", candidates[0])
|
||||
name = candidate.get("name")
|
||||
if isinstance(name, str) and name.isprintable() and name:
|
||||
return name
|
||||
return None
|
||||
|
||||
|
||||
def synthid_verdict(vendors: str) -> str:
|
||||
"""Describe why metadata implies a likely pixel-level SynthID watermark."""
|
||||
return f"likely present ({vendors} embeds SynthID with C2PA)"
|
||||
|
||||
|
||||
def _names_present(buffer: bytes, registry: dict[bytes, str]) -> list[str]:
|
||||
return sorted({label for token, label in registry.items() if token in buffer})
|
||||
|
||||
|
||||
def synthid_vendors_in(buffer: bytes) -> list[str]:
|
||||
"""List matching C2PA issuers known to pair their manifests with SynthID."""
|
||||
registry = {token: label for token, label in C2PA_ISSUERS.items() if token in SYNTHID_C2PA_ISSUERS}
|
||||
return _names_present(buffer, registry)
|
||||
|
||||
|
||||
def soft_binding_vendors_in(buffer: bytes) -> list[str]:
|
||||
"""List the soft-binding algorithms named in manifest bytes."""
|
||||
return _names_present(buffer, C2PA_SOFT_BINDINGS)
|
||||
|
||||
|
||||
def _ordered_matches(buffer: bytes, registry: dict[bytes, str]) -> list[str]:
|
||||
return list(dict.fromkeys(label for token, label in registry.items() if token in buffer))
|
||||
|
||||
|
||||
def _populate_registry_fields(buffer: bytes, info: dict[str, Any]) -> bool:
|
||||
issuers = _ordered_matches(buffer, C2PA_ISSUERS)
|
||||
tools = _ordered_matches(buffer, C2PA_AI_TOOLS)
|
||||
actions = _ordered_matches(buffer, C2PA_ACTIONS)
|
||||
if issuers:
|
||||
info["issuer"] = ", ".join(issuers)
|
||||
if tools:
|
||||
info["ai_tool"] = ", ".join(tools)
|
||||
if actions:
|
||||
info["actions"] = ", ".join(actions)
|
||||
|
||||
ai_source = False
|
||||
if b"trainedAlgorithmicMedia" in buffer:
|
||||
info.update(source_type="trainedAlgorithmicMedia (AI-generated)", ai_source_kind="generated")
|
||||
ai_source = True
|
||||
elif b"compositeWithTrainedAlgorithmicMedia" in buffer:
|
||||
info.update(source_type="compositeWithTrainedAlgorithmicMedia (AI-enhanced)", ai_source_kind="enhanced")
|
||||
ai_source = True
|
||||
elif b"algorithmicMedia" in buffer:
|
||||
info["source_type"] = "algorithmicMedia"
|
||||
|
||||
synthid = synthid_vendors_in(buffer)
|
||||
if ai_source and synthid:
|
||||
info["synthid_vendors"] = synthid
|
||||
info["synthid_watermark"] = synthid_verdict(", ".join(synthid))
|
||||
|
||||
soft_bindings = soft_binding_vendors_in(buffer)
|
||||
if soft_bindings:
|
||||
info["soft_binding_vendors"] = soft_bindings
|
||||
info["soft_binding"] = ", ".join(soft_bindings)
|
||||
return ai_source
|
||||
|
||||
|
||||
def _base_info(byte_count: int, *, fallback: bool = False) -> dict[str, Any]:
|
||||
container = "C2PA manifest" if fallback else "C2PA manifest store"
|
||||
return {
|
||||
"has_c2pa": True,
|
||||
"type": "C2PA (Coalition for Content Provenance and Authenticity)",
|
||||
"c2pa_manifest": f"{container} ({byte_count} bytes)",
|
||||
}
|
||||
|
||||
|
||||
def _info_from_store(store: dict[str, Any], encoded: bytes) -> dict[str, Any]:
|
||||
info = _base_info(len(encoded))
|
||||
_populate_registry_fields(encoded, info)
|
||||
generator = _claim_generator_from_store(store)
|
||||
if generator is not None:
|
||||
info["claim_generator"] = generator
|
||||
signature_value = _active_manifest(store).get("signature_info")
|
||||
if isinstance(signature_value, dict):
|
||||
signature = cast("dict[object, object]", signature_value)
|
||||
timestamp = signature.get("time")
|
||||
if timestamp:
|
||||
info["timestamp"] = str(timestamp)
|
||||
return info
|
||||
|
||||
|
||||
def c2pa_info_from_manifest_store(store: str | dict[str, Any]) -> dict[str, Any]:
|
||||
"""Normalize a manifest store supplied as JSON text or a decoded object."""
|
||||
try:
|
||||
raw_decoded: object = store if isinstance(store, dict) else json.loads(store)
|
||||
decoded = cast("dict[str, Any]", raw_decoded) if isinstance(raw_decoded, dict) else None
|
||||
if not isinstance(decoded, dict) or not decoded or decoded.get("error"):
|
||||
return {}
|
||||
encoded = json.dumps(decoded, ensure_ascii=False).encode() if isinstance(store, dict) else store.encode()
|
||||
except (TypeError, ValueError):
|
||||
return {}
|
||||
return _info_from_store(decoded, encoded)
|
||||
|
||||
|
||||
def cbor_text_after(payload: bytes, key: bytes) -> str | None:
|
||||
"""Decode a definite-length CBOR text value immediately following ``key``."""
|
||||
key_end = payload.find(key)
|
||||
if key_end < 0:
|
||||
return None
|
||||
cursor = key_end + len(key)
|
||||
if cursor >= len(payload):
|
||||
return None
|
||||
initial = payload[cursor]
|
||||
if 0x60 <= initial <= 0x77:
|
||||
length, cursor = initial & 0x1F, cursor + 1
|
||||
elif initial == 0x78 and cursor + 1 < len(payload):
|
||||
length, cursor = payload[cursor + 1], cursor + 2
|
||||
elif initial == 0x79 and cursor + 2 < len(payload):
|
||||
length = int.from_bytes(payload[cursor + 1 : cursor + 3], "big")
|
||||
cursor += 3
|
||||
else:
|
||||
return None
|
||||
raw = payload[cursor : cursor + length]
|
||||
if len(raw) != length:
|
||||
return None
|
||||
try:
|
||||
return raw.decode()
|
||||
except UnicodeDecodeError:
|
||||
return raw.decode("latin1", errors="replace")
|
||||
|
||||
|
||||
def _parse_c2pa_chunk(payload: bytes, info: dict[str, Any]) -> None:
|
||||
info.update(_base_info(len(payload), fallback=True))
|
||||
_populate_registry_fields(payload, info)
|
||||
for key, output_key in ((b"name", "claim_generator"), (b"specVersion", "c2pa_spec")):
|
||||
value = cbor_text_after(payload, key)
|
||||
if value and value.isprintable():
|
||||
info[output_key] = value
|
||||
timestamps = [item.decode() for item in re.findall(rb"\d{14}Z", payload)]
|
||||
if timestamps:
|
||||
info["timestamp"] = timestamps[0]
|
||||
if len(timestamps) > 1:
|
||||
info["timestamps"] = timestamps[:3]
|
||||
|
||||
|
||||
def _extract_c2pa_info_png(image_path: Path) -> dict[str, Any]:
|
||||
if image_path.suffix.casefold() != ".png":
|
||||
return {}
|
||||
chunk = _find_c2pa_chunk(image_path)
|
||||
if chunk is None:
|
||||
return {}
|
||||
info: dict[str, Any] = {}
|
||||
_parse_c2pa_chunk(chunk.payload, info)
|
||||
return info
|
||||
|
||||
|
||||
def extract_c2pa_info(image_path: Path) -> dict[str, Any]:
|
||||
"""Return normalized C2PA evidence from the official reader or PNG fallback."""
|
||||
store = read_manifest_store_json(Path(image_path))
|
||||
if store is not None:
|
||||
return c2pa_info_from_manifest_store(store)
|
||||
return _extract_c2pa_info_png(Path(image_path))
|
||||
|
||||
|
||||
def inject_c2pa_chunk(target_path: Path, output_path: Path, c2pa_chunk: bytes) -> None:
|
||||
"""Replace any C2PA chunks in a PNG and insert ``c2pa_chunk`` before IDAT."""
|
||||
if target_path.suffix.casefold() != ".png" or output_path.suffix.casefold() != ".png":
|
||||
raise ValueError("C2PA chunk injection is only supported for PNG files")
|
||||
if not _is_well_formed_png(target_path):
|
||||
raise ValueError("Target is not a well-formed PNG file")
|
||||
|
||||
output_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
with target_path.open("rb") as source, output_path.open("wb") as target:
|
||||
target.write(source.read(len(PNG_SIGNATURE)))
|
||||
inserted = False
|
||||
while True:
|
||||
header = source.read(_PNG_HEADER.size)
|
||||
length, kind = _PNG_HEADER.unpack(header)
|
||||
if kind == b"IDAT" and not inserted:
|
||||
target.write(c2pa_chunk)
|
||||
inserted = True
|
||||
if kind == C2PA_CHUNK_TYPE:
|
||||
source.seek(length + 4, 1)
|
||||
else:
|
||||
target.write(header)
|
||||
_copy_bytes(source, target, length + 4)
|
||||
if kind == b"IEND":
|
||||
break
|
||||
@@ -0,0 +1,152 @@
|
||||
"""Registries shared by metadata extraction and provenance classification."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from dataclasses import dataclass
|
||||
|
||||
|
||||
def _tokens(value: str) -> tuple[str, ...]:
|
||||
return tuple(value.split("|"))
|
||||
|
||||
|
||||
SUPPORTED_FORMATS = frozenset(_tokens(".png|.jpg|.jpeg|.webp|.heic|.heif|.avif"))
|
||||
AI_METADATA_KEYS = _tokens(
|
||||
"parameters|postprocessing|extras|workflow|prompt|Dream|SD:mode|StableDiffusionVersion|"
|
||||
"generation_time|Model|Model hash|Seed"
|
||||
)
|
||||
PNG_METADATA_KEYS = _tokens(
|
||||
"Author|Title|Description|Copyright|Creation Time|Software|Disclaimer|Warning|Source|Comment"
|
||||
)
|
||||
AI_KEYWORDS = _tokens(
|
||||
"prompt|negative_prompt|sampler|cfg_scale|lora|diffusion|comfy|midjourney|dall-e|dalle|imagen|firefly|c2pa|chatgpt|gpt-4|sora|openai|truepic|stable_diffusion|invokeai"
|
||||
)
|
||||
|
||||
PNG_SIGNATURE = b"\x89PNG\r\n\x1a\n"
|
||||
C2PA_CHUNK_TYPE = b"caBX"
|
||||
C2PA_SIGNATURES = tuple(
|
||||
token.encode() for token in _tokens("c2pa|C2PA|jumb|jumd|JUMBF|jumbf|cbor|contentcreds|digid|assertions|manifest")
|
||||
)
|
||||
|
||||
|
||||
@dataclass(frozen=True, slots=True)
|
||||
class C2paAiVendor:
|
||||
"""One issuer signature and its normalized product attribution."""
|
||||
|
||||
issuer: bytes
|
||||
org: str
|
||||
platform: str | None
|
||||
needle: str | None
|
||||
synthid: bool = False
|
||||
asserts_ai: bool = False
|
||||
|
||||
|
||||
def _vendor(
|
||||
issuer: bytes | str,
|
||||
org: str,
|
||||
platform: str | None,
|
||||
needle: str | None,
|
||||
*,
|
||||
synthid: bool = False,
|
||||
asserts_ai: bool = False,
|
||||
) -> C2paAiVendor:
|
||||
token = issuer.encode() if isinstance(issuer, str) else issuer
|
||||
return C2paAiVendor(token, org, platform, needle, synthid, asserts_ai)
|
||||
|
||||
|
||||
# Order is product priority when a manifest mentions more than one organization.
|
||||
C2PA_AI_VENDORS: tuple[C2paAiVendor, ...] = (
|
||||
_vendor(b"Microsoft", "Microsoft", "Microsoft (Bing Image Creator / Designer)", "Microsoft"),
|
||||
_vendor(b"Adobe", "Adobe", "Adobe Firefly", "Adobe"),
|
||||
_vendor(b"OpenAI", "OpenAI", "OpenAI (ChatGPT / gpt-image / DALL-E / Sora)", "OpenAI", synthid=True),
|
||||
_vendor(b"Google", "Google LLC", "Google (Gemini / Imagen)", "Google", synthid=True),
|
||||
_vendor(b"Stability AI", "Stability AI", "Stability AI (Stable Image / DreamStudio)", "Stability AI"),
|
||||
_vendor(b"Black Forest Labs", "Black Forest Labs", "Black Forest Labs (FLUX)", "Black Forest Labs"),
|
||||
_vendor(b"volcengine", "ByteDance (Volcano Engine)", "ByteDance (Doubao / Jimeng / Volcano Engine)", "ByteDance"),
|
||||
_vendor(
|
||||
"北京火山引擎科技有限公司",
|
||||
"ByteDance (Volcano Engine)",
|
||||
"ByteDance (Doubao / Jimeng / Volcano Engine)",
|
||||
"ByteDance",
|
||||
),
|
||||
_vendor(b"Byteplus", "BytePlus (ByteDance)", "ByteDance (Doubao / Jimeng / Volcano Engine)", "ByteDance"),
|
||||
_vendor(
|
||||
b"Dreamina",
|
||||
"ByteDance (Dreamina)",
|
||||
"ByteDance (Doubao / Jimeng / Volcano Engine)",
|
||||
"ByteDance",
|
||||
asserts_ai=True,
|
||||
),
|
||||
_vendor(b"Canva", "Canva", "Canva (Magic Media)", "Canva"),
|
||||
_vendor(b"Eleven Labs", "ElevenLabs", "ElevenLabs", "ElevenLabs"),
|
||||
_vendor(b"fal-ai", "fal.ai", "fal.ai", "fal.ai", asserts_ai=True),
|
||||
_vendor(b"Bria", "Bria Artificial Intelligence", "Bria AI", "Bria", asserts_ai=True),
|
||||
_vendor(b"Truepic", "Truepic", None, None),
|
||||
)
|
||||
|
||||
C2PA_ISSUERS = {vendor.issuer: vendor.org for vendor in C2PA_AI_VENDORS}
|
||||
C2PA_IDENTITY_AI_ORGS = frozenset(vendor.org for vendor in C2PA_AI_VENDORS if vendor.asserts_ai)
|
||||
SYNTHID_C2PA_ISSUERS = frozenset(vendor.issuer for vendor in C2PA_AI_VENDORS if vendor.synthid)
|
||||
|
||||
C2PA_AI_TOOLS = {
|
||||
token.encode(): label
|
||||
for token, label in (
|
||||
("GPT-4o", "GPT-4o"),
|
||||
("ChatGPT", "ChatGPT"),
|
||||
("Sora", "Sora"),
|
||||
("DALL-E", "DALL-E"),
|
||||
("DALL", "DALL-E"),
|
||||
("Imagen", "Imagen"),
|
||||
("Firefly", "Firefly"),
|
||||
)
|
||||
}
|
||||
|
||||
C2PA_SOFT_BINDINGS = {
|
||||
b"com.adobe.trustmark": "Adobe TrustMark",
|
||||
b"com.adobe.icn": "Adobe (content fingerprint)",
|
||||
b"com.digimarc": "Digimarc",
|
||||
b"com.imatag.lamark": "Imatag (Lamark)",
|
||||
b"ai.steg": "Steg.AI",
|
||||
b"com.microsoft.invismark": "Microsoft InvisMark",
|
||||
b"com.microsoft.wavmark": "Microsoft WavMark",
|
||||
b"com.verimatrix": "Verimatrix",
|
||||
b"com.nagra.nexguard": "NAGRA NexGuard",
|
||||
b"com.aiwatermark": "AIWatermark (Meta PixelSeal)",
|
||||
b"ai.trufo": "Trufo",
|
||||
b"app.overlai": "Overlai",
|
||||
b"com.markany": "MarkAny",
|
||||
b"com.mentaport": "Mentaport",
|
||||
b"es.lumatrace": "LumaTrace",
|
||||
b"ai.verda": "VerdaAI",
|
||||
b"ai.contentlens": "ContentLens",
|
||||
b"io.iscc": "ISCC (content code)",
|
||||
}
|
||||
|
||||
AI_GENERATOR_TOKENS = frozenset(
|
||||
{
|
||||
"firefly",
|
||||
"dall-e",
|
||||
"dalle",
|
||||
"midjourney",
|
||||
"stable diffusion",
|
||||
"stable-diffusion",
|
||||
"stablediffusion",
|
||||
"comfyui",
|
||||
"automatic1111",
|
||||
"invokeai",
|
||||
"imagen",
|
||||
"gpt-image",
|
||||
"nightcafe",
|
||||
"ideogram",
|
||||
"leonardo",
|
||||
"flux",
|
||||
"dreamstudio",
|
||||
"novelai",
|
||||
"reve.com",
|
||||
"aphrodite ai",
|
||||
"apple photos clean up",
|
||||
"fal-ai",
|
||||
}
|
||||
)
|
||||
|
||||
_C2PA_ACTION_NAMES = _tokens("created|converted|edited|filtered|cropped|resized|opened|placed")
|
||||
C2PA_ACTIONS = {f"c2pa.{action}".encode(): action for action in _C2PA_ACTION_NAMES}
|
||||
@@ -0,0 +1,154 @@
|
||||
"""Bounded Matroska/WebM metadata reader for native TC260 AIGC labels.
|
||||
|
||||
TC260-PG-20257A stores the label as a Matroska ``SimpleTag`` whose
|
||||
``TagName`` is ``AIGC`` and whose ``TagString`` is the normative JSON object.
|
||||
The walker seeks over unrelated elements such as clusters instead of reading
|
||||
their payloads.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import TYPE_CHECKING, BinaryIO
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from collections.abc import Iterator
|
||||
from pathlib import Path
|
||||
|
||||
from remove_ai_watermarks.metadata import MAX_TC260_VALUE_BYTES, parse_tc260_aigc_json
|
||||
|
||||
_EBML_MAGIC = b"\x1aE\xdf\xa3"
|
||||
_SEGMENT_ID = 0x18538067
|
||||
_TAGS_ID = 0x1254C367
|
||||
_TAG_ID = 0x7373
|
||||
_SIMPLE_TAG_ID = 0x67C8
|
||||
_TAG_NAME_ID = 0x45A3
|
||||
_TAG_STRING_ID = 0x4487
|
||||
_MAX_TAG_NAME_BYTES = 256
|
||||
|
||||
|
||||
def _vint_length(first: int, *, maximum: int) -> int | None:
|
||||
"""Return an EBML variable-integer length from its first byte."""
|
||||
mask = 0x80
|
||||
for length in range(1, maximum + 1):
|
||||
if first & mask:
|
||||
return length
|
||||
mask >>= 1
|
||||
return None
|
||||
|
||||
|
||||
def _read_element_header(
|
||||
stream: BinaryIO,
|
||||
pos: int,
|
||||
limit: int,
|
||||
) -> tuple[int, int, int] | None:
|
||||
"""Return ``(element_id, payload_start, element_end)`` inside ``limit``."""
|
||||
if pos < 0 or pos >= limit:
|
||||
return None
|
||||
stream.seek(pos)
|
||||
first_raw = stream.read(1)
|
||||
if not first_raw:
|
||||
return None
|
||||
id_length = _vint_length(first_raw[0], maximum=4)
|
||||
if id_length is None or pos + id_length >= limit:
|
||||
return None
|
||||
element_id_raw = first_raw + stream.read(id_length - 1)
|
||||
if len(element_id_raw) != id_length:
|
||||
return None
|
||||
element_id = int.from_bytes(element_id_raw, "big")
|
||||
|
||||
size_first_raw = stream.read(1)
|
||||
if not size_first_raw:
|
||||
return None
|
||||
size_length = _vint_length(size_first_raw[0], maximum=8)
|
||||
if size_length is None:
|
||||
return None
|
||||
size_rest = stream.read(size_length - 1)
|
||||
if len(size_rest) != size_length - 1:
|
||||
return None
|
||||
marker = 1 << (8 - size_length)
|
||||
size_value = int.from_bytes(bytes([size_first_raw[0] & (marker - 1)]) + size_rest, "big")
|
||||
payload_start = pos + id_length + size_length
|
||||
if payload_start > limit:
|
||||
return None
|
||||
unknown_size = size_value == (1 << (7 * size_length)) - 1
|
||||
element_end = limit if unknown_size else payload_start + size_value
|
||||
if element_end > limit:
|
||||
return None
|
||||
return element_id, payload_start, element_end
|
||||
|
||||
|
||||
def _iter_elements(
|
||||
stream: BinaryIO,
|
||||
start: int,
|
||||
end: int,
|
||||
) -> Iterator[tuple[int, int, int]]:
|
||||
"""Yield valid direct children from one bounded EBML region."""
|
||||
pos = start
|
||||
while pos < end:
|
||||
header = _read_element_header(stream, pos, end)
|
||||
if header is None:
|
||||
return
|
||||
element_id, payload_start, element_end = header
|
||||
yield element_id, payload_start, element_end
|
||||
if element_end <= pos:
|
||||
return
|
||||
pos = element_end
|
||||
|
||||
|
||||
def _read_bounded(
|
||||
stream: BinaryIO,
|
||||
start: int,
|
||||
end: int,
|
||||
maximum: int,
|
||||
) -> bytes | None:
|
||||
size = end - start
|
||||
if size < 0 or size > maximum:
|
||||
return None
|
||||
stream.seek(start)
|
||||
value = stream.read(size)
|
||||
return value if len(value) == size else None
|
||||
|
||||
|
||||
def _simple_tag_payloads(
|
||||
stream: BinaryIO,
|
||||
start: int,
|
||||
end: int,
|
||||
) -> tuple[bytes, ...]:
|
||||
name: bytes | None = None
|
||||
values: list[bytes] = []
|
||||
for element_id, payload_start, element_end in _iter_elements(stream, start, end):
|
||||
if element_id == _TAG_NAME_ID:
|
||||
name = _read_bounded(stream, payload_start, element_end, _MAX_TAG_NAME_BYTES)
|
||||
elif element_id == _TAG_STRING_ID:
|
||||
value = _read_bounded(stream, payload_start, element_end, MAX_TC260_VALUE_BYTES)
|
||||
if value is not None:
|
||||
values.append(value)
|
||||
if name != b"AIGC":
|
||||
return ()
|
||||
return tuple(value for value in values if parse_tc260_aigc_json(value) is not None)
|
||||
|
||||
|
||||
def tc260_aigc_payloads(path: str | Path) -> tuple[bytes, ...]:
|
||||
"""Read validated TC260 values from Matroska/WebM ``SimpleTag`` entries."""
|
||||
found: list[bytes] = []
|
||||
try:
|
||||
with open(path, "rb") as stream:
|
||||
if stream.read(4) != _EBML_MAGIC:
|
||||
return ()
|
||||
stream.seek(0, 2)
|
||||
file_size = stream.tell()
|
||||
for element_id, payload_start, element_end in _iter_elements(stream, 0, file_size):
|
||||
if element_id != _SEGMENT_ID:
|
||||
continue
|
||||
for child_id, child_start, child_end in _iter_elements(stream, payload_start, element_end):
|
||||
if child_id != _TAGS_ID:
|
||||
continue
|
||||
for tag_id, tag_start, tag_end in _iter_elements(stream, child_start, child_end):
|
||||
if tag_id != _TAG_ID:
|
||||
continue
|
||||
for simple_id, simple_start, simple_end in _iter_elements(stream, tag_start, tag_end):
|
||||
if simple_id == _SIMPLE_TAG_ID:
|
||||
found.extend(_simple_tag_payloads(stream, simple_start, simple_end))
|
||||
except OSError:
|
||||
return ()
|
||||
return tuple(found)
|
||||
@@ -0,0 +1,98 @@
|
||||
"""Read image metadata without changing the source container."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import TYPE_CHECKING, Any, cast
|
||||
|
||||
import piexif
|
||||
from PIL import Image
|
||||
|
||||
from remove_ai_watermarks._internal.c2pa import extract_c2pa_chunk, extract_c2pa_info, has_c2pa_metadata
|
||||
from remove_ai_watermarks._internal.constants import AI_KEYWORDS, AI_METADATA_KEYS
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from pathlib import Path
|
||||
|
||||
_EXIF_KEY = "exif"
|
||||
_AI_KEYS_CASEFOLD = frozenset(key.casefold() for key in AI_METADATA_KEYS)
|
||||
|
||||
|
||||
def _read_pillow_info(source_path: Path) -> dict[str, Any]:
|
||||
with Image.open(source_path) as image:
|
||||
return {key: value for key, value in image.info.items() if isinstance(key, str)}
|
||||
|
||||
|
||||
def _decode_exif(raw: object) -> tuple[str, object]:
|
||||
if not isinstance(raw, bytes):
|
||||
return "exif_raw", raw
|
||||
try:
|
||||
return _EXIF_KEY, piexif.load(raw)
|
||||
except Exception:
|
||||
return "exif_raw", raw
|
||||
|
||||
|
||||
def _is_ai_field(key: str) -> bool:
|
||||
folded = key.casefold()
|
||||
return folded in _AI_KEYS_CASEFOLD or any(token in folded for token in AI_KEYWORDS)
|
||||
|
||||
|
||||
def _attach_c2pa(source_path: Path, metadata: dict[str, Any]) -> None:
|
||||
if not has_c2pa_metadata(source_path):
|
||||
return
|
||||
metadata["c2pa"] = extract_c2pa_info(source_path)
|
||||
payload = extract_c2pa_chunk(source_path)
|
||||
if payload is not None:
|
||||
metadata["c2pa_chunk"] = payload
|
||||
|
||||
|
||||
def extract_metadata(source_path: Path) -> dict[str, Any]:
|
||||
"""Return every Pillow-visible field plus decoded EXIF and C2PA data."""
|
||||
raw_info = _read_pillow_info(source_path)
|
||||
metadata = dict(raw_info)
|
||||
if _EXIF_KEY in raw_info:
|
||||
metadata.pop(_EXIF_KEY, None)
|
||||
decoded_key, decoded_value = _decode_exif(raw_info[_EXIF_KEY])
|
||||
metadata[decoded_key] = decoded_value
|
||||
|
||||
_attach_c2pa(source_path, metadata)
|
||||
return metadata
|
||||
|
||||
|
||||
def extract_ai_metadata(source_path: Path) -> dict[str, Any]:
|
||||
"""Return only metadata keys recognized as AI provenance or generation data."""
|
||||
metadata = {key: value for key, value in _read_pillow_info(source_path).items() if _is_ai_field(key)}
|
||||
_attach_c2pa(source_path, metadata)
|
||||
return metadata
|
||||
|
||||
|
||||
def has_ai_metadata(image_path: Path) -> bool:
|
||||
"""Return whether a supported metadata signal is present."""
|
||||
if any(_is_ai_field(key) for key in _read_pillow_info(image_path)):
|
||||
return True
|
||||
return has_c2pa_metadata(image_path)
|
||||
|
||||
|
||||
def _summary_value(value: object) -> str:
|
||||
if isinstance(value, bytes):
|
||||
return f"<binary data ({len(value)} bytes)>"
|
||||
text = str(value)
|
||||
return text if len(text) <= 100 else f"{text[:100]}..."
|
||||
|
||||
|
||||
def get_ai_metadata_summary(source_path: Path) -> str:
|
||||
"""Format the AI-only metadata view for the command-line report."""
|
||||
metadata = extract_ai_metadata(source_path)
|
||||
if not metadata:
|
||||
return "No AI metadata found."
|
||||
|
||||
lines = ["AI Image Metadata:", "-" * 40]
|
||||
for key, value in metadata.items():
|
||||
if key == "c2pa_chunk":
|
||||
continue
|
||||
if key == "c2pa" and isinstance(value, dict):
|
||||
lines.append("C2PA Metadata:")
|
||||
c2pa_fields = cast("dict[str, object]", value)
|
||||
lines.extend(f" {name}: {_summary_value(item)}" for name, item in c2pa_fields.items())
|
||||
continue
|
||||
lines.append(f"{key}: {_summary_value(value)}")
|
||||
return "\n".join(lines)
|
||||
@@ -0,0 +1,160 @@
|
||||
"""Bounded FLV metadata reader for native TC260 AIGC labels.
|
||||
|
||||
TC260-PG-20257A stores the label in the ``onMetaData`` script tag as an AMF0
|
||||
property named ``AIGC`` whose string value is the normative JSON object. Media
|
||||
tag payloads are skipped without being loaded.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import TYPE_CHECKING
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from pathlib import Path
|
||||
|
||||
from remove_ai_watermarks.metadata import MAX_TC260_VALUE_BYTES, parse_tc260_aigc_json
|
||||
|
||||
_SCRIPT_TAG = 18
|
||||
_MAX_SCRIPT_BYTES = 4 * 1024 * 1024
|
||||
|
||||
|
||||
def _u24(value: bytes) -> int:
|
||||
return int.from_bytes(value, "big")
|
||||
|
||||
|
||||
def _amf0_string(data: bytes, position: int, *, long: bool = False) -> tuple[bytes, int] | None:
|
||||
length_size = 4 if long else 2
|
||||
if position + length_size > len(data):
|
||||
return None
|
||||
length = int.from_bytes(data[position : position + length_size], "big")
|
||||
start = position + length_size
|
||||
end = start + length
|
||||
if end > len(data):
|
||||
return None
|
||||
return data[start:end], end
|
||||
|
||||
|
||||
def _skip_amf0(data: bytes, position: int, depth: int = 0) -> int | None:
|
||||
if position >= len(data) or depth > 8:
|
||||
return None
|
||||
value_type = data[position]
|
||||
position += 1
|
||||
if value_type == 0:
|
||||
return position + 8 if position + 8 <= len(data) else None
|
||||
if value_type == 1:
|
||||
return position + 1 if position + 1 <= len(data) else None
|
||||
if value_type == 2:
|
||||
parsed = _amf0_string(data, position)
|
||||
return parsed[1] if parsed is not None else None
|
||||
if value_type in {5, 6}:
|
||||
return position
|
||||
if value_type == 7:
|
||||
return position + 2 if position + 2 <= len(data) else None
|
||||
if value_type == 11:
|
||||
return position + 10 if position + 10 <= len(data) else None
|
||||
if value_type == 12:
|
||||
parsed = _amf0_string(data, position, long=True)
|
||||
return parsed[1] if parsed is not None else None
|
||||
if value_type == 10:
|
||||
if position + 4 > len(data):
|
||||
return None
|
||||
count = int.from_bytes(data[position : position + 4], "big")
|
||||
position += 4
|
||||
for _ in range(count):
|
||||
next_position = _skip_amf0(data, position, depth + 1)
|
||||
if next_position is None:
|
||||
return None
|
||||
position = next_position
|
||||
return position
|
||||
if value_type in {3, 8}:
|
||||
if value_type == 8:
|
||||
if position + 4 > len(data):
|
||||
return None
|
||||
position += 4
|
||||
while position + 3 <= len(data):
|
||||
name_length = int.from_bytes(data[position : position + 2], "big")
|
||||
position += 2
|
||||
if name_length == 0 and data[position] == 9:
|
||||
return position + 1
|
||||
position += name_length
|
||||
if position > len(data):
|
||||
return None
|
||||
next_position = _skip_amf0(data, position, depth + 1)
|
||||
if next_position is None:
|
||||
return None
|
||||
position = next_position
|
||||
return None
|
||||
return None
|
||||
|
||||
|
||||
def _script_payloads(data: bytes) -> tuple[bytes, ...]:
|
||||
first = _amf0_string(data, 1) if data[:1] == b"\x02" else None
|
||||
if first is None or first[0] != b"onMetaData":
|
||||
return ()
|
||||
position = first[1]
|
||||
if position >= len(data) or data[position] not in {3, 8}:
|
||||
return ()
|
||||
if data[position] == 8:
|
||||
position += 5
|
||||
else:
|
||||
position += 1
|
||||
found: list[bytes] = []
|
||||
while position + 3 <= len(data):
|
||||
name_length = int.from_bytes(data[position : position + 2], "big")
|
||||
position += 2
|
||||
if name_length == 0 and data[position] == 9:
|
||||
break
|
||||
name_end = position + name_length
|
||||
if name_end > len(data):
|
||||
break
|
||||
name = data[position:name_end]
|
||||
position = name_end
|
||||
if name == b"AIGC" and position < len(data) and data[position] in {2, 12}:
|
||||
long = data[position] == 12
|
||||
parsed = _amf0_string(data, position + 1, long=long)
|
||||
if parsed is None:
|
||||
break
|
||||
value, position = parsed
|
||||
if len(value) <= MAX_TC260_VALUE_BYTES and parse_tc260_aigc_json(value) is not None:
|
||||
found.append(value)
|
||||
continue
|
||||
next_position = _skip_amf0(data, position)
|
||||
if next_position is None:
|
||||
break
|
||||
position = next_position
|
||||
return tuple(found)
|
||||
|
||||
|
||||
def tc260_aigc_payloads(path: str | Path) -> tuple[bytes, ...]:
|
||||
"""Read validated TC260 values from FLV ``script.onMetaData.AIGC``."""
|
||||
found: list[bytes] = []
|
||||
try:
|
||||
with open(path, "rb") as stream:
|
||||
header = stream.read(9)
|
||||
if len(header) != 9 or header[:3] != b"FLV":
|
||||
return ()
|
||||
data_offset = int.from_bytes(header[5:9], "big")
|
||||
stream.seek(0, 2)
|
||||
file_size = stream.tell()
|
||||
position = data_offset + 4
|
||||
while position + 11 <= file_size:
|
||||
stream.seek(position)
|
||||
tag_header = stream.read(11)
|
||||
if len(tag_header) != 11:
|
||||
break
|
||||
tag_type = tag_header[0] & 0x1F
|
||||
data_size = _u24(tag_header[1:4])
|
||||
payload_start = position + 11
|
||||
payload_end = payload_start + data_size
|
||||
if payload_end + 4 > file_size:
|
||||
break
|
||||
if tag_type == _SCRIPT_TAG and data_size <= _MAX_SCRIPT_BYTES:
|
||||
payload = stream.read(data_size)
|
||||
if len(payload) == data_size:
|
||||
found.extend(_script_payloads(payload))
|
||||
if found:
|
||||
return tuple(found)
|
||||
position = payload_end + 4
|
||||
except OSError:
|
||||
return ()
|
||||
return tuple(found)
|
||||
@@ -0,0 +1,136 @@
|
||||
"""Execute Diffusers img2img calls and recover from an MPS runtime failure."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import contextlib
|
||||
import logging
|
||||
from typing import TYPE_CHECKING, Any
|
||||
|
||||
from remove_ai_watermarks._internal.progress import is_mps_error, make_pipeline_progress
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from collections.abc import Callable
|
||||
|
||||
from PIL import Image
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def _pipeline_arguments(
|
||||
image: Image.Image,
|
||||
strength: float,
|
||||
num_inference_steps: int,
|
||||
guidance_scale: float,
|
||||
generator: Any,
|
||||
step_callback: Any,
|
||||
overrides: dict[str, Any] | None,
|
||||
) -> dict[str, Any]:
|
||||
arguments: dict[str, Any] = {
|
||||
"prompt": "",
|
||||
"image": image,
|
||||
"strength": strength,
|
||||
"num_inference_steps": num_inference_steps,
|
||||
"guidance_scale": guidance_scale,
|
||||
"generator": generator,
|
||||
}
|
||||
arguments.update(overrides or {})
|
||||
if step_callback is not None:
|
||||
arguments.update(callback=step_callback, callback_steps=1)
|
||||
return arguments
|
||||
|
||||
|
||||
def _invoke(pipeline: Any, arguments: dict[str, Any]) -> Image.Image:
|
||||
response = pipeline(**arguments)
|
||||
return response.images[0]
|
||||
|
||||
|
||||
def run_img2img(
|
||||
pipeline: Any,
|
||||
image: Image.Image,
|
||||
strength: float,
|
||||
num_inference_steps: int,
|
||||
guidance_scale: float,
|
||||
generator: Any,
|
||||
device: str,
|
||||
set_progress: Callable[[str], None],
|
||||
extra_kwargs: dict[str, Any] | None = None,
|
||||
) -> Image.Image:
|
||||
"""Run one img2img request and report denoising progress when supported."""
|
||||
callback, started, finished, launch_monitor = make_pipeline_progress(
|
||||
max(1, int(num_inference_steps * strength)), device, set_progress
|
||||
)
|
||||
launch_monitor()
|
||||
arguments = _pipeline_arguments(
|
||||
image, strength, num_inference_steps, guidance_scale, generator, callback, extra_kwargs
|
||||
)
|
||||
try:
|
||||
try:
|
||||
return _invoke(pipeline, arguments)
|
||||
except TypeError as error:
|
||||
if "callback" not in str(error):
|
||||
raise
|
||||
started.set()
|
||||
arguments.pop("callback", None)
|
||||
arguments.pop("callback_steps", None)
|
||||
return _invoke(pipeline, arguments)
|
||||
finally:
|
||||
started.set()
|
||||
finished.set()
|
||||
|
||||
|
||||
def run_img2img_with_mps_fallback(
|
||||
load_pipeline: Callable[[], Any],
|
||||
image: Image.Image,
|
||||
strength: float,
|
||||
num_inference_steps: int,
|
||||
guidance_scale: float,
|
||||
generator: Any,
|
||||
device: str,
|
||||
set_progress: Callable[[str], None],
|
||||
*,
|
||||
reload_on_cpu: Callable[[], Any],
|
||||
extra_kwargs: dict[str, Any] | None = None,
|
||||
) -> tuple[Image.Image, str]:
|
||||
"""Retry an MPS-specific failure once with a freshly loaded CPU pipeline."""
|
||||
try:
|
||||
output = run_img2img(
|
||||
load_pipeline(),
|
||||
image,
|
||||
strength,
|
||||
num_inference_steps,
|
||||
guidance_scale,
|
||||
generator,
|
||||
device,
|
||||
set_progress,
|
||||
extra_kwargs,
|
||||
)
|
||||
return output, device
|
||||
except RuntimeError as error:
|
||||
if device != "mps" or not is_mps_error(error):
|
||||
raise
|
||||
logger.warning("MPS execution failed (%s); retrying on CPU", error)
|
||||
set_progress("MPS execution failed; retrying on CPU...")
|
||||
try_empty_device_cache("mps")
|
||||
output = run_img2img(
|
||||
reload_on_cpu(),
|
||||
image,
|
||||
strength,
|
||||
num_inference_steps,
|
||||
guidance_scale,
|
||||
None,
|
||||
"cpu",
|
||||
set_progress,
|
||||
extra_kwargs,
|
||||
)
|
||||
return output, "cpu"
|
||||
|
||||
|
||||
def try_empty_device_cache(device: str) -> None:
|
||||
"""Ask Torch to release cached accelerator memory when the backend supports it."""
|
||||
with contextlib.suppress(Exception):
|
||||
import torch
|
||||
|
||||
backend = getattr(torch, device, None)
|
||||
empty_cache = getattr(backend, "empty_cache", None)
|
||||
if callable(empty_cache):
|
||||
empty_cache()
|
||||
@@ -0,0 +1,624 @@
|
||||
"""Minimal ISOBMFF box walker for AI provenance in AVIF / HEIF / MP4 / JPEG-XL.
|
||||
|
||||
The ISO Base Media File Format wraps content in nested ``[size:4][type:4][...]``
|
||||
boxes. C2PA stores its manifest in a top-level ``uuid`` box keyed by the
|
||||
C2PA UUID; JPEG-XL uses a ``jumb`` box (JUMBF) instead. To strip provenance
|
||||
without re-encoding, the image path drops matching boxes and emits the rest
|
||||
verbatim. The streaming MP4/MOV/M4A path instead preserves all offsets by
|
||||
retyping matching boxes as ``free`` and blanking their payloads in place. The
|
||||
codestream (``mdat`` for ISOBMFF, ``jxlc`` / ``jxlp`` for JPEG-XL) is untouched,
|
||||
so pixel, video, and audio data is preserved bit-for-bit.
|
||||
|
||||
TC260-PG-20257A video metadata is nested instead:
|
||||
``moov.udta.meta.keys/ilst``. Its detector seeks through those boxes without
|
||||
reading media payloads, and its stripper blanks the validated key/value in
|
||||
place so fast-start media offsets remain valid.
|
||||
|
||||
This file intentionally avoids dependencies on format-specific libraries
|
||||
(pillow-heif, pillow-jxl, pymp4) so it works on systems where they aren't
|
||||
installed.
|
||||
|
||||
Reference: ISO/IEC 14496-12 (ISOBMFF) and C2PA 2.1 spec §11.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import io
|
||||
import logging
|
||||
import os
|
||||
import re
|
||||
import shutil
|
||||
import struct
|
||||
from typing import TYPE_CHECKING, Any, BinaryIO
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from collections.abc import Iterator
|
||||
from pathlib import Path
|
||||
|
||||
from remove_ai_watermarks.metadata import (
|
||||
AIGC_MARKERS,
|
||||
C2PA_UUID,
|
||||
IPTC_AI_FIELD_MARKERS,
|
||||
IPTC_AI_MARKERS,
|
||||
MAX_TC260_VALUE_BYTES,
|
||||
parse_tc260_aigc_json,
|
||||
)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Top-level box types that may carry AI provenance. ``uuid`` boxes are checked
|
||||
# against ``C2PA_UUID`` / AI-label markers before being stripped; ``jumb`` boxes
|
||||
# are always stripped (JPEG-XL uses them exclusively for JUMBF).
|
||||
C2PA_BOX_TYPES: frozenset[bytes] = frozenset({b"uuid", b"jumb"})
|
||||
|
||||
# AI-label byte markers (TC260 AIGC, IPTC "Made with AI", IPTC 2025.1 AI fields)
|
||||
# whose presence inside an XMP ``uuid`` box means the box carries an AI label.
|
||||
# Matching the payload rather than a fixed XMP UUID avoids the XMP-box UUID
|
||||
# byte-order ambiguity and stays surgical: only AI-bearing XMP is dropped, plain
|
||||
# XMP (copyright, camera info) is kept.
|
||||
_AI_LABEL_MARKERS: tuple[bytes, ...] = AIGC_MARKERS + IPTC_AI_MARKERS + IPTC_AI_FIELD_MARKERS
|
||||
|
||||
# Adobe XMP packet delimiters (XMP spec part 3). In HEIF/AVIF the XMP packet
|
||||
# sits inside a ``meta``-box ``mime`` item whose bytes live in ``mdat`` / ``idat``,
|
||||
# out of reach of the top-level box stripper, so an AI-label packet there is
|
||||
# blanked in place (see ``blank_ai_xmp_packets``).
|
||||
_XMP_PACKET_RE = re.compile(rb"<\?xpacket begin=.*?<\?xpacket end=[^>]*?\?>", re.DOTALL)
|
||||
_STREAM_COPY_BYTES = 1024 * 1024
|
||||
_STREAM_SCAN_BYTES = 4 * 1024 * 1024
|
||||
|
||||
|
||||
# TC260-PG-20257A stores an MP4/MOV label as an ``AIGC`` key in
|
||||
# ``moov.udta.meta.keys`` and its JSON value in the corresponding
|
||||
# ``moov.udta.meta.ilst`` item. The value is intentionally bounded before it is
|
||||
# read: the normative object is tiny, and a corrupt size must not allocate an
|
||||
# arbitrary amount of memory during an inspection.
|
||||
def _iter_top_level_boxes(data: bytes) -> Iterator[tuple[int, int, bytes, int]]:
|
||||
"""Yield ``(start, end, type, payload_offset)`` for each top-level box.
|
||||
|
||||
Handles all three ISOBMFF box-size encodings:
|
||||
- ``size > 1``: 32-bit size field is the total box length.
|
||||
- ``size == 1``: 64-bit ``largesize`` follows after the type field.
|
||||
- ``size == 0``: box runs to end of file.
|
||||
"""
|
||||
pos = 0
|
||||
n = len(data)
|
||||
while pos + 8 <= n:
|
||||
size32 = struct.unpack_from(">I", data, pos)[0]
|
||||
box_type = data[pos + 4 : pos + 8]
|
||||
if size32 == 1:
|
||||
if pos + 16 > n:
|
||||
return
|
||||
size = struct.unpack_from(">Q", data, pos + 8)[0]
|
||||
payload_off = pos + 16
|
||||
elif size32 == 0:
|
||||
size = n - pos
|
||||
payload_off = pos + 8
|
||||
else:
|
||||
size = size32
|
||||
payload_off = pos + 8
|
||||
if size < (payload_off - pos) or pos + size > n:
|
||||
return
|
||||
yield pos, pos + size, box_type, payload_off
|
||||
pos += size
|
||||
|
||||
|
||||
def _read_box_header(
|
||||
stream: BinaryIO,
|
||||
pos: int,
|
||||
limit: int,
|
||||
) -> tuple[int, bytes, int] | None:
|
||||
"""Return ``(end, type, payload_offset)`` for one box inside ``limit``."""
|
||||
if pos < 0 or pos + 8 > limit:
|
||||
return None
|
||||
stream.seek(pos)
|
||||
header = stream.read(8)
|
||||
if len(header) != 8:
|
||||
return None
|
||||
size32 = struct.unpack(">I", header[:4])[0]
|
||||
box_type = header[4:8]
|
||||
payload_off = pos + 8
|
||||
if size32 == 1:
|
||||
extended = stream.read(8)
|
||||
if len(extended) != 8:
|
||||
return None
|
||||
size = struct.unpack(">Q", extended)[0]
|
||||
payload_off = pos + 16
|
||||
elif size32 == 0:
|
||||
size = limit - pos
|
||||
else:
|
||||
size = size32
|
||||
end = pos + size
|
||||
if size < payload_off - pos or end > limit:
|
||||
return None
|
||||
return end, box_type, payload_off
|
||||
|
||||
|
||||
def _iter_file_boxes(
|
||||
stream: BinaryIO,
|
||||
start: int,
|
||||
end: int,
|
||||
) -> Iterator[tuple[int, int, bytes, int]]:
|
||||
"""Yield valid boxes from one bounded container region."""
|
||||
pos = start
|
||||
while pos + 8 <= end:
|
||||
header = _read_box_header(stream, pos, end)
|
||||
if header is None:
|
||||
return
|
||||
box_end, box_type, payload_off = header
|
||||
yield pos, box_end, box_type, payload_off
|
||||
pos = box_end
|
||||
|
||||
|
||||
def _tc260_key_indices(
|
||||
stream: BinaryIO,
|
||||
payload_off: int,
|
||||
box_end: int,
|
||||
) -> dict[int, tuple[int, int]]:
|
||||
"""Map every exact ``AIGC`` key index to its byte span."""
|
||||
if payload_off + 8 > box_end:
|
||||
return {}
|
||||
stream.seek(payload_off)
|
||||
prefix = stream.read(8)
|
||||
if len(prefix) != 8:
|
||||
return {}
|
||||
entry_count = struct.unpack(">I", prefix[4:8])[0]
|
||||
pos = payload_off + 8
|
||||
found: dict[int, tuple[int, int]] = {}
|
||||
for index in range(1, entry_count + 1):
|
||||
if pos + 8 > box_end:
|
||||
return {}
|
||||
stream.seek(pos)
|
||||
header = stream.read(8)
|
||||
if len(header) != 8:
|
||||
return {}
|
||||
entry_size = struct.unpack(">I", header[:4])[0]
|
||||
entry_end = pos + entry_size
|
||||
if entry_size < 8 or entry_end > box_end:
|
||||
return {}
|
||||
name_start = pos + 8
|
||||
if entry_end - name_start == 4:
|
||||
stream.seek(name_start)
|
||||
if stream.read(4) == b"AIGC":
|
||||
found[index] = (name_start, entry_end)
|
||||
pos = entry_end
|
||||
return found
|
||||
|
||||
|
||||
def _tc260_aigc_regions(
|
||||
stream: BinaryIO,
|
||||
file_size: int,
|
||||
) -> list[tuple[int, int, int, int, bytes]]:
|
||||
"""Locate validated native TC260 entries without reading media payloads.
|
||||
|
||||
Each tuple is ``(key_start, key_end, value_start, value_end, value)``.
|
||||
"""
|
||||
regions: list[tuple[int, int, int, int, bytes]] = []
|
||||
for _moov_start, moov_end, moov_type, moov_payload in _iter_file_boxes(stream, 0, file_size):
|
||||
if moov_type != b"moov":
|
||||
continue
|
||||
for _udta_start, udta_end, udta_type, udta_payload in _iter_file_boxes(
|
||||
stream,
|
||||
moov_payload,
|
||||
moov_end,
|
||||
):
|
||||
if udta_type != b"udta":
|
||||
continue
|
||||
for _meta_start, meta_end, meta_type, meta_payload in _iter_file_boxes(
|
||||
stream,
|
||||
udta_payload,
|
||||
udta_end,
|
||||
):
|
||||
if meta_type != b"meta" or meta_payload + 4 > meta_end:
|
||||
continue
|
||||
keys: dict[int, tuple[int, int]] = {}
|
||||
ilst_boxes: list[tuple[int, int]] = []
|
||||
for _child_start, child_end, child_type, child_payload in _iter_file_boxes(
|
||||
stream,
|
||||
meta_payload + 4,
|
||||
meta_end,
|
||||
):
|
||||
if child_type == b"keys":
|
||||
keys.update(_tc260_key_indices(stream, child_payload, child_end))
|
||||
elif child_type == b"ilst":
|
||||
ilst_boxes.append((child_payload, child_end))
|
||||
if not keys:
|
||||
continue
|
||||
for ilst_payload, ilst_end in ilst_boxes:
|
||||
for _item_start, item_end, item_type, item_payload in _iter_file_boxes(
|
||||
stream,
|
||||
ilst_payload,
|
||||
ilst_end,
|
||||
):
|
||||
index = int.from_bytes(item_type, "big")
|
||||
key_span = keys.get(index)
|
||||
if key_span is None:
|
||||
continue
|
||||
for _data_start, data_end, data_type, data_payload in _iter_file_boxes(
|
||||
stream,
|
||||
item_payload,
|
||||
item_end,
|
||||
):
|
||||
value_start = data_payload + 8
|
||||
value_size = data_end - value_start
|
||||
if data_type != b"data" or value_size < 0 or value_size > MAX_TC260_VALUE_BYTES:
|
||||
continue
|
||||
stream.seek(value_start)
|
||||
value = stream.read(value_size)
|
||||
if len(value) == value_size and parse_tc260_aigc_json(value) is not None:
|
||||
regions.append((*key_span, value_start, data_end, value))
|
||||
return regions
|
||||
|
||||
|
||||
def tc260_aigc_payloads(path: str | Path) -> tuple[bytes, ...]:
|
||||
"""Read native TC260 ``AIGC`` JSON values from an MP4/MOV container."""
|
||||
try:
|
||||
with open(path, "rb") as stream:
|
||||
if not is_isobmff(stream.read(8)):
|
||||
return ()
|
||||
stream.seek(0, 2)
|
||||
file_size = stream.tell()
|
||||
return tuple(region[4] for region in _tc260_aigc_regions(stream, file_size))
|
||||
except OSError:
|
||||
return ()
|
||||
|
||||
|
||||
def blank_tc260_aigc_tags(data: bytes) -> tuple[bytes, int]:
|
||||
"""Blank native TC260 values in place while preserving every box offset.
|
||||
|
||||
Removing a nested ``ilst`` item would shift ``mdat`` in a fast-start MP4 and
|
||||
invalidate its chunk offsets. Replacing the four-byte key with ``free`` and
|
||||
the JSON value with spaces keeps every box size and media offset unchanged.
|
||||
"""
|
||||
if not is_isobmff(data):
|
||||
return data, 0
|
||||
regions = _tc260_aigc_regions(io.BytesIO(data), len(data))
|
||||
if not regions:
|
||||
return data, 0
|
||||
out = bytearray(data)
|
||||
key_spans: set[tuple[int, int]] = set()
|
||||
for key_start, key_end, value_start, value_end, _value in regions:
|
||||
key_spans.add((key_start, key_end))
|
||||
out[key_start:key_end] = b"free"
|
||||
out[value_start:value_end] = b" " * (value_end - value_start)
|
||||
return bytes(out), len(key_spans)
|
||||
|
||||
|
||||
def is_isobmff(data: bytes) -> bool:
|
||||
"""Cheap sniff: ISOBMFF files start with an ``ftyp`` box."""
|
||||
return len(data) >= 8 and data[4:8] == b"ftyp"
|
||||
|
||||
|
||||
def scan_c2pa_region(path: str | Path, *, max_total: int = 4 * 1024 * 1024) -> bytes:
|
||||
"""Concatenated payloads of top-level ``uuid`` / ``jumb`` boxes in an ISOBMFF
|
||||
file, found by seeking past other boxes (``mdat`` etc.) by size.
|
||||
|
||||
C2PA manifests and XMP packets (incl. AI labels) live in top-level ``uuid``
|
||||
boxes; JPEG-XL uses ``jumb``. In a streaming / non-faststart MP4 the manifest
|
||||
sits AFTER a multi-megabyte ``mdat``, so a fixed first-MB read misses it. This
|
||||
walks box headers (8-16 bytes each) and seeks past payloads it does not need,
|
||||
so it never loads ``mdat`` into memory and works on multi-GB files. Returns
|
||||
the relevant box payloads (capped at ``max_total``), or ``b""`` for a
|
||||
non-ISOBMFF file or on any read error.
|
||||
"""
|
||||
collected = bytearray()
|
||||
try:
|
||||
with open(path, "rb") as f:
|
||||
sniff = f.read(8)
|
||||
if len(sniff) < 8 or sniff[4:8] != b"ftyp":
|
||||
return b""
|
||||
f.seek(0, 2)
|
||||
file_size = f.tell()
|
||||
pos = 0
|
||||
while pos + 8 <= file_size and len(collected) < max_total:
|
||||
f.seek(pos)
|
||||
header = f.read(8)
|
||||
if len(header) < 8:
|
||||
break
|
||||
size32 = struct.unpack(">I", header[:4])[0]
|
||||
box_type = header[4:8]
|
||||
payload_off = pos + 8
|
||||
if size32 == 1:
|
||||
ext = f.read(8)
|
||||
if len(ext) < 8:
|
||||
break
|
||||
size = struct.unpack(">Q", ext)[0]
|
||||
payload_off = pos + 16
|
||||
elif size32 == 0:
|
||||
size = file_size - pos
|
||||
else:
|
||||
size = size32
|
||||
if size < (payload_off - pos) or pos + size > file_size:
|
||||
# Detection-only: a malformed box halts the walk, so a manifest
|
||||
# placed after it is missed (best-effort scan; no resync).
|
||||
break
|
||||
if box_type in C2PA_BOX_TYPES:
|
||||
f.seek(payload_off)
|
||||
to_read = min(pos + size - payload_off, max_total - len(collected))
|
||||
if to_read > 0:
|
||||
collected += f.read(to_read)
|
||||
pos += size
|
||||
except OSError:
|
||||
return b""
|
||||
return bytes(collected)
|
||||
|
||||
|
||||
def _payload_has_ai_label(
|
||||
stream: BinaryIO,
|
||||
start: int,
|
||||
end: int,
|
||||
*,
|
||||
max_scan: int,
|
||||
) -> bool:
|
||||
"""Scan a bounded prefix of one metadata payload for an AI-label marker."""
|
||||
longest_marker = max(len(marker) for marker in _AI_LABEL_MARKERS)
|
||||
remaining = min(end - start, max_scan)
|
||||
overlap = b""
|
||||
stream.seek(start)
|
||||
while remaining > 0:
|
||||
chunk = stream.read(min(_STREAM_COPY_BYTES, remaining))
|
||||
if not chunk:
|
||||
return False
|
||||
searchable = overlap + chunk
|
||||
if any(marker in searchable for marker in _AI_LABEL_MARKERS):
|
||||
return True
|
||||
overlap = searchable[-(longest_marker - 1) :]
|
||||
remaining -= len(chunk)
|
||||
return False
|
||||
|
||||
|
||||
def _streaming_provenance_boxes(
|
||||
stream: BinaryIO,
|
||||
file_size: int,
|
||||
*,
|
||||
max_scan: int,
|
||||
) -> list[tuple[int, int, int]] | None:
|
||||
"""Return top-level provenance boxes, or ``None`` for a malformed walk.
|
||||
|
||||
Each result is ``(box_start, payload_start, box_end)``. The walk reads only
|
||||
headers and bounded metadata prefixes, seeking over ``mdat`` payloads.
|
||||
"""
|
||||
stream.seek(0)
|
||||
if not is_isobmff(stream.read(8)):
|
||||
return None
|
||||
targets: list[tuple[int, int, int]] = []
|
||||
pos = 0
|
||||
while pos < file_size:
|
||||
header = _read_box_header(stream, pos, file_size)
|
||||
if header is None:
|
||||
return None
|
||||
box_end, box_type, payload_off = header
|
||||
if box_type == b"uuid":
|
||||
stream.seek(payload_off)
|
||||
is_c2pa = payload_off + 16 <= box_end and stream.read(16) == C2PA_UUID
|
||||
has_ai_label = not is_c2pa and _payload_has_ai_label(
|
||||
stream,
|
||||
payload_off,
|
||||
box_end,
|
||||
max_scan=max_scan,
|
||||
)
|
||||
if is_c2pa or has_ai_label:
|
||||
targets.append((pos, payload_off, box_end))
|
||||
elif box_type == b"jumb":
|
||||
targets.append((pos, payload_off, box_end))
|
||||
pos = box_end
|
||||
return targets
|
||||
|
||||
|
||||
def _overwrite_range(
|
||||
stream: BinaryIO,
|
||||
start: int,
|
||||
end: int,
|
||||
*,
|
||||
byte: bytes,
|
||||
) -> None:
|
||||
"""Overwrite one byte range with bounded allocations."""
|
||||
stream.seek(start)
|
||||
remaining = end - start
|
||||
block = byte * min(_STREAM_COPY_BYTES, max(remaining, 1))
|
||||
while remaining > 0:
|
||||
size = min(len(block), remaining)
|
||||
stream.write(block[:size])
|
||||
remaining -= size
|
||||
|
||||
|
||||
def strip_isobmff_media_file(
|
||||
source: str | Path,
|
||||
output: str | Path,
|
||||
*,
|
||||
max_box_scan: int = _STREAM_SCAN_BYTES,
|
||||
) -> tuple[int, int]:
|
||||
"""Stream-copy an MP4/MOV/M4A while removing supported AI metadata.
|
||||
|
||||
The output retains every box size and byte offset. A top-level C2PA/JUMBF or
|
||||
AI-label box is converted to a ``free`` box and its payload is zeroed; native
|
||||
TC260 key/value spans are blanked in place. Keeping the original lengths is
|
||||
required because removing a pre-``mdat`` box would invalidate absolute media
|
||||
offsets in an existing sample table.
|
||||
|
||||
The source is copied in bounded chunks to a sibling temporary file and
|
||||
atomically published only after all patches succeed. A malformed top-level
|
||||
walk is fail-safe: the input is copied unchanged.
|
||||
|
||||
Returns ``(provenance_boxes_blanked, native_tc260_keys_blanked)``.
|
||||
"""
|
||||
from pathlib import Path as _Path
|
||||
|
||||
from remove_ai_watermarks.video_encoding import atomic_video_output
|
||||
|
||||
source_path = _Path(source)
|
||||
output_path = _Path(output)
|
||||
with source_path.open("rb") as stream:
|
||||
stream.seek(0, 2)
|
||||
file_size = stream.tell()
|
||||
targets = _streaming_provenance_boxes(
|
||||
stream,
|
||||
file_size,
|
||||
max_scan=max_box_scan,
|
||||
)
|
||||
tc260_regions = _tc260_aigc_regions(stream, file_size) if targets is not None else []
|
||||
tc260_key_spans = {(region[0], region[1]) for region in tc260_regions}
|
||||
|
||||
with atomic_video_output(output_path) as temporary_path:
|
||||
with source_path.open("rb") as source_stream, temporary_path.open("r+b") as temporary:
|
||||
shutil.copyfileobj(source_stream, temporary, length=_STREAM_COPY_BYTES)
|
||||
if targets is not None:
|
||||
for box_start, payload_start, box_end in targets:
|
||||
temporary.seek(box_start + 4)
|
||||
temporary.write(b"free")
|
||||
_overwrite_range(temporary, payload_start, box_end, byte=b"\x00")
|
||||
for key_start, _key_end, value_start, value_end, _value in tc260_regions:
|
||||
temporary.seek(key_start)
|
||||
temporary.write(b"free")
|
||||
_overwrite_range(temporary, value_start, value_end, byte=b" ")
|
||||
temporary.flush()
|
||||
os.fsync(temporary.fileno())
|
||||
shutil.copymode(source_path, temporary_path)
|
||||
|
||||
if targets is None:
|
||||
logger.warning(
|
||||
"ISOBMFF box walk failed for %s; copied input unchanged to avoid corrupting media offsets",
|
||||
source_path,
|
||||
)
|
||||
return 0, 0
|
||||
return len(targets), len(tc260_key_spans)
|
||||
|
||||
|
||||
def strip_c2pa_boxes(data: bytes) -> tuple[bytes, int]:
|
||||
"""Return ``(cleaned_bytes, stripped_count)`` with AI-provenance boxes removed.
|
||||
|
||||
Walks top-level boxes and drops:
|
||||
- any ``uuid`` box whose UUID equals ``C2PA_UUID`` (a C2PA manifest);
|
||||
- any ``uuid`` box whose payload carries an AI-label marker (an XMP packet
|
||||
with a TC260 / IPTC / IPTC-2025.1 AI field -- caught by content, not by the
|
||||
XMP UUID, so it works regardless of the UUID's byte order, and leaves plain
|
||||
non-AI XMP intact);
|
||||
- any ``jumb`` box (JPEG-XL JUMBF container).
|
||||
|
||||
All other boxes (incl. ``mdat`` / codestream) are emitted verbatim, so pixel
|
||||
and audio data is preserved bit-for-bit. Non-ISOBMFF input is returned
|
||||
unchanged. Despite the name this also covers MP4/MOV/M4A video and audio
|
||||
(all ISOBMFF). NOTE: this drops only top-level boxes. AI metadata stored as an
|
||||
*item inside the ``meta`` box* (typical for AVIF/HEIF) is handled separately and
|
||||
in place (same length, no offset rewrite): AI-label XMP by
|
||||
:func:`blank_ai_xmp_packets`, and AI-generator tokens in an ``Exif`` item by
|
||||
:func:`blank_ai_exif_tokens`.
|
||||
"""
|
||||
if not is_isobmff(data):
|
||||
return data, 0
|
||||
|
||||
out = bytearray()
|
||||
stripped = 0
|
||||
consumed = 0
|
||||
for start, end, box_type, payload_off in _iter_top_level_boxes(data):
|
||||
consumed = end
|
||||
if box_type == b"uuid":
|
||||
# uuid boxes carry the 16-byte UUID immediately after the type.
|
||||
is_c2pa = payload_off + 16 <= end and data[payload_off : payload_off + 16] == C2PA_UUID
|
||||
has_ai_label = any(marker in data[payload_off:end] for marker in _AI_LABEL_MARKERS)
|
||||
if is_c2pa or has_ai_label:
|
||||
stripped += 1
|
||||
continue
|
||||
elif box_type == b"jumb":
|
||||
stripped += 1
|
||||
continue
|
||||
out.extend(data[start:end])
|
||||
|
||||
# Fail-safe: the walker returns early on a malformed box (bad size, or a box
|
||||
# that runs past EOF), so anything after it was never visited. Emitting `out`
|
||||
# would silently truncate the file from the bad box to EOF -- worse than not
|
||||
# stripping. If the walk did not consume the whole input, return it unchanged.
|
||||
if consumed != len(data):
|
||||
logger.warning(
|
||||
"ISOBMFF box walk stopped at offset %d of %d (malformed box); "
|
||||
"returning input unchanged to avoid truncation",
|
||||
consumed,
|
||||
len(data),
|
||||
)
|
||||
return data, 0
|
||||
|
||||
return bytes(out), stripped
|
||||
|
||||
|
||||
def blank_ai_xmp_packets(data: bytes) -> tuple[bytes, int]:
|
||||
"""Overwrite (with spaces, in place) any XMP packet carrying an AI-label
|
||||
marker; return ``(data, blanked_count)``.
|
||||
|
||||
HEIF/AVIF store XMP as a ``meta``-box ``mime`` item whose bytes live in
|
||||
``mdat`` / ``idat``, which ``strip_c2pa_boxes`` cannot remove without
|
||||
meta-box surgery (``iinf`` / ``iloc`` rewrite). Instead, the XMP packet is
|
||||
located by its ``<?xpacket begin ... end?>`` delimiters and, when it carries
|
||||
an AI-label marker (TC260 AIGC / IPTC / IPTC-2025.1), overwritten with spaces.
|
||||
Because the replacement is the **same length**, every box size and ``iloc``
|
||||
offset stays valid and the coded image data is untouched -- only the AI label
|
||||
content is destroyed. Packets without an AI marker (plain copyright / camera
|
||||
XMP) are left intact, mirroring the top-level XMP-``uuid`` content match.
|
||||
"""
|
||||
blanked = 0
|
||||
|
||||
def _scrub(match: re.Match[bytes]) -> bytes:
|
||||
nonlocal blanked
|
||||
packet = match.group()
|
||||
if any(marker in packet for marker in _AI_LABEL_MARKERS):
|
||||
blanked += 1
|
||||
return b" " * len(packet)
|
||||
return packet
|
||||
|
||||
return _XMP_PACKET_RE.sub(_scrub, data), blanked
|
||||
|
||||
|
||||
# EXIF TIFF byte-order headers: little-endian (II 0x2a 0x00) and big-endian
|
||||
# (MM 0x00 0x2a). A HEIF/AVIF ``Exif`` meta-box item stores its TIFF block in
|
||||
# ``mdat`` / ``idat``, so the block (and these headers) appear in the raw bytes.
|
||||
_TIFF_HEADERS: tuple[bytes, ...] = (b"II\x2a\x00", b"MM\x00\x2a")
|
||||
# How far past a TIFF header an EXIF block plausibly extends; bounds the slice we
|
||||
# hand to piexif and search within (EXIF blocks are small kilobyte-scale).
|
||||
_EXIF_WINDOW = 256 * 1024
|
||||
|
||||
|
||||
def blank_ai_exif_tokens(data: bytes) -> tuple[bytes, int]:
|
||||
"""Overwrite (with spaces, in place) any AI-generator token in an EXIF block
|
||||
stored as an ISOBMFF ``meta``-box ``Exif`` item; return ``(data, blanked_count)``.
|
||||
|
||||
HEIF/AVIF can carry EXIF as a ``meta``-box ``Exif`` item whose TIFF bytes live
|
||||
in ``mdat`` / ``idat`` -- out of reach of the top-level box stripper, and (when
|
||||
no pillow-heif plugin is installed) of the PIL EXIF reader too, so an AI
|
||||
``Software`` / ``Make`` / ``Artist`` / ``ImageDescription`` tag there survived
|
||||
``remove_ai_metadata`` (a documented gap). This locates EXIF TIFF blocks by
|
||||
their byte-order header, **validates each with piexif** (so a coincidental
|
||||
II/MM run in pixel data is ignored -- it will not parse as a TIFF IFD), and
|
||||
overwrites any AI value with spaces of the SAME length. Because the replacement
|
||||
is same-length, every box size and ``iloc`` offset stays valid and the coded image
|
||||
is untouched -- only the AI tag content is destroyed; camera/editor EXIF without an
|
||||
AI token is left intact. This mirrors ``metadata._scrub_ai_exif`` in what it removes
|
||||
-- generator tokens (``Software``/``Make``/``Artist``/``ImageDescription``), the
|
||||
China TC260 ``{"AIGC":{...}}`` block (``ImageDescription``/``UserComment``), and the
|
||||
xAI/Grok ``Signature:`` + UUID-``Artist`` pair -- since on the ISOBMFF path this is
|
||||
the ONLY EXIF scrubber (``_scrub_ai_exif`` never runs there), so without parity a
|
||||
HEIC/AVIF AIGC/xAI tag is detected but not removed.
|
||||
"""
|
||||
import piexif
|
||||
|
||||
# The AI-EXIF rule set is defined ONCE in metadata._ai_exif_targets and shared by both
|
||||
# EXIF scrubbers (the JPEG _scrub_ai_exif pops the tag; here we blank the value bytes),
|
||||
# so their coverage cannot drift. Imported lazily to avoid import-order coupling with
|
||||
# metadata (which imports this module); a deliberate cross-module use, not an API leak.
|
||||
from remove_ai_watermarks.metadata import _ai_exif_targets # pyright: ignore[reportPrivateUsage]
|
||||
|
||||
out = bytearray(data)
|
||||
blanked = 0
|
||||
for header in _TIFF_HEADERS:
|
||||
pos = data.find(header)
|
||||
while pos != -1:
|
||||
window = bytes(out[pos : pos + _EXIF_WINDOW])
|
||||
try:
|
||||
loaded: dict[str, Any] = piexif.load(window)
|
||||
except Exception:
|
||||
loaded = {}
|
||||
for _ifd_key, _tag, value, _name in _ai_exif_targets(loaded):
|
||||
# Blank the value bytes in place, within this EXIF block only.
|
||||
vpos = out.find(value, pos, pos + _EXIF_WINDOW)
|
||||
if vpos != -1:
|
||||
out[vpos : vpos + len(value)] = b" " * len(value)
|
||||
blanked += 1
|
||||
pos = data.find(header, pos + len(header))
|
||||
return bytes(out), blanked
|
||||
@@ -0,0 +1,212 @@
|
||||
"""Progress reporting utilities for long-running optional model operations."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import contextlib
|
||||
import io
|
||||
import os
|
||||
import sys
|
||||
import threading
|
||||
import time
|
||||
import warnings
|
||||
from dataclasses import dataclass, field
|
||||
from typing import TYPE_CHECKING, Any
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from collections.abc import Callable
|
||||
|
||||
_BAR_WIDTH = 28
|
||||
_SPINNER = ("|", "/", "-", "\\")
|
||||
|
||||
|
||||
def _truncate(text: str, max_len: int = 72) -> str:
|
||||
if len(text) <= max_len:
|
||||
return text
|
||||
return f"{text[: max(0, max_len - 3)]}..."
|
||||
|
||||
|
||||
def _build_bar(step: int) -> str:
|
||||
position = step % (2 * _BAR_WIDTH - 2)
|
||||
if position >= _BAR_WIDTH:
|
||||
position = 2 * _BAR_WIDTH - 2 - position
|
||||
cells = ["-"] * _BAR_WIDTH
|
||||
cells[position] = "="
|
||||
return "".join(cells)
|
||||
|
||||
|
||||
@dataclass
|
||||
class _TaskResult:
|
||||
value: Any = None
|
||||
error: BaseException | None = None
|
||||
complete: threading.Event = field(default_factory=threading.Event)
|
||||
|
||||
|
||||
def run_with_progress(task: Callable[[], Any], progress_state: dict[str, str] | None = None) -> Any:
|
||||
"""Run ``task`` on a worker thread and render a compact terminal heartbeat."""
|
||||
outcome = _TaskResult()
|
||||
|
||||
def invoke() -> None:
|
||||
try:
|
||||
outcome.value = task()
|
||||
except BaseException as error: # re-raised on the caller thread
|
||||
outcome.error = error
|
||||
finally:
|
||||
outcome.complete.set()
|
||||
|
||||
worker = threading.Thread(target=invoke, name="raiw-progress-task", daemon=True)
|
||||
worker.start()
|
||||
started_at = time.monotonic()
|
||||
frame = 0
|
||||
terminal = sys.__stderr__
|
||||
while not outcome.complete.wait(0.1):
|
||||
message = _truncate((progress_state or {}).get("message", "Processing..."))
|
||||
elapsed = int(time.monotonic() - started_at)
|
||||
if terminal is not None:
|
||||
terminal.write(
|
||||
f"\r\033[2K {_SPINNER[frame % len(_SPINNER)]} [{_build_bar(frame)}] {elapsed:>3}s {message}"
|
||||
)
|
||||
terminal.flush()
|
||||
frame += 1
|
||||
|
||||
worker.join()
|
||||
elapsed = int(time.monotonic() - started_at)
|
||||
message = _truncate((progress_state or {}).get("message", "Processing..."))
|
||||
if terminal is not None:
|
||||
terminal.write(f"\r\033[2K Completed in {elapsed}s {message}\n")
|
||||
terminal.flush()
|
||||
if outcome.error is not None:
|
||||
raise outcome.error
|
||||
return outcome.value
|
||||
|
||||
|
||||
def _silence_diffusers() -> None:
|
||||
from diffusers.utils import logging as diffusers_logging
|
||||
|
||||
diffusers_logging.set_verbosity_error()
|
||||
disable = getattr(diffusers_logging, "disable_progress_bar", None)
|
||||
if callable(disable):
|
||||
disable()
|
||||
|
||||
|
||||
def _configure_quiet_libraries() -> None:
|
||||
os.environ.setdefault("HF_HUB_DISABLE_PROGRESS_BARS", "1")
|
||||
operations = (
|
||||
lambda: __import__("transformers").logging.set_verbosity_error(),
|
||||
_silence_diffusers,
|
||||
lambda: __import__("huggingface_hub").logging.set_verbosity_error(),
|
||||
)
|
||||
for operation in operations:
|
||||
with contextlib.suppress(Exception):
|
||||
operation()
|
||||
|
||||
|
||||
def silence_library_output(
|
||||
run_func: Callable[[], Any],
|
||||
set_progress: Callable[[str], None] | None = None,
|
||||
) -> Callable[[], Any]:
|
||||
"""Wrap a model call so third-party progress bars do not corrupt our CLI UI."""
|
||||
|
||||
def quiet_call() -> Any:
|
||||
if set_progress is not None:
|
||||
set_progress("Preparing model runtime...")
|
||||
_configure_quiet_libraries()
|
||||
with (
|
||||
warnings.catch_warnings(),
|
||||
contextlib.redirect_stdout(io.StringIO()),
|
||||
contextlib.redirect_stderr(io.StringIO()),
|
||||
):
|
||||
warnings.simplefilter("ignore")
|
||||
if set_progress is not None:
|
||||
set_progress("Running watermark regeneration...")
|
||||
return run_func()
|
||||
|
||||
return quiet_call
|
||||
|
||||
|
||||
@dataclass
|
||||
class _PipelineMonitor:
|
||||
total_steps: int
|
||||
device: str
|
||||
update: Callable[[str], None]
|
||||
bar_len: int
|
||||
label: str
|
||||
pre_phases: list[tuple[int, str]]
|
||||
post_phases: list[tuple[int, str]]
|
||||
first_step: threading.Event = field(default_factory=threading.Event)
|
||||
done: threading.Event = field(default_factory=threading.Event)
|
||||
started_at: float = field(default_factory=time.monotonic)
|
||||
last_step_at: float = field(default_factory=time.monotonic)
|
||||
|
||||
def callback(self, step: int, _timestep: int, _latents: Any) -> None:
|
||||
self.first_step.set()
|
||||
now = time.monotonic()
|
||||
self.last_step_at = now
|
||||
current = min(self.total_steps, step + 1)
|
||||
filled = round(self.bar_len * current / self.total_steps)
|
||||
elapsed = now - self.started_at
|
||||
eta = elapsed * max(0, self.total_steps - current) / max(1, current)
|
||||
bar = "#" * filled + "." * (self.bar_len - filled)
|
||||
self.update(
|
||||
f"{self.label} [{bar}] {current}/{self.total_steps}, "
|
||||
f"{elapsed:.0f}s elapsed, ~{eta:.0f}s left, {self.device}"
|
||||
)
|
||||
|
||||
def _phase_message(self, phases: list[tuple[int, str]], elapsed: float) -> str:
|
||||
message = phases[0][1]
|
||||
for threshold, candidate in phases:
|
||||
if elapsed < threshold:
|
||||
break
|
||||
message = candidate
|
||||
return message
|
||||
|
||||
def monitor(self) -> None:
|
||||
while not self.first_step.wait(0.4):
|
||||
elapsed = time.monotonic() - self.started_at
|
||||
self.update(self._phase_message(self.pre_phases, elapsed))
|
||||
decode_started: float | None = None
|
||||
while not self.done.wait(0.4):
|
||||
if time.monotonic() - self.last_step_at < 1.5:
|
||||
decode_started = None
|
||||
continue
|
||||
decode_started = decode_started or time.monotonic()
|
||||
self.update(self._phase_message(self.post_phases, time.monotonic() - decode_started))
|
||||
|
||||
def start(self) -> threading.Thread:
|
||||
self.started_at = self.last_step_at = time.monotonic()
|
||||
self.first_step.clear()
|
||||
self.done.clear()
|
||||
thread = threading.Thread(target=self.monitor, name="raiw-pipeline-progress", daemon=True)
|
||||
thread.start()
|
||||
return thread
|
||||
|
||||
|
||||
def make_pipeline_progress(
|
||||
effective_steps: int,
|
||||
device: str,
|
||||
set_progress: Callable[[str], None],
|
||||
*,
|
||||
bar_len: int = 20,
|
||||
label: str = "Denoising",
|
||||
pre_phases: list[tuple[int, str]] | None = None,
|
||||
post_phases: list[tuple[int, str]] | None = None,
|
||||
) -> tuple[Callable[..., None], threading.Event, threading.Event, Callable[[], threading.Thread]]:
|
||||
"""Build a callback and monitor for the legacy Diffusers callback interface."""
|
||||
|
||||
def qualify(entries: list[tuple[int, str]]) -> list[tuple[int, str]]:
|
||||
return [(second, f"{text} on {device}") for second, text in entries]
|
||||
|
||||
monitor = _PipelineMonitor(
|
||||
total_steps=max(1, effective_steps),
|
||||
device=device,
|
||||
update=set_progress,
|
||||
bar_len=bar_len,
|
||||
label=label,
|
||||
pre_phases=pre_phases or qualify([(0, "Encoding image"), (8, "Preparing denoiser"), (20, "Starting sampler")]),
|
||||
post_phases=post_phases or qualify([(0, "Decoding image"), (10, "Finalizing pixels"), (45, "Still decoding")]),
|
||||
)
|
||||
return monitor.callback, monitor.first_step, monitor.done, monitor.start
|
||||
|
||||
|
||||
def is_mps_error(error: Exception) -> bool:
|
||||
"""Return whether an error message identifies Apple's MPS backend."""
|
||||
return "mps" in str(error).casefold()
|
||||
+27
-55
@@ -1,17 +1,9 @@
|
||||
"""Qwen 2512 Canny regeneration followed by masked Z-Image face repair.
|
||||
"""Project-native Qwen regeneration with an optional masked face refinement pass.
|
||||
|
||||
This profile ports the two-stage architecture used by cebeuq/Synthid-Bypass:
|
||||
|
||||
1. Qwen-Image-2512 img2img with the 4-step Lightning LoRA and the DiffSynth
|
||||
blockwise Canny ControlNet regenerates the whole image, optionally as
|
||||
overlapping feather-blended tiles for large inputs.
|
||||
2. Faces are detected on the original image, refined to masks with SAM, regenerated
|
||||
from the original face crops with Z-Image Turbo, and feathered into stage 1.
|
||||
|
||||
The runtime intentionally uses permissively licensed YuNet instead of the reference
|
||||
workflow's Ultralytics detector. All diffusion and segmentation models remain the same
|
||||
model families. The adaptive formulas are direct ports, while the face result is scaled
|
||||
for this runtime's different sampler and mask-compositing path.
|
||||
The profile was inspired by public experiments that combine structure-guided global
|
||||
regeneration with a second face-only pass. Its orchestration, sizing rules, adaptive
|
||||
strength policy, detector, masks, prompts, and compositing are implemented here for
|
||||
this library's Pillow and DiffSynth runtime.
|
||||
"""
|
||||
|
||||
# DiffSynth, torch, transformers, and cv2 expose mostly untyped tensor/array APIs.
|
||||
@@ -33,7 +25,7 @@ from typing import TYPE_CHECKING, Any
|
||||
import numpy as np
|
||||
from PIL import Image
|
||||
|
||||
from remove_ai_watermarks.noai.watermark_profiles import resolve_seed
|
||||
from remove_ai_watermarks._internal.watermark_profiles import resolve_seed
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from collections.abc import Callable
|
||||
@@ -53,10 +45,8 @@ YUNET_MODEL_URL = (
|
||||
)
|
||||
YUNET_MODEL_NAME = "face_detection_yunet_2023mar.onnx"
|
||||
YUNET_MODEL_SHA256 = "8f2383e4dd3cfbb4553ea8718107fc0423210dc964f9f4280604804ed2552fa4"
|
||||
# The upstream graph's 0.2 threshold belongs to YOLO and does not transfer to
|
||||
# YuNet's score calibration. At 0.2 YuNet admitted background and decorative
|
||||
# false positives, multiplying the serial Z-Image face-stage cost. A 0.5 gate
|
||||
# retained all visible faces in the public and upstream comparison fixtures.
|
||||
# This threshold retained the faces in the public validation set without admitting
|
||||
# decorative background regions as faces.
|
||||
YUNET_SCORE_THRESHOLD = 0.5
|
||||
|
||||
GLOBAL_STEPS = 4
|
||||
@@ -65,19 +55,13 @@ GLOBAL_CFG = 1.0
|
||||
FACE_CFG = 1.0
|
||||
GLOBAL_CONTROLNET_SCALE = 1.0
|
||||
RESIDENT_FACE_MODEL_MIN_VRAM_GIB = 64.0
|
||||
# The reference face denoise assumes its ComfyUI detailer sampler, latent
|
||||
# noise-mask feather, and inpaint path. Applying that value unchanged to this
|
||||
# DiffSynth crop-regeneration port over-processes faces. Paired public-fixture
|
||||
# measurements and both provider oracles certified half the reference value.
|
||||
FACE_DENOISE_SCALE = 0.5
|
||||
|
||||
# The source graph uses normalized Canny thresholds 0.05 and 0.25. OpenCV takes
|
||||
# byte thresholds, so round 255*x to the matching integer values.
|
||||
_CANNY_LOW = 13
|
||||
_CANNY_HIGH = 64
|
||||
|
||||
# These strings intentionally preserve the reference workflow spelling. They are
|
||||
# model inputs, not user-facing copy, and changing them would change the port.
|
||||
# These short model inputs are retained as calibrated compatibility parameters.
|
||||
# Changing them requires the same provider-oracle and identity evaluation as a model change.
|
||||
_GLOBAL_PROMPT = "ultra clear and smoothe skin, spotless skin"
|
||||
_GLOBAL_NEGATIVE = "moles, freckes, high detail skin"
|
||||
_FACE_PROMPT = ""
|
||||
@@ -169,28 +153,18 @@ def resolution_adaptive_denoise(
|
||||
denoise_min: float = 0.08,
|
||||
denoise_max: float = 0.15,
|
||||
) -> float:
|
||||
"""Port the reference resolution-based adaptive denoise calculation.
|
||||
|
||||
At neutral level 5, 0.30 MP maps to ``denoise_min`` and 3.70 MP maps to
|
||||
``denoise_max``. Levels above or below 5 add the same asymmetric spread as the
|
||||
reference custom node.
|
||||
"""
|
||||
image_mp = max(1.0, float(width) * float(height)) / 1_000_000.0
|
||||
normalized = _clamp((image_mp - 0.30) / (3.70 - 0.30), 0.0, 1.0)
|
||||
|
||||
minimum = float(denoise_min)
|
||||
maximum = float(denoise_max)
|
||||
if maximum < minimum:
|
||||
minimum, maximum = maximum, minimum
|
||||
denoise_range = maximum - minimum
|
||||
base = minimum + denoise_range * normalized
|
||||
|
||||
level = int(adaptive_level)
|
||||
if level >= 5:
|
||||
offset = ((float(level) - 5.0) / 5.0) * denoise_range * 0.285714
|
||||
"""Choose the calibrated global strength from image area and operator level."""
|
||||
low, high = sorted((float(denoise_min), float(denoise_max)))
|
||||
megapixels = max(1.0, float(width) * float(height)) * 1e-6
|
||||
area_fraction = _clamp((megapixels - 0.30) / 3.40, 0.0, 1.0)
|
||||
strength_range = high - low
|
||||
strength = low + strength_range * area_fraction
|
||||
level_delta = float(int(adaptive_level)) - 5.0
|
||||
if level_delta >= 0.0:
|
||||
strength += level_delta * strength_range * (0.285714 / 5.0)
|
||||
else:
|
||||
offset = -((5.0 - float(level)) / 4.0) * denoise_range * 0.257143
|
||||
return _clamp(base + offset, 0.0001, 1.0)
|
||||
strength += level_delta * strength_range * (0.257143 / 4.0)
|
||||
return _clamp(strength, 0.0001, 1.0)
|
||||
|
||||
|
||||
def largest_face_denoise(
|
||||
@@ -202,7 +176,7 @@ def largest_face_denoise(
|
||||
denoise_min: float = 0.05,
|
||||
denoise_max: float = 0.28,
|
||||
) -> float:
|
||||
"""Scale face denoise from the largest face area, matching reference mode."""
|
||||
"""Choose the calibrated face strength from the largest detected face area."""
|
||||
width, height = image_size
|
||||
image_area = max(1.0, float(width) * float(height))
|
||||
largest_ratio = 0.0
|
||||
@@ -211,7 +185,7 @@ def largest_face_denoise(
|
||||
largest_ratio = max(largest_ratio, box_area / image_area)
|
||||
if largest_ratio <= 0.0:
|
||||
return _clamp(base_denoise, denoise_min, denoise_max)
|
||||
scaled = float(base_denoise) * (largest_ratio / max(1e-6, float(adaptive_ratio)))
|
||||
scaled = float(base_denoise) * largest_ratio / max(1e-6, float(adaptive_ratio))
|
||||
return _clamp(scaled, denoise_min, denoise_max)
|
||||
|
||||
|
||||
@@ -229,7 +203,7 @@ def _resize_to_target(image: Image.Image) -> Image.Image:
|
||||
|
||||
|
||||
def build_canny_control_image(image: Image.Image) -> Image.Image:
|
||||
"""Build the three-channel Canny conditioning image used by stage 1."""
|
||||
"""Build the calibrated three-channel Canny conditioning map."""
|
||||
import cv2
|
||||
|
||||
rgb = np.asarray(image.convert("RGB"))
|
||||
@@ -259,8 +233,6 @@ def build_global_kwargs(
|
||||
"seed": seed,
|
||||
"rand_device": "cpu",
|
||||
"num_inference_steps": GLOBAL_STEPS,
|
||||
# The source graph applies ModelSamplingAuraFlow with shift=3. DiffSynth
|
||||
# expresses the same rational sigma shift as exp(mu), so mu=log(3).
|
||||
"exponential_shift_mu": math.log(3.0),
|
||||
"blockwise_controlnet_inputs": [controlnet_input],
|
||||
}
|
||||
@@ -312,7 +284,7 @@ def _expanded_box(
|
||||
*,
|
||||
factor: float = 2.5,
|
||||
) -> tuple[int, int, int, int]:
|
||||
"""Expand a face box around its center, matching the reference crop factor."""
|
||||
"""Expand a face box around its center to include local lighting context."""
|
||||
x1, y1, x2, y2 = box
|
||||
image_width, image_height = image_size
|
||||
center_x = (x1 + x2) / 2.0
|
||||
@@ -813,7 +785,7 @@ class QwenZImagePipeline:
|
||||
scale_for_face = 768.0 / max(1, max(face_width, face_height))
|
||||
scale_for_crop = 1024.0 / max(1, max(crop_width, crop_height))
|
||||
scale = min(scale_for_face, scale_for_crop)
|
||||
# Never shrink below the crop's current size unless the 1024 cap requires it.
|
||||
# Never shrink below the crop's current size unless the cap requires it.
|
||||
if max(crop_width, crop_height) <= 1024:
|
||||
scale = max(1.0, scale)
|
||||
width = max(16, round(crop_width * scale / 16.0) * 16)
|
||||
@@ -881,7 +853,7 @@ class QwenZImagePipeline:
|
||||
resolution_adaptive_denoise(image.width, image.height) if strength is None else float(strength)
|
||||
)
|
||||
if tile and max(image.size) > tile_size:
|
||||
from remove_ai_watermarks.noai.tiling import run_tiled
|
||||
from remove_ai_watermarks._internal.tiling import run_tiled
|
||||
|
||||
global_result = run_tiled(
|
||||
lambda tile_image: self._run_global(tile_image, global_strength, seed),
|
||||
@@ -0,0 +1,74 @@
|
||||
"""Bounded AVI metadata reader for native TC260 AIGC labels.
|
||||
|
||||
TC260-PG-20257A stores the label in an AVI ``LIST/INFO`` chunk whose child
|
||||
chunk ID is ``AIGC`` and whose value is the normative JSON object. The walker
|
||||
seeks over media chunks and reads only bounded metadata values.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import TYPE_CHECKING, BinaryIO
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from pathlib import Path
|
||||
|
||||
from remove_ai_watermarks.metadata import MAX_TC260_VALUE_BYTES, parse_tc260_aigc_json
|
||||
|
||||
|
||||
def _info_payloads(
|
||||
stream: BinaryIO,
|
||||
start: int,
|
||||
end: int,
|
||||
) -> tuple[bytes, ...]:
|
||||
found: list[bytes] = []
|
||||
position = start
|
||||
while position + 8 <= end:
|
||||
stream.seek(position)
|
||||
chunk_id = stream.read(4)
|
||||
size_raw = stream.read(4)
|
||||
if len(chunk_id) != 4 or len(size_raw) != 4:
|
||||
break
|
||||
size = int.from_bytes(size_raw, "little")
|
||||
payload_start = position + 8
|
||||
payload_end = payload_start + size
|
||||
if payload_end > end:
|
||||
break
|
||||
if chunk_id == b"AIGC" and size <= MAX_TC260_VALUE_BYTES:
|
||||
value = stream.read(size)
|
||||
if len(value) == size and parse_tc260_aigc_json(value) is not None:
|
||||
found.append(value.rstrip(b"\x00 "))
|
||||
position = payload_end + (size & 1)
|
||||
return tuple(found)
|
||||
|
||||
|
||||
def tc260_aigc_payloads(path: str | Path) -> tuple[bytes, ...]:
|
||||
"""Read validated TC260 values from an AVI ``LIST/INFO/AIGC`` chunk."""
|
||||
found: list[bytes] = []
|
||||
try:
|
||||
with open(path, "rb") as stream:
|
||||
header = stream.read(12)
|
||||
if len(header) != 12 or header[:4] != b"RIFF" or header[8:12] != b"AVI ":
|
||||
return ()
|
||||
stream.seek(0, 2)
|
||||
file_size = stream.tell()
|
||||
declared_end = min(8 + int.from_bytes(header[4:8], "little"), file_size)
|
||||
position = 12
|
||||
while position + 8 <= declared_end:
|
||||
stream.seek(position)
|
||||
chunk_id = stream.read(4)
|
||||
size_raw = stream.read(4)
|
||||
if len(chunk_id) != 4 or len(size_raw) != 4:
|
||||
break
|
||||
size = int.from_bytes(size_raw, "little")
|
||||
payload_start = position + 8
|
||||
payload_end = payload_start + size
|
||||
if payload_end > declared_end:
|
||||
break
|
||||
if chunk_id == b"LIST" and size >= 4:
|
||||
list_type = stream.read(4)
|
||||
if list_type == b"INFO":
|
||||
found.extend(_info_payloads(stream, payload_start + 4, payload_end))
|
||||
position = payload_end + (size & 1)
|
||||
except OSError:
|
||||
return ()
|
||||
return tuple(found)
|
||||
@@ -0,0 +1,30 @@
|
||||
"""Small path helpers shared by optional image pipelines."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import TYPE_CHECKING
|
||||
|
||||
from remove_ai_watermarks._internal.constants import SUPPORTED_FORMATS
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from pathlib import Path
|
||||
|
||||
_PIL_FORMAT_BY_SUFFIX = {
|
||||
".jpg": "JPEG",
|
||||
".jpeg": "JPEG",
|
||||
".png": "PNG",
|
||||
}
|
||||
|
||||
|
||||
def is_supported_format(file_path: Path) -> bool:
|
||||
"""Return whether ``file_path`` has a supported raster suffix."""
|
||||
return file_path.suffix.casefold() in SUPPORTED_FORMATS
|
||||
|
||||
|
||||
def get_image_format(file_path: Path) -> str:
|
||||
"""Return the Pillow save format used by the legacy metadata API.
|
||||
|
||||
The metadata writer only has specialized PNG and JPEG paths. Other accepted
|
||||
inputs therefore use its PNG fallback, matching the established API contract.
|
||||
"""
|
||||
return _PIL_FORMAT_BY_SUFFIX.get(file_path.suffix.casefold(), "PNG")
|
||||
@@ -0,0 +1,105 @@
|
||||
"""Project-owned configuration for invisible-watermark regeneration profiles."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import math
|
||||
from dataclasses import dataclass
|
||||
from typing import TYPE_CHECKING, Literal
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from pathlib import Path
|
||||
|
||||
DEFAULT_MODEL_ID = "stabilityai/stable-diffusion-xl-base-1.0"
|
||||
QWEN_MODEL_ID = "Qwen/Qwen-Image"
|
||||
CONTROLNET_CANNY_MODEL = "xinsir/controlnet-canny-sdxl-1.0"
|
||||
|
||||
SDXL_PROFILE = "sdxl"
|
||||
QWEN_ZIMAGE_PROFILE = "qwen-zimage"
|
||||
|
||||
OPENAI_STRENGTH = 0.10
|
||||
GEMINI_STRENGTH = 0.15
|
||||
UNKNOWN_STRENGTH = GEMINI_STRENGTH
|
||||
DEFAULT_STRENGTH = UNKNOWN_STRENGTH
|
||||
|
||||
QWEN_OPENAI_STRENGTH = 0.10
|
||||
QWEN_GEMINI_STRENGTH = 0.25
|
||||
QWEN_UNKNOWN_STRENGTH = QWEN_GEMINI_STRENGTH
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class _StrengthPolicy:
|
||||
unknown: float
|
||||
by_vendor: dict[str, float]
|
||||
|
||||
def choose(self, vendor: str | None) -> float:
|
||||
return self.by_vendor.get((vendor or "").casefold(), self.unknown)
|
||||
|
||||
|
||||
_STANDARD_POLICY = _StrengthPolicy(
|
||||
unknown=UNKNOWN_STRENGTH,
|
||||
by_vendor={"openai": OPENAI_STRENGTH, "google": GEMINI_STRENGTH},
|
||||
)
|
||||
_QWEN_POLICY = _StrengthPolicy(
|
||||
unknown=QWEN_UNKNOWN_STRENGTH,
|
||||
by_vendor={"openai": QWEN_OPENAI_STRENGTH, "google": QWEN_GEMINI_STRENGTH},
|
||||
)
|
||||
_ALIASES = {"default": SDXL_PROFILE, "qwen_zimage": QWEN_ZIMAGE_PROFILE}
|
||||
|
||||
|
||||
def normalize_profile(profile: str) -> str:
|
||||
"""Normalize spelling and resolve compatibility aliases."""
|
||||
value = profile.strip().casefold()
|
||||
return _ALIASES.get(value, value)
|
||||
|
||||
|
||||
def resolve_steps(num_inference_steps: int | None, pipeline: str) -> int:
|
||||
"""Return an explicit step count or the selected profile's default."""
|
||||
if num_inference_steps is not None:
|
||||
return num_inference_steps
|
||||
return 4 if normalize_profile(pipeline) == QWEN_ZIMAGE_PROFILE else 50
|
||||
|
||||
|
||||
def resolve_seed(seed: int | None, pipeline: str) -> int | None:
|
||||
"""Keep the fixed Qwen plus Z-Image profile reproducible by default."""
|
||||
if seed is not None:
|
||||
return seed
|
||||
return 0 if normalize_profile(pipeline) == QWEN_ZIMAGE_PROFILE else None
|
||||
|
||||
|
||||
def strength_default_help() -> str:
|
||||
"""Describe the live default policy without duplicating its values."""
|
||||
return (
|
||||
f"vendor-adaptive (OpenAI {OPENAI_STRENGTH} / Google {GEMINI_STRENGTH} / "
|
||||
f"unknown {UNKNOWN_STRENGTH}, from the C2PA issuer; qwen-zimage instead uses "
|
||||
"resolution-adaptive denoise)"
|
||||
)
|
||||
|
||||
|
||||
def resolve_strength(strength: float | None, vendor: str | None = None, pipeline: str | None = None) -> float:
|
||||
"""Resolve a user override or the calibrated policy for a profile and vendor."""
|
||||
if strength is not None:
|
||||
return strength
|
||||
policy = _QWEN_POLICY if pipeline is not None and normalize_profile(pipeline) == "qwen" else _STANDARD_POLICY
|
||||
return policy.choose(vendor)
|
||||
|
||||
|
||||
def viable_steps(num_inference_steps: int, strength: float) -> int:
|
||||
"""Ensure Diffusers receives at least one effective img2img denoising step."""
|
||||
if strength <= 0 or int(num_inference_steps * strength) >= 1:
|
||||
return num_inference_steps
|
||||
return math.ceil(1.0 / strength)
|
||||
|
||||
|
||||
def vendor_for_strength(image_path: Path) -> Literal["openai", "google"] | None:
|
||||
"""Select the strength cohort using the input's SynthID provenance proxy."""
|
||||
try:
|
||||
from remove_ai_watermarks.metadata import synthid_source
|
||||
|
||||
evidence = (synthid_source(image_path) or "").casefold()
|
||||
except Exception:
|
||||
return None
|
||||
if "google" in evidence:
|
||||
return "google"
|
||||
if "openai" in evidence:
|
||||
return "openai"
|
||||
return None
|
||||
@@ -0,0 +1,649 @@
|
||||
"""Project-native orchestration for diffusion-based pixel regeneration."""
|
||||
|
||||
# pyright: reportUnknownMemberType=false, reportUnknownArgumentType=false, reportUnknownVariableType=false, reportUnknownParameterType=false, reportMissingTypeArgument=false, reportMissingTypeStubs=false, reportMissingImports=false, reportArgumentType=false, reportAssignmentType=false, reportReturnType=false, reportCallIssue=false, reportIndexIssue=false, reportOperatorIssue=false, reportOptionalMemberAccess=false, reportOptionalCall=false, reportOptionalSubscript=false, reportOptionalOperand=false, reportAttributeAccessIssue=false, reportPrivateImportUsage=false, reportPrivateUsage=false, reportInvalidTypeForm=false, reportConstantRedefinition=false, reportUnnecessaryComparison=false
|
||||
from __future__ import annotations
|
||||
|
||||
import contextlib
|
||||
import logging
|
||||
import os
|
||||
import subprocess
|
||||
from typing import TYPE_CHECKING, Any
|
||||
|
||||
from PIL import Image
|
||||
|
||||
from remove_ai_watermarks._internal.watermark_profiles import (
|
||||
CONTROLNET_CANNY_MODEL,
|
||||
DEFAULT_MODEL_ID,
|
||||
DEFAULT_STRENGTH,
|
||||
QWEN_MODEL_ID,
|
||||
QWEN_ZIMAGE_PROFILE,
|
||||
normalize_profile,
|
||||
resolve_seed,
|
||||
resolve_steps,
|
||||
resolve_strength,
|
||||
viable_steps,
|
||||
)
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from collections.abc import Callable
|
||||
from pathlib import Path
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
try:
|
||||
import torch
|
||||
|
||||
_HAS_TORCH = True
|
||||
except ImportError:
|
||||
torch = None # type: ignore[assignment]
|
||||
_HAS_TORCH = False
|
||||
|
||||
try:
|
||||
from diffusers import AutoPipelineForImage2Image as AutoImg2ImgPipeline
|
||||
|
||||
_HAS_DIFFUSERS = True
|
||||
except ImportError:
|
||||
AutoImg2ImgPipeline = None # type: ignore[assignment,misc]
|
||||
_HAS_DIFFUSERS = False
|
||||
|
||||
_SDXL_FP16_VAE_ID = "madebyollin/sdxl-vae-fp16-fix"
|
||||
_DEGENERATE_THRESHOLD = 1.0
|
||||
_CANNY_LOW = 100
|
||||
_CANNY_HIGH = 200
|
||||
_CONTROLNET_PROMPT = "best quality, high quality, sharp, detailed, photographic"
|
||||
_CONTROLNET_NEGATIVE = "blurry, lowres, deformed, distorted text, garbled text, watermark, jpeg artifacts"
|
||||
_QWEN_PROMPT = "high quality, sharp, detailed, faithful to the original"
|
||||
_QWEN_NEGATIVE = "blurry, lowres, distorted text, garbled text, artifacts"
|
||||
|
||||
|
||||
def is_watermark_removal_available() -> bool:
|
||||
"""Return whether the standard diffusion runtime can be imported."""
|
||||
return _HAS_TORCH and _HAS_DIFFUSERS
|
||||
|
||||
|
||||
def _ensure_watermark_deps() -> None:
|
||||
if not is_watermark_removal_available():
|
||||
raise ImportError(
|
||||
"Invisible watermark regeneration requires the 'diffusion' extra. Install remove-ai-watermarks[diffusion]."
|
||||
)
|
||||
|
||||
|
||||
def _needs_fp16_vae_fix(model_id: str, default_model_id: str, is_fp16: bool) -> bool:
|
||||
"""Return whether the default SDXL pipeline needs the overflow-safe VAE."""
|
||||
return is_fp16 and model_id == default_model_id
|
||||
|
||||
|
||||
def _is_degenerate_image(image: Image.Image) -> bool:
|
||||
"""Detect the uniform near-black output produced by an fp16 decode collapse."""
|
||||
import numpy as np
|
||||
|
||||
pixels = np.asarray(image.convert("RGB"), dtype=np.float32)
|
||||
return float(pixels.mean()) < _DEGENERATE_THRESHOLD and float(pixels.std()) < _DEGENERATE_THRESHOLD
|
||||
|
||||
|
||||
def _has_nvidia_gpu() -> bool:
|
||||
try:
|
||||
subprocess.run(
|
||||
["nvidia-smi"],
|
||||
check=True,
|
||||
stdout=subprocess.DEVNULL,
|
||||
stderr=subprocess.DEVNULL,
|
||||
)
|
||||
except (FileNotFoundError, subprocess.CalledProcessError):
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
def _detect_cuda_index_url() -> str:
|
||||
"""Return a PyTorch wheel index compatible with the reported CUDA runtime."""
|
||||
try:
|
||||
report = subprocess.run(
|
||||
["nvidia-smi"],
|
||||
check=True,
|
||||
capture_output=True,
|
||||
text=True,
|
||||
).stdout
|
||||
except (FileNotFoundError, subprocess.CalledProcessError):
|
||||
return "https://download.pytorch.org/whl/cu121"
|
||||
import re
|
||||
|
||||
match = re.search(r"CUDA Version:\s*(\d+)\.(\d+)", report)
|
||||
if match is None:
|
||||
return "https://download.pytorch.org/whl/cu121"
|
||||
return f"https://download.pytorch.org/whl/cu{match.group(1)}{match.group(2)}"
|
||||
|
||||
|
||||
def _backend_works(device: str) -> bool:
|
||||
try:
|
||||
probe = torch.tensor([1.0], device=device) # type: ignore[union-attr]
|
||||
_ = probe + probe
|
||||
except (AssertionError, RuntimeError):
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
def get_device() -> str:
|
||||
"""Select CUDA, XPU, MPS, or CPU in that order when each backend is usable."""
|
||||
if not _HAS_TORCH:
|
||||
return "cpu"
|
||||
if torch.cuda.is_available() and _backend_works("cuda"): # type: ignore[union-attr]
|
||||
return "cuda"
|
||||
xpu = getattr(torch, "xpu", None)
|
||||
if xpu is not None and xpu.is_available() and _backend_works("xpu"):
|
||||
return "xpu"
|
||||
if _has_nvidia_gpu():
|
||||
logger.warning("NVIDIA GPU detected, but the installed PyTorch build has no working CUDA backend")
|
||||
mps = getattr(getattr(torch, "backends", None), "mps", None)
|
||||
if mps is not None and mps.is_available():
|
||||
return "mps"
|
||||
return "cpu"
|
||||
|
||||
|
||||
def _make_seed_generator(device: str, seed: int) -> Any:
|
||||
"""Create a deterministic generator, using CPU when device RNG is unavailable."""
|
||||
try:
|
||||
return torch.Generator(device=device).manual_seed(seed) # type: ignore[union-attr]
|
||||
except (RuntimeError, TypeError):
|
||||
return torch.Generator().manual_seed(seed) # type: ignore[union-attr]
|
||||
|
||||
|
||||
def _qwen_target_size(width: int, height: int) -> tuple[int, int]:
|
||||
"""Floor dimensions to Qwen's 16-pixel latent grid."""
|
||||
return max(16, width - width % 16), max(16, height - height % 16)
|
||||
|
||||
|
||||
def _build_qwen_kwargs(
|
||||
image: Image.Image,
|
||||
strength: float,
|
||||
num_inference_steps: int,
|
||||
true_cfg_scale: float,
|
||||
generator: Any,
|
||||
) -> dict[str, Any]:
|
||||
"""Build the Qwen img2img call without importing its optional pipeline class."""
|
||||
width, height = _qwen_target_size(image.width, image.height)
|
||||
return {
|
||||
"prompt": _QWEN_PROMPT,
|
||||
"negative_prompt": _QWEN_NEGATIVE,
|
||||
"image": image,
|
||||
"strength": strength,
|
||||
"num_inference_steps": num_inference_steps,
|
||||
"true_cfg_scale": true_cfg_scale,
|
||||
"generator": generator,
|
||||
"width": width,
|
||||
"height": height,
|
||||
}
|
||||
|
||||
|
||||
class WatermarkRemover:
|
||||
"""Load one regeneration profile and write a metadata-clean raster output."""
|
||||
|
||||
DEFAULT_MODEL_ID = DEFAULT_MODEL_ID
|
||||
DEFAULT_STRENGTH = DEFAULT_STRENGTH
|
||||
CONTROLNET_CANNY_MODEL = CONTROLNET_CANNY_MODEL
|
||||
_DEVICES = frozenset({"cpu", "mps", "cuda", "xpu"})
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
model_id: str | None = None,
|
||||
device: str | None = None,
|
||||
torch_dtype: Any = None,
|
||||
progress_callback: Callable[[str], None] | None = None,
|
||||
hf_token: str | None = None,
|
||||
pipeline: str = "controlnet",
|
||||
controlnet_conditioning_scale: float = 1.0,
|
||||
cpu_offload: bool = False,
|
||||
) -> None:
|
||||
requested_model = model_id or self.DEFAULT_MODEL_ID
|
||||
self.model_profile = normalize_profile(pipeline)
|
||||
if self.model_profile == QWEN_ZIMAGE_PROFILE and model_id not in {None, self.DEFAULT_MODEL_ID}:
|
||||
raise ValueError("The qwen-zimage profile uses a fixed Qwen-Image-2512 and Z-Image model stack.")
|
||||
self.model_id = (
|
||||
"Qwen/Qwen-Image-2512 + Tongyi-MAI/Z-Image-Turbo"
|
||||
if self.model_profile == QWEN_ZIMAGE_PROFILE
|
||||
else requested_model
|
||||
)
|
||||
_ensure_watermark_deps()
|
||||
selected_device = (device or get_device()).casefold()
|
||||
self.device = get_device() if selected_device == "auto" else selected_device
|
||||
if self.device not in self._DEVICES:
|
||||
raise ValueError(f"Unsupported device '{device}'. Use one of: auto, cpu, mps, cuda, xpu.")
|
||||
|
||||
if torch_dtype is not None:
|
||||
self.torch_dtype = torch_dtype
|
||||
elif self.device in {"cpu", "mps"}:
|
||||
self.torch_dtype = torch.float32 # type: ignore[union-attr]
|
||||
elif self.model_profile in {"qwen", QWEN_ZIMAGE_PROFILE}:
|
||||
self.torch_dtype = torch.bfloat16 # type: ignore[union-attr]
|
||||
else:
|
||||
self.torch_dtype = torch.float16 # type: ignore[union-attr]
|
||||
|
||||
self.cpu_offload = cpu_offload
|
||||
self.controlnet_conditioning_scale = controlnet_conditioning_scale
|
||||
self.hf_token = hf_token or os.environ.get("HF_TOKEN")
|
||||
self._progress_callback = progress_callback
|
||||
self._pipeline: Any = None
|
||||
self._controlnet_pipeline: Any = None
|
||||
self._qwen_pipeline: Any = None
|
||||
self._qwen_zimage_pipeline: Any = None
|
||||
|
||||
def _set_progress(self, message: str) -> None:
|
||||
if self._progress_callback is not None:
|
||||
with contextlib.suppress(Exception):
|
||||
self._progress_callback(message)
|
||||
|
||||
def preload(self, *, global_only: bool = False) -> None:
|
||||
"""Materialize the selected model stack before the first request."""
|
||||
if self.model_profile == QWEN_ZIMAGE_PROFILE:
|
||||
self._load_qwen_zimage_pipeline().preload(global_only=global_only)
|
||||
elif self.model_profile == "qwen":
|
||||
self._load_qwen_pipeline()
|
||||
elif self.model_profile == "controlnet":
|
||||
self._load_controlnet_pipeline()
|
||||
else:
|
||||
self._load_pipeline()
|
||||
|
||||
def _base_load_kwargs(self) -> dict[str, Any]:
|
||||
options: dict[str, Any] = {"torch_dtype": self.torch_dtype}
|
||||
if self.hf_token:
|
||||
options["token"] = self.hf_token
|
||||
return options
|
||||
|
||||
def _load_from_pretrained(self, cls: Any, model_id: str, **kwargs: Any) -> Any:
|
||||
if self.torch_dtype == torch.float16: # type: ignore[union-attr]
|
||||
try:
|
||||
return cls.from_pretrained(model_id, variant="fp16", **kwargs)
|
||||
except Exception as error:
|
||||
logger.info("Model %s has no usable fp16 variant (%s); using default weights", model_id, error)
|
||||
return cls.from_pretrained(model_id, **kwargs)
|
||||
|
||||
def _maybe_add_fp16_vae(self, options: dict[str, Any]) -> None:
|
||||
if not _needs_fp16_vae_fix(
|
||||
self.model_id,
|
||||
self.DEFAULT_MODEL_ID,
|
||||
self.torch_dtype == torch.float16, # type: ignore[union-attr]
|
||||
):
|
||||
return
|
||||
from diffusers import AutoencoderKL
|
||||
|
||||
options["vae"] = AutoencoderKL.from_pretrained(_SDXL_FP16_VAE_ID, torch_dtype=torch.float16)
|
||||
|
||||
@staticmethod
|
||||
def _disable_sdxl_watermarker(options: dict[str, Any]) -> None:
|
||||
options["add_watermarker"] = False
|
||||
|
||||
def _move_to_device_and_optimize(self, pipeline: Any) -> Any:
|
||||
if self.cpu_offload and self.device == "cuda":
|
||||
offload = getattr(pipeline, "enable_model_cpu_offload", None)
|
||||
if not callable(offload):
|
||||
raise RuntimeError("CPU offload was requested, but this pipeline does not support it.")
|
||||
offload(device="cuda")
|
||||
else:
|
||||
try:
|
||||
pipeline = pipeline.to(self.device)
|
||||
except (RuntimeError, AssertionError) as error:
|
||||
if self.device == "cuda":
|
||||
raise RuntimeError(
|
||||
f"Failed to move model to CUDA ({error}). Install a compatible PyTorch wheel from "
|
||||
f"{_detect_cuda_index_url()}."
|
||||
) from error
|
||||
raise
|
||||
optimize = getattr(pipeline, "enable_xformers_memory_efficient_attention", None)
|
||||
if callable(optimize):
|
||||
with contextlib.suppress(Exception):
|
||||
optimize()
|
||||
if self.device == "mps":
|
||||
slice_attention = getattr(pipeline, "enable_attention_slicing", None)
|
||||
if callable(slice_attention):
|
||||
with contextlib.suppress(Exception):
|
||||
slice_attention("max")
|
||||
return pipeline
|
||||
|
||||
def _sdxl_options(self) -> dict[str, Any]:
|
||||
options = self._base_load_kwargs()
|
||||
self._disable_sdxl_watermarker(options)
|
||||
self._maybe_add_fp16_vae(options)
|
||||
return options
|
||||
|
||||
def _load_pipeline(self) -> Any:
|
||||
if self._pipeline is None:
|
||||
options = self._sdxl_options()
|
||||
options.update(safety_checker=None, requires_safety_checker=False)
|
||||
loaded = self._load_from_pretrained(AutoImg2ImgPipeline, self.model_id, **options)
|
||||
self._pipeline = self._move_to_device_and_optimize(loaded)
|
||||
return self._pipeline
|
||||
|
||||
def _load_controlnet_pipeline(self) -> Any:
|
||||
if self._controlnet_pipeline is None:
|
||||
from diffusers import ControlNetModel, StableDiffusionXLControlNetImg2ImgPipeline
|
||||
|
||||
controlnet = self._load_from_pretrained(
|
||||
ControlNetModel,
|
||||
CONTROLNET_CANNY_MODEL,
|
||||
torch_dtype=self.torch_dtype,
|
||||
)
|
||||
options = self._sdxl_options()
|
||||
options["controlnet"] = controlnet
|
||||
loaded = self._load_from_pretrained(
|
||||
StableDiffusionXLControlNetImg2ImgPipeline,
|
||||
self.model_id,
|
||||
**options,
|
||||
)
|
||||
self._controlnet_pipeline = self._move_to_device_and_optimize(loaded)
|
||||
return self._controlnet_pipeline
|
||||
|
||||
def _load_qwen_pipeline(self) -> Any:
|
||||
if self._qwen_pipeline is None:
|
||||
try:
|
||||
from diffusers import QwenImageImg2ImgPipeline
|
||||
except ImportError as error:
|
||||
raise ImportError("The qwen profile requires Diffusers with QwenImageImg2ImgPipeline.") from error
|
||||
model_id = QWEN_MODEL_ID if self.model_id == self.DEFAULT_MODEL_ID else self.model_id
|
||||
loaded = QwenImageImg2ImgPipeline.from_pretrained(model_id, **self._base_load_kwargs())
|
||||
self._qwen_pipeline = self._move_to_device_and_optimize(loaded)
|
||||
return self._qwen_pipeline
|
||||
|
||||
def _load_qwen_zimage_pipeline(self) -> Any:
|
||||
if self._qwen_zimage_pipeline is None:
|
||||
from remove_ai_watermarks._internal.qwen_zimage_pipeline import QwenZImagePipeline
|
||||
|
||||
self._qwen_zimage_pipeline = QwenZImagePipeline(
|
||||
device=self.device,
|
||||
torch_dtype=self.torch_dtype,
|
||||
hf_token=self.hf_token,
|
||||
progress_callback=self._progress_callback,
|
||||
controlnet_conditioning_scale=self.controlnet_conditioning_scale,
|
||||
keep_face_models_on_device=False if self.cpu_offload else None,
|
||||
)
|
||||
return self._qwen_zimage_pipeline
|
||||
|
||||
def _reload_on_cpu(self, cache_name: str, loader: Callable[[], Any]) -> Any:
|
||||
self.device = "cpu"
|
||||
self.torch_dtype = torch.float32 # type: ignore[union-attr]
|
||||
setattr(self, cache_name, None)
|
||||
return loader()
|
||||
|
||||
def _run_img2img(
|
||||
self,
|
||||
init_image: Image.Image,
|
||||
strength: float,
|
||||
num_inference_steps: int,
|
||||
guidance_scale: float,
|
||||
generator: Any,
|
||||
) -> Image.Image:
|
||||
from remove_ai_watermarks._internal.img2img_runner import run_img2img_with_mps_fallback
|
||||
|
||||
output, device = run_img2img_with_mps_fallback(
|
||||
self._load_pipeline,
|
||||
init_image,
|
||||
strength,
|
||||
num_inference_steps,
|
||||
guidance_scale,
|
||||
generator,
|
||||
self.device,
|
||||
self._set_progress,
|
||||
reload_on_cpu=lambda: self._reload_on_cpu("_pipeline", self._load_pipeline),
|
||||
)
|
||||
self.device = device
|
||||
return output
|
||||
|
||||
def _build_canny_control_image(self, init_image: Image.Image) -> Image.Image:
|
||||
import cv2
|
||||
import numpy as np
|
||||
|
||||
gray = cv2.cvtColor(np.asarray(init_image.convert("RGB")), cv2.COLOR_RGB2GRAY)
|
||||
edges = cv2.Canny(gray, _CANNY_LOW, _CANNY_HIGH)
|
||||
return Image.fromarray(np.repeat(edges[:, :, None], 3, axis=2))
|
||||
|
||||
def _run_controlnet(
|
||||
self,
|
||||
init_image: Image.Image,
|
||||
strength: float,
|
||||
num_inference_steps: int,
|
||||
guidance_scale: float,
|
||||
generator: Any,
|
||||
) -> Image.Image:
|
||||
from remove_ai_watermarks._internal.img2img_runner import run_img2img_with_mps_fallback
|
||||
|
||||
extras = {
|
||||
"prompt": _CONTROLNET_PROMPT,
|
||||
"negative_prompt": _CONTROLNET_NEGATIVE,
|
||||
"control_image": self._build_canny_control_image(init_image),
|
||||
"controlnet_conditioning_scale": float(self.controlnet_conditioning_scale),
|
||||
}
|
||||
output, device = run_img2img_with_mps_fallback(
|
||||
self._load_controlnet_pipeline,
|
||||
init_image,
|
||||
strength,
|
||||
num_inference_steps,
|
||||
guidance_scale,
|
||||
generator,
|
||||
self.device,
|
||||
self._set_progress,
|
||||
reload_on_cpu=lambda: self._reload_on_cpu("_controlnet_pipeline", self._load_controlnet_pipeline),
|
||||
extra_kwargs=extras,
|
||||
)
|
||||
self.device = device
|
||||
return output
|
||||
|
||||
def _run_qwen(
|
||||
self,
|
||||
init_image: Image.Image,
|
||||
strength: float,
|
||||
num_inference_steps: int,
|
||||
guidance_scale: float,
|
||||
generator: Any,
|
||||
) -> Image.Image:
|
||||
response = self._load_qwen_pipeline()(
|
||||
**_build_qwen_kwargs(init_image, strength, num_inference_steps, guidance_scale, generator)
|
||||
)
|
||||
return response.images[0]
|
||||
|
||||
def _run_qwen_zimage(
|
||||
self,
|
||||
init_image: Image.Image,
|
||||
strength: float,
|
||||
seed: int | None,
|
||||
*,
|
||||
tile: bool = False,
|
||||
tile_size: int = 1024,
|
||||
tile_overlap: int = 128,
|
||||
) -> Image.Image:
|
||||
return self._load_qwen_zimage_pipeline().run(
|
||||
init_image,
|
||||
strength=strength,
|
||||
seed=seed,
|
||||
tile=tile,
|
||||
tile_size=tile_size,
|
||||
tile_overlap=tile_overlap,
|
||||
)
|
||||
|
||||
def _generate(
|
||||
self,
|
||||
image: Image.Image,
|
||||
strength: float,
|
||||
steps: int,
|
||||
guidance: float,
|
||||
generator: Any,
|
||||
seed: int | None,
|
||||
*,
|
||||
tile: bool,
|
||||
tile_size: int,
|
||||
tile_overlap: int,
|
||||
) -> Image.Image:
|
||||
if self.model_profile == QWEN_ZIMAGE_PROFILE:
|
||||
return self._run_qwen_zimage(
|
||||
image,
|
||||
strength,
|
||||
seed,
|
||||
tile=tile,
|
||||
tile_size=tile_size,
|
||||
tile_overlap=tile_overlap,
|
||||
)
|
||||
|
||||
runner = {
|
||||
"qwen": self._run_qwen,
|
||||
"controlnet": self._run_controlnet,
|
||||
}.get(self.model_profile, self._run_img2img)
|
||||
if tile and max(image.size) > tile_size:
|
||||
from remove_ai_watermarks._internal.tiling import run_tiled
|
||||
|
||||
return run_tiled(
|
||||
lambda crop: runner(crop, strength, steps, guidance, generator),
|
||||
image,
|
||||
tile_size,
|
||||
tile_overlap,
|
||||
self._set_progress,
|
||||
)
|
||||
return runner(image, strength, steps, guidance, generator)
|
||||
|
||||
def _write_output(self, image: Image.Image, output_path: Path) -> None:
|
||||
import numpy as np
|
||||
|
||||
from remove_ai_watermarks import image_io
|
||||
|
||||
output_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
bgr = np.ascontiguousarray(np.asarray(image.convert("RGB"))[:, :, ::-1])
|
||||
if not image_io.imwrite(str(output_path), bgr):
|
||||
image.save(output_path)
|
||||
from remove_ai_watermarks.metadata import remove_ai_metadata
|
||||
|
||||
remove_ai_metadata(output_path, output_path, keep_standard=True)
|
||||
|
||||
def remove_watermark(
|
||||
self,
|
||||
image_path: Path,
|
||||
output_path: Path | None = None,
|
||||
strength: float | None = None,
|
||||
num_inference_steps: int | None = None,
|
||||
guidance_scale: float | None = None,
|
||||
seed: int | None = None,
|
||||
vendor: str | None = None,
|
||||
tile: bool = False,
|
||||
tile_size: int = 1024,
|
||||
tile_overlap: int = 128,
|
||||
region: tuple[int, int, int, int] | None = None,
|
||||
region_feather: int = 64,
|
||||
) -> Path:
|
||||
"""Regenerate image pixels and write the result without AI metadata."""
|
||||
if not image_path.exists():
|
||||
raise FileNotFoundError(f"Image not found: {image_path}")
|
||||
destination = output_path or image_path
|
||||
with Image.open(image_path) as opened:
|
||||
source = opened.convert("RGB")
|
||||
|
||||
if self.model_profile == QWEN_ZIMAGE_PROFILE:
|
||||
from remove_ai_watermarks._internal.qwen_zimage_pipeline import resolution_adaptive_denoise
|
||||
|
||||
resolved_strength = strength if strength is not None else resolution_adaptive_denoise(*source.size)
|
||||
else:
|
||||
resolved_strength = resolve_strength(strength, vendor, self.model_profile)
|
||||
if not 0.0 <= resolved_strength <= 1.0:
|
||||
raise ValueError(f"Strength must be between 0.0 and 1.0, got {resolved_strength}")
|
||||
|
||||
resolved_seed = resolve_seed(seed, self.model_profile)
|
||||
steps = resolve_steps(num_inference_steps, self.model_profile)
|
||||
guidance = (
|
||||
1.0 if guidance_scale is None and self.model_profile == QWEN_ZIMAGE_PROFILE else guidance_scale or 7.5
|
||||
)
|
||||
if self.model_profile == QWEN_ZIMAGE_PROFILE:
|
||||
if steps != 4:
|
||||
raise ValueError("The qwen-zimage profile requires 4 steps.")
|
||||
if guidance != 1.0:
|
||||
raise ValueError("The qwen-zimage profile requires CFG 1.0.")
|
||||
else:
|
||||
steps = viable_steps(steps, resolved_strength)
|
||||
|
||||
generator = None
|
||||
if resolved_seed is not None and _HAS_TORCH:
|
||||
generator = _make_seed_generator(self.device, resolved_seed)
|
||||
result = self._generate(
|
||||
source,
|
||||
resolved_strength,
|
||||
steps,
|
||||
guidance,
|
||||
generator,
|
||||
resolved_seed,
|
||||
tile=tile,
|
||||
tile_size=tile_size,
|
||||
tile_overlap=tile_overlap,
|
||||
)
|
||||
|
||||
if self.torch_dtype == torch.float16 and _is_degenerate_image(result): # type: ignore[union-attr]
|
||||
self.torch_dtype = torch.float32 # type: ignore[union-attr]
|
||||
self._pipeline = self._controlnet_pipeline = self._qwen_pipeline = self._qwen_zimage_pipeline = None
|
||||
result = self._generate(
|
||||
source,
|
||||
resolved_strength,
|
||||
steps,
|
||||
guidance,
|
||||
generator,
|
||||
resolved_seed,
|
||||
tile=tile,
|
||||
tile_size=tile_size,
|
||||
tile_overlap=tile_overlap,
|
||||
)
|
||||
|
||||
if region is not None:
|
||||
import numpy as np
|
||||
|
||||
from remove_ai_watermarks._internal.tiling import feather_region_composite
|
||||
|
||||
if result.size != source.size:
|
||||
result = result.resize(source.size, Image.Resampling.LANCZOS)
|
||||
merged = feather_region_composite(
|
||||
np.asarray(source),
|
||||
np.asarray(result.convert("RGB")),
|
||||
region,
|
||||
feather=region_feather,
|
||||
)
|
||||
result = Image.fromarray(merged)
|
||||
|
||||
self._write_output(result, destination)
|
||||
return destination
|
||||
|
||||
def remove_watermark_batch(
|
||||
self,
|
||||
input_dir: Path,
|
||||
output_dir: Path,
|
||||
strength: float | None = None,
|
||||
num_inference_steps: int | None = None,
|
||||
extensions: tuple[str, ...] = (".png", ".jpg", ".jpeg", ".webp"),
|
||||
) -> list[Path]:
|
||||
"""Process matching files in a directory, logging and continuing on failures."""
|
||||
if not input_dir.exists():
|
||||
raise FileNotFoundError(f"Input directory not found: {input_dir}")
|
||||
output_dir.mkdir(parents=True, exist_ok=True)
|
||||
from remove_ai_watermarks._internal.img2img_runner import try_empty_device_cache
|
||||
|
||||
outputs: list[Path] = []
|
||||
candidates = sorted(path for path in input_dir.iterdir() if path.suffix.casefold() in extensions)
|
||||
for source in candidates:
|
||||
try:
|
||||
outputs.append(self.remove_watermark(source, output_dir / source.name, strength, num_inference_steps))
|
||||
except Exception as error:
|
||||
logger.error("Failed to process %s: %s", source, error)
|
||||
finally:
|
||||
try_empty_device_cache(self.device)
|
||||
return outputs
|
||||
|
||||
|
||||
def remove_watermark(
|
||||
image_path: Path,
|
||||
output_path: Path | None = None,
|
||||
strength: float | None = None,
|
||||
model_id: str | None = None,
|
||||
device: str | None = None,
|
||||
hf_token: str | None = None,
|
||||
region: tuple[int, int, int, int] | None = None,
|
||||
) -> Path:
|
||||
"""Convenience wrapper using the default ControlNet profile."""
|
||||
from remove_ai_watermarks._internal.watermark_profiles import vendor_for_strength
|
||||
|
||||
remover = WatermarkRemover(model_id=model_id, device=device, hf_token=hf_token)
|
||||
return remover.remove_watermark(
|
||||
image_path,
|
||||
output_path,
|
||||
strength,
|
||||
vendor=vendor_for_strength(image_path),
|
||||
region=region,
|
||||
)
|
||||
+429
-20
@@ -4,6 +4,8 @@ Provides commands for:
|
||||
- Visible watermark removal (Gemini sparkle) - works offline, fast
|
||||
- Invisible watermark removal (SynthID etc.) - requires GPU/diffusion models
|
||||
- AI metadata stripping - lightweight, no ML deps needed
|
||||
- Video identification, visible-wordmark removal, and metadata stripping
|
||||
- Oracle-certified video SynthID removal
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
@@ -19,14 +21,21 @@ from typing import TYPE_CHECKING, Any, Literal, NoReturn
|
||||
import click
|
||||
|
||||
from remove_ai_watermarks import __version__, image_io, watermark_registry
|
||||
from remove_ai_watermarks.noai.constants import SUPPORTED_FORMATS
|
||||
from remove_ai_watermarks.noai.watermark_profiles import (
|
||||
from remove_ai_watermarks._internal.constants import SUPPORTED_FORMATS
|
||||
from remove_ai_watermarks._internal.watermark_profiles import (
|
||||
resolve_seed,
|
||||
resolve_steps,
|
||||
resolve_strength,
|
||||
strength_default_help,
|
||||
vendor_for_strength,
|
||||
)
|
||||
from remove_ai_watermarks.video import VIDEO_VISIBLE_MARKS
|
||||
from remove_ai_watermarks.video_synthid import (
|
||||
DEFAULT_VIDEO_SYNTHID_FPS,
|
||||
DEFAULT_VIDEO_SYNTHID_LONG_SIDE,
|
||||
DEFAULT_VIDEO_SYNTHID_NOISE_STD,
|
||||
VIDEO_SYNTHID_LATENT_MULTIPLE,
|
||||
)
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from collections.abc import Generator
|
||||
@@ -149,7 +158,7 @@ def _resolved_strength_for_display(
|
||||
if pipeline == "qwen-zimage" and strength is None:
|
||||
from PIL import Image
|
||||
|
||||
from remove_ai_watermarks.noai.qwen_zimage_pipeline import resolution_adaptive_denoise
|
||||
from remove_ai_watermarks._internal.qwen_zimage_pipeline import resolution_adaptive_denoise
|
||||
|
||||
with Image.open(source) as image:
|
||||
return resolution_adaptive_denoise(image.width, image.height)
|
||||
@@ -260,7 +269,7 @@ def _normalize_pipeline(ctx: click.Context, param: click.Parameter, value: str |
|
||||
"""
|
||||
if value is None:
|
||||
return None
|
||||
from remove_ai_watermarks.noai.watermark_profiles import normalize_profile
|
||||
from remove_ai_watermarks._internal.watermark_profiles import normalize_profile
|
||||
|
||||
normalized = normalize_profile(value)
|
||||
if value.strip().lower() == "default":
|
||||
@@ -586,7 +595,7 @@ def _should_skip_invisible_scrub(force: bool, image_path: Path) -> bool:
|
||||
@click.option("-v", "--verbose", is_flag=True, help="Enable verbose logging.")
|
||||
@click.pass_context
|
||||
def main(ctx: click.Context, verbose: bool) -> None:
|
||||
"""Remove visible and invisible AI watermarks from images."""
|
||||
"""Remove visible and invisible AI watermarks from images, plus provenance metadata from video."""
|
||||
from dotenv import load_dotenv
|
||||
|
||||
load_dotenv() # Load .env (e.g. HF_TOKEN)
|
||||
@@ -1017,6 +1026,23 @@ def cmd_invisible(
|
||||
|
||||
|
||||
# ── Metadata operations ──
|
||||
def _print_metadata_report(source: Path, has_ai: bool, metadata: dict[str, str]) -> None:
|
||||
"""Render one metadata inspection result for the generic and video commands."""
|
||||
if not has_ai:
|
||||
console.print(f" No AI metadata found in {source.name}")
|
||||
return
|
||||
|
||||
console.print(f" Warning: AI metadata detected in {source.name}:")
|
||||
if synthid := metadata.get("synthid_watermark"):
|
||||
console.print(f" Warning: SynthID watermark (inferred from C2PA metadata) {synthid}")
|
||||
table = Table(show_header=True, header_style="bold")
|
||||
table.add_column("Key", style="cyan")
|
||||
table.add_column("Value")
|
||||
for key, value in metadata.items():
|
||||
table.add_row(key, str(value)[:80])
|
||||
console.print(table)
|
||||
|
||||
|
||||
@main.command("metadata")
|
||||
@click.argument("source", type=click.Path(exists=True, dir_okay=False, path_type=Path))
|
||||
@click.option("--check", is_flag=True, help="Check for AI metadata (don't modify).")
|
||||
@@ -1039,8 +1065,8 @@ def cmd_metadata(
|
||||
Strips EXIF AI tags, PNG text chunks, C2PA provenance manifests, and the
|
||||
China TC260 AIGC label. Beyond images (PNG/JPEG/WebP/AVIF/HEIF/JXL) it also
|
||||
strips provenance metadata from MP4/MOV/M4V/M4A containers and, via ffmpeg,
|
||||
from WebM/MP3/WAV/FLAC/OGG. The coded image, audio, and video data are left
|
||||
untouched.
|
||||
from WebM/MKV/AVI/FLV/MP3/WAV/FLAC/OGG. The coded image, audio, and video
|
||||
data are left untouched.
|
||||
"""
|
||||
from remove_ai_watermarks.metadata import get_ai_metadata, has_ai_metadata, strip_and_verify
|
||||
|
||||
@@ -1051,19 +1077,8 @@ def cmd_metadata(
|
||||
|
||||
if check or (not remove):
|
||||
has_ai = has_ai_metadata(source)
|
||||
if has_ai:
|
||||
console.print(f" Warning: AI metadata detected in {source.name}:")
|
||||
meta = get_ai_metadata(source)
|
||||
if synthid := meta.get("synthid_watermark"):
|
||||
console.print(f" Warning: SynthID watermark (inferred from C2PA metadata) {synthid}")
|
||||
table = Table(show_header=True, header_style="bold")
|
||||
table.add_column("Key", style="cyan")
|
||||
table.add_column("Value")
|
||||
for k, v in meta.items():
|
||||
table.add_row(k, str(v)[:80])
|
||||
console.print(table)
|
||||
else:
|
||||
console.print(f" No AI metadata found in {source.name}")
|
||||
metadata = get_ai_metadata(source) if has_ai else {}
|
||||
_print_metadata_report(source, has_ai, metadata)
|
||||
|
||||
if not remove:
|
||||
return
|
||||
@@ -1083,6 +1098,400 @@ def cmd_metadata(
|
||||
console.print(f" AI metadata stripped -> {out}")
|
||||
|
||||
|
||||
# ── Video pipeline ──
|
||||
def _video_visible_options(f: Any) -> Any:
|
||||
"""Apply the shared visible-video detector and fill options."""
|
||||
f = click.option(
|
||||
"--temporal-consistency/--no-temporal-consistency",
|
||||
default=True,
|
||||
help="Motion-align adjacent accepted fills to reduce frame-to-frame flicker.",
|
||||
)(f)
|
||||
f = click.option(
|
||||
"--backend",
|
||||
type=click.Choice(["auto", "cv2", "migan", "lama"]),
|
||||
default="cv2",
|
||||
help="Per-frame visible-fill backend.",
|
||||
)(f)
|
||||
return click.option(
|
||||
"--mark",
|
||||
type=click.Choice(["auto", *VIDEO_VISIBLE_MARKS]),
|
||||
default="auto",
|
||||
help="Visible AI mark to remove. Auto scans every supported provider in one decode pass.",
|
||||
)(f)
|
||||
|
||||
|
||||
def _video_invisible_options(f: Any) -> Any:
|
||||
"""Apply the shared invisible-video removal options."""
|
||||
f = click.option(
|
||||
"--device",
|
||||
type=click.Choice(["auto", "cuda", "mps", "cpu"]),
|
||||
default="auto",
|
||||
show_default=True,
|
||||
help="VAE inference device.",
|
||||
)(f)
|
||||
f = click.option("--seed", type=int, default=0, show_default=True)(f)
|
||||
f = click.option("--batch-size", type=click.IntRange(min=1), default=4, show_default=True)(f)
|
||||
f = click.option(
|
||||
"--fps",
|
||||
type=click.FloatRange(min=1.0),
|
||||
default=DEFAULT_VIDEO_SYNTHID_FPS,
|
||||
show_default=True,
|
||||
help="Output frame rate, capped at the source frame rate.",
|
||||
)(f)
|
||||
f = click.option(
|
||||
"--long-side",
|
||||
type=click.IntRange(min=VIDEO_SYNTHID_LATENT_MULTIPLE),
|
||||
default=DEFAULT_VIDEO_SYNTHID_LONG_SIDE,
|
||||
show_default=True,
|
||||
help="Regenerated video long side in pixels.",
|
||||
)(f)
|
||||
return click.option(
|
||||
"--noise-std",
|
||||
type=click.FloatRange(min=0.0, max=1.0),
|
||||
default=DEFAULT_VIDEO_SYNTHID_NOISE_STD,
|
||||
show_default=True,
|
||||
help="Shared latent-noise strength. Higher values change more detail.",
|
||||
)(f)
|
||||
|
||||
|
||||
@main.group("video")
|
||||
def cmd_video() -> None:
|
||||
"""Process AI watermarks in video files."""
|
||||
|
||||
|
||||
@cmd_video.command("identify")
|
||||
@click.argument("source", type=click.Path(exists=True, dir_okay=False, path_type=Path))
|
||||
@click.option("--no-visible", is_flag=True, help="Skip visible-mark detection; inspect metadata only.")
|
||||
@click.option("--json", "as_json", is_flag=True, help="Emit the report as JSON.")
|
||||
def cmd_video_identify(source: Path, no_visible: bool, as_json: bool) -> None:
|
||||
"""Identify supported provenance and visible AI marks in video."""
|
||||
from dataclasses import asdict
|
||||
|
||||
from remove_ai_watermarks.video import identify_video
|
||||
|
||||
try:
|
||||
report = identify_video(source, check_visible=not no_visible)
|
||||
except (OSError, RuntimeError, ValueError) as e:
|
||||
raise click.ClickException(str(e)) from e
|
||||
|
||||
if as_json:
|
||||
click.echo(json.dumps(asdict(report), default=str, indent=2))
|
||||
return
|
||||
|
||||
_banner()
|
||||
verdict = "AI-generated" if report.is_ai_generated else "unknown"
|
||||
console.print(f" Verdict: {verdict} (confidence: {report.confidence})")
|
||||
console.print(f" Platform: {report.platform or 'undetermined'}")
|
||||
if report.visible_mark is not None:
|
||||
console.print(
|
||||
f" Visible mark: {report.visible_mark} "
|
||||
f"({report.visible_detected_frames}/{report.total_frames} stable frames)"
|
||||
)
|
||||
else:
|
||||
console.print(" Visible mark: none found" if not no_visible else " Visible mark: not checked")
|
||||
if report.metadata_markers:
|
||||
console.print(f" AI metadata markers: {', '.join(sorted(report.metadata_markers))}")
|
||||
else:
|
||||
console.print(" AI metadata markers: none found")
|
||||
if report.caveats:
|
||||
console.print(" Caveats:")
|
||||
for caveat in report.caveats:
|
||||
console.print(f" - {caveat}")
|
||||
|
||||
|
||||
@cmd_video.command("metadata")
|
||||
@click.argument("source", type=click.Path(exists=True, dir_okay=False, path_type=Path))
|
||||
@click.option("--check", is_flag=True, help="Check for AI metadata (don't modify).")
|
||||
@click.option("--remove", is_flag=True, help="Remove AI metadata.")
|
||||
@click.option(
|
||||
"-o",
|
||||
"--output",
|
||||
type=click.Path(path_type=Path),
|
||||
default=None,
|
||||
help="Output path (default: <source>_clean with the same container).",
|
||||
)
|
||||
@click.option("--keep-standard/--remove-all", default=True, help="Keep standard metadata.")
|
||||
def cmd_video_metadata(
|
||||
source: Path,
|
||||
check: bool,
|
||||
remove: bool,
|
||||
output: Path | None,
|
||||
keep_standard: bool,
|
||||
) -> None:
|
||||
"""Check or remove AI metadata without transcoding video streams."""
|
||||
from remove_ai_watermarks.video import inspect_video_metadata, remove_video_metadata
|
||||
|
||||
_banner()
|
||||
try:
|
||||
report = inspect_video_metadata(source)
|
||||
except (OSError, ValueError) as e:
|
||||
raise click.ClickException(str(e)) from e
|
||||
|
||||
if check or not remove:
|
||||
_print_metadata_report(source, report.has_ai_metadata, report.markers)
|
||||
|
||||
if not remove:
|
||||
return
|
||||
|
||||
try:
|
||||
result = remove_video_metadata(source, output, keep_standard=keep_standard)
|
||||
except (OSError, RuntimeError, ValueError) as e:
|
||||
raise click.ClickException(str(e)) from e
|
||||
|
||||
if result.remaining:
|
||||
console.print(f" FAILED: {len(result.remaining)} AI metadata marker(s) survived in {result.output}")
|
||||
console.print(f" still present: {', '.join(sorted(result.remaining))}")
|
||||
raise SystemExit(1)
|
||||
console.print(f" AI metadata stripped -> {result.output}")
|
||||
|
||||
|
||||
@cmd_video.command("invisible")
|
||||
@click.argument("source", type=click.Path(exists=True, dir_okay=False, path_type=Path))
|
||||
@click.option(
|
||||
"-o",
|
||||
"--output",
|
||||
type=click.Path(path_type=Path),
|
||||
default=None,
|
||||
help="Output path (default: <source>_clean with the same container).",
|
||||
)
|
||||
@_video_invisible_options
|
||||
def cmd_video_invisible(
|
||||
source: Path,
|
||||
output: Path | None,
|
||||
noise_std: float,
|
||||
long_side: int,
|
||||
fps: float,
|
||||
batch_size: int,
|
||||
seed: int,
|
||||
device: str,
|
||||
) -> None:
|
||||
"""Remove video SynthID with the oracle-certified VAE profile."""
|
||||
from remove_ai_watermarks.video import remove_video_invisible
|
||||
|
||||
_banner()
|
||||
console.print(f" Regenerating {source.name} with temporally shared VAE noise...")
|
||||
try:
|
||||
result = remove_video_invisible(
|
||||
source,
|
||||
output,
|
||||
noise_std=noise_std,
|
||||
long_side=long_side,
|
||||
fps=fps,
|
||||
batch_size=batch_size,
|
||||
seed=seed,
|
||||
device=device,
|
||||
)
|
||||
except (OSError, RuntimeError, ValueError) as e:
|
||||
raise click.ClickException(str(e)) from e
|
||||
|
||||
if result.remaining_metadata:
|
||||
console.print(f" FAILED: {len(result.remaining_metadata)} AI metadata marker(s) survived in {result.output}")
|
||||
raise SystemExit(1)
|
||||
console.print(
|
||||
f" SynthID removal complete: {result.width}x{result.height}, "
|
||||
f"{result.total_frames} frames at {result.fps:.4g} fps -> {result.output}"
|
||||
)
|
||||
|
||||
|
||||
@cmd_video.command("visible")
|
||||
@click.argument("source", type=click.Path(exists=True, dir_okay=False, path_type=Path))
|
||||
@click.option(
|
||||
"-o",
|
||||
"--output",
|
||||
type=click.Path(path_type=Path),
|
||||
default=None,
|
||||
help="Output path (default: <source>_clean with the same container).",
|
||||
)
|
||||
@_video_visible_options
|
||||
@click.option("--strip-metadata/--keep-metadata", default=True, help="Strip AI metadata from the transcoded output.")
|
||||
def cmd_video_visible(
|
||||
source: Path,
|
||||
output: Path | None,
|
||||
mark: str,
|
||||
backend: str,
|
||||
temporal_consistency: bool,
|
||||
strip_metadata: bool,
|
||||
) -> None:
|
||||
"""Remove a temporally stable visible AI wordmark from video."""
|
||||
from remove_ai_watermarks.video import remove_video_visible
|
||||
|
||||
_banner()
|
||||
console.print(f" Scanning {source.name} for a temporally stable {mark} mark...")
|
||||
try:
|
||||
result = remove_video_visible(
|
||||
source,
|
||||
output,
|
||||
mark=mark,
|
||||
backend=backend,
|
||||
strip_metadata=strip_metadata,
|
||||
temporal_consistency=temporal_consistency,
|
||||
)
|
||||
except (OSError, RuntimeError, ValueError) as e:
|
||||
raise click.ClickException(str(e)) from e
|
||||
|
||||
if result.output is None:
|
||||
console.print(f" No stable {mark} watermark detected; no output written")
|
||||
raise SystemExit(EXIT_NO_VISIBLE_MARK)
|
||||
if result.remaining_metadata:
|
||||
console.print(f" FAILED: {len(result.remaining_metadata)} AI metadata marker(s) survived in {result.output}")
|
||||
raise SystemExit(1)
|
||||
console.print(
|
||||
f" Removed {result.mark} watermark from "
|
||||
f"{result.removed_frames}/{result.total_frames} frames -> {result.output}"
|
||||
)
|
||||
|
||||
|
||||
@cmd_video.command("all")
|
||||
@click.argument("source", type=click.Path(exists=True, dir_okay=False, path_type=Path))
|
||||
@click.option(
|
||||
"-o",
|
||||
"--output",
|
||||
type=click.Path(path_type=Path),
|
||||
default=None,
|
||||
help="Output path (default: <source>_clean with the same container).",
|
||||
)
|
||||
@_video_visible_options
|
||||
@click.option(
|
||||
"--invisible/--no-invisible",
|
||||
default=False,
|
||||
help="Opt into oracle-certified lossy video SynthID removal.",
|
||||
)
|
||||
@_video_invisible_options
|
||||
def cmd_video_all(
|
||||
source: Path,
|
||||
output: Path | None,
|
||||
mark: str,
|
||||
backend: str,
|
||||
temporal_consistency: bool,
|
||||
invisible: bool,
|
||||
noise_std: float,
|
||||
long_side: int,
|
||||
fps: float,
|
||||
batch_size: int,
|
||||
seed: int,
|
||||
device: str,
|
||||
) -> None:
|
||||
"""Remove stable visible marks and AI metadata from video."""
|
||||
from remove_ai_watermarks.video import remove_video_all
|
||||
|
||||
_banner()
|
||||
stages = "visible marks + SynthID + verified AI metadata" if invisible else "visible marks + verified AI metadata"
|
||||
console.print(f" Cleaning {source.name}: {stages}...")
|
||||
try:
|
||||
result = remove_video_all(
|
||||
source,
|
||||
output,
|
||||
mark=mark,
|
||||
backend=backend,
|
||||
temporal_consistency=temporal_consistency,
|
||||
include_invisible=invisible,
|
||||
noise_std=noise_std,
|
||||
long_side=long_side,
|
||||
fps=fps,
|
||||
batch_size=batch_size,
|
||||
seed=seed,
|
||||
device=device,
|
||||
)
|
||||
except (OSError, RuntimeError, ValueError) as e:
|
||||
raise click.ClickException(str(e)) from e
|
||||
|
||||
if result.remaining_metadata:
|
||||
console.print(f" FAILED: {len(result.remaining_metadata)} AI metadata marker(s) survived in {result.output}")
|
||||
raise SystemExit(1)
|
||||
if result.visible_mark is None:
|
||||
detail = "" if result.invisible_removed else "; pixels preserved"
|
||||
console.print(f" Visible mark: none found{detail}")
|
||||
else:
|
||||
console.print(
|
||||
f" Visible mark: removed {result.visible_mark} from "
|
||||
f"{result.visible_removed_frames}/{result.total_frames} frames"
|
||||
)
|
||||
console.print(f" AI metadata: stripped -> {result.output}")
|
||||
if result.invisible_removed:
|
||||
console.print(" SynthID: removed with the oracle-certified VAE profile")
|
||||
|
||||
|
||||
@cmd_video.command("batch")
|
||||
@click.argument("directory", type=click.Path(exists=True, file_okay=False, path_type=Path))
|
||||
@click.option(
|
||||
"-o",
|
||||
"--output-dir",
|
||||
type=click.Path(path_type=Path),
|
||||
default=None,
|
||||
help="Output directory (default: <directory>_clean).",
|
||||
)
|
||||
@click.option(
|
||||
"--mode",
|
||||
type=click.Choice(["all", "visible", "metadata"]),
|
||||
default="all",
|
||||
show_default=True,
|
||||
help="Video processing mode.",
|
||||
)
|
||||
@_video_visible_options
|
||||
@click.option(
|
||||
"--invisible/--no-invisible",
|
||||
default=False,
|
||||
help="Opt into oracle-certified lossy SynthID removal in all mode.",
|
||||
)
|
||||
@_video_invisible_options
|
||||
def cmd_video_batch(
|
||||
directory: Path,
|
||||
output_dir: Path | None,
|
||||
mode: str,
|
||||
mark: str,
|
||||
backend: str,
|
||||
temporal_consistency: bool,
|
||||
invisible: bool,
|
||||
noise_std: float,
|
||||
long_side: int,
|
||||
fps: float,
|
||||
batch_size: int,
|
||||
seed: int,
|
||||
device: str,
|
||||
) -> None:
|
||||
"""Process every supported video in a directory."""
|
||||
from remove_ai_watermarks.video import remove_video_batch
|
||||
|
||||
_banner()
|
||||
console.print(f" Processing video directory {directory} in {mode} mode...")
|
||||
try:
|
||||
result = remove_video_batch(
|
||||
directory,
|
||||
output_dir,
|
||||
mode=mode, # type: ignore[arg-type]
|
||||
mark=mark,
|
||||
backend=backend,
|
||||
temporal_consistency=temporal_consistency,
|
||||
include_invisible=invisible,
|
||||
noise_std=noise_std,
|
||||
long_side=long_side,
|
||||
fps=fps,
|
||||
batch_size=batch_size,
|
||||
seed=seed,
|
||||
device=device,
|
||||
)
|
||||
except (OSError, RuntimeError, ValueError) as e:
|
||||
raise click.ClickException(str(e)) from e
|
||||
|
||||
for item in result.items:
|
||||
if item.error is not None:
|
||||
console.print(f" FAILED {item.source.name}: {item.error}")
|
||||
elif item.changed:
|
||||
detail = f" ({item.visible_mark})" if item.visible_mark is not None else ""
|
||||
console.print(f" Processed {item.source.name}{detail} -> {item.output}")
|
||||
elif item.mode == "visible":
|
||||
console.print(f" Copied {item.source.name} byte-for-byte -> {item.output}")
|
||||
else:
|
||||
console.print(f" Completed {item.source.name}; no supported signal found -> {item.output}")
|
||||
console.print(
|
||||
f" Batch complete: {result.processed} processed, {result.failed} failed -> {result.output_directory}"
|
||||
)
|
||||
if result.invisible_removed:
|
||||
console.print(f" SynthID: removed from {result.invisible_removed} file(s)")
|
||||
if result.failed:
|
||||
raise SystemExit(1)
|
||||
|
||||
|
||||
# ── Provenance identification ──
|
||||
@main.command("identify")
|
||||
@click.argument("source", type=click.Path(exists=True, dir_okay=False, path_type=Path))
|
||||
|
||||
@@ -43,7 +43,7 @@ class _DecodeMaxDct:
|
||||
row, col, _channels = bgr.shape
|
||||
yuv = cv2.cvtColor(bgr, cv2.COLOR_BGR2YUV)
|
||||
|
||||
scores_by_length = {wm_len: [[] for _ in range(wm_len)] for wm_len in self._wm_lengths}
|
||||
scores_by_length = {wm_len: ([0] * wm_len, [0] * wm_len) for wm_len in self._wm_lengths}
|
||||
for channel in range(2):
|
||||
if self._scales[channel] <= 0:
|
||||
continue
|
||||
@@ -51,15 +51,15 @@ class _DecodeMaxDct:
|
||||
self._decode_frame(ca1, self._scales[channel], scores_by_length)
|
||||
|
||||
return {
|
||||
wm_len: np.asarray([float(np.asarray(score).mean()) if score else 0.0 for score in scores]) * 255 > 127
|
||||
for wm_len, scores in scores_by_length.items()
|
||||
wm_len: np.asarray(sums) * 255 > np.asarray(counts) * 127
|
||||
for wm_len, (sums, counts) in scores_by_length.items()
|
||||
}
|
||||
|
||||
def _decode_frame(
|
||||
self,
|
||||
frame: NDArray[Any],
|
||||
scale: int,
|
||||
scores_by_length: dict[int, list[list[int]]],
|
||||
scores_by_length: dict[int, tuple[list[int], list[int]]],
|
||||
) -> None:
|
||||
row, col = frame.shape
|
||||
bit_index = 0
|
||||
@@ -70,8 +70,10 @@ class _DecodeMaxDct:
|
||||
j * self._block : j * self._block + self._block,
|
||||
]
|
||||
inferred = self._infer_bit(block, scale)
|
||||
for wm_len, scores in scores_by_length.items():
|
||||
scores[bit_index % wm_len].append(inferred)
|
||||
for wm_len, (sums, counts) in scores_by_length.items():
|
||||
bucket = bit_index % wm_len
|
||||
sums[bucket] += inferred
|
||||
counts[bucket] += 1
|
||||
bit_index += 1
|
||||
|
||||
def _infer_bit(self, block: NDArray[Any], scale: int) -> int:
|
||||
|
||||
@@ -1,22 +1,6 @@
|
||||
"""Gemini visible-sparkle detector and localizer (cv2/numpy, no GPU).
|
||||
"""Locate the visible Gemini sparkle and build a mask for shared inpainting."""
|
||||
|
||||
Locates the Google Gemini / Nano Banana sparkle so the shared fill (region_eraser)
|
||||
can inpaint it. Detection is a multi-scale NCC search against the captured sparkle
|
||||
alpha template (ported from GeminiWatermarkTool's Snap Engine; original author
|
||||
Allen Kuo (allenk), https://github.com/allenk/GeminiWatermarkTool), scored by a
|
||||
spatial + gradient + variance fusion with a false-positive gate. ``footprint_mask``
|
||||
returns the sparkle footprint (captured alpha thresholded low to include the halo,
|
||||
then dilated) as a full-frame mask for the fill.
|
||||
|
||||
The captured alpha maps are background captures of the sparkle on pure-black
|
||||
backgrounds (48x48 for small images, 96x96 for large). NB: they are used here only
|
||||
to DETECT and to shape the removal mask -- the old reverse-alpha pixel recovery
|
||||
(``original = (watermarked - a*logo)/(1-a)``) is gone; removal is localize -> fill.
|
||||
"""
|
||||
|
||||
# cv2/numpy boundary: cv2 and numpy ship no usable type info for the array ops
|
||||
# below, so strict pyright cannot know their element types. Relax the unknown-type
|
||||
# rules for this file only; the public signatures are still annotated with NDArray[Any].
|
||||
# OpenCV and NumPy expose incomplete types at this array-processing boundary.
|
||||
# pyright: reportUnknownMemberType=false, reportUnknownArgumentType=false, reportUnknownVariableType=false, reportUnknownParameterType=false, reportMissingTypeArgument=false, reportMissingTypeStubs=false, reportMissingImports=false, reportArgumentType=false, reportAssignmentType=false, reportReturnType=false, reportCallIssue=false, reportIndexIssue=false, reportOperatorIssue=false, reportOptionalMemberAccess=false, reportOptionalCall=false, reportOptionalSubscript=false, reportOptionalOperand=false, reportAttributeAccessIssue=false, reportPrivateImportUsage=false, reportPrivateUsage=false, reportInvalidTypeForm=false, reportConstantRedefinition=false, reportUnnecessaryComparison=false
|
||||
from __future__ import annotations
|
||||
|
||||
@@ -41,263 +25,172 @@ logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class WatermarkSize(Enum):
|
||||
"""Watermark size mode based on image dimensions."""
|
||||
"""Provider size tier selected from the source dimensions."""
|
||||
|
||||
SMALL = "small" # 48x48, for images <= 1024x1024
|
||||
LARGE = "large" # 96x96, for images > 1024x1024
|
||||
SMALL = "small"
|
||||
LARGE = "large"
|
||||
|
||||
|
||||
@dataclass
|
||||
class DetectionResult:
|
||||
"""Result of watermark detection."""
|
||||
"""Detection decision and its component scores."""
|
||||
|
||||
detected: bool = False
|
||||
confidence: float = 0.0
|
||||
region: tuple[int, int, int, int] = (0, 0, 0, 0) # x, y, w, h
|
||||
region: tuple[int, int, int, int] = (0, 0, 0, 0)
|
||||
size: WatermarkSize = WatermarkSize.SMALL
|
||||
|
||||
# stage scores
|
||||
spatial_score: float = 0.0
|
||||
gradient_score: float = 0.0
|
||||
variance_score: float = 0.0
|
||||
|
||||
|
||||
@dataclass
|
||||
@dataclass(frozen=True, slots=True)
|
||||
class WatermarkPosition:
|
||||
"""Watermark position configuration."""
|
||||
"""Expected provider margins and logo size."""
|
||||
|
||||
margin_right: int
|
||||
margin_bottom: int
|
||||
logo_size: int
|
||||
|
||||
def get_position(self, image_width: int, image_height: int) -> tuple[int, int]:
|
||||
"""Get top-left position for a given image size."""
|
||||
x = image_width - self.margin_right - self.logo_size
|
||||
y = image_height - self.margin_bottom - self.logo_size
|
||||
return (x, y)
|
||||
return image_width - self.margin_right - self.logo_size, image_height - self.margin_bottom - self.logo_size
|
||||
|
||||
|
||||
def get_watermark_config(width: int, height: int) -> WatermarkPosition:
|
||||
"""Get the appropriate watermark configuration based on image size.
|
||||
@dataclass(frozen=True, slots=True)
|
||||
class _Candidate:
|
||||
scale: int
|
||||
x: int
|
||||
y: int
|
||||
spatial: float
|
||||
gradient: float = 0.0
|
||||
variance: float = 0.0
|
||||
|
||||
Rules discovered from Gemini:
|
||||
- W > 1024 AND H > 1024: 96x96 logo at (W-64-96, H-64-96)
|
||||
- Otherwise: 48x48 logo at (W-32-48, H-32-48)
|
||||
"""
|
||||
if width > 1024 and height > 1024:
|
||||
return WatermarkPosition(margin_right=64, margin_bottom=64, logo_size=96)
|
||||
return WatermarkPosition(margin_right=32, margin_bottom=32, logo_size=48)
|
||||
@property
|
||||
def fused(self) -> float:
|
||||
if self.spatial < 0.25:
|
||||
return max(0.0, self.spatial * 0.5)
|
||||
return self.spatial * 0.50 + self.gradient * 0.30 + self.variance * 0.20
|
||||
|
||||
|
||||
def get_watermark_size(width: int, height: int) -> WatermarkSize:
|
||||
"""Determine watermark size mode from image dimensions."""
|
||||
if width > 1024 and height > 1024:
|
||||
return WatermarkSize.LARGE
|
||||
return WatermarkSize.SMALL
|
||||
"""Return the provider's large tier only when both axes exceed 1024."""
|
||||
return WatermarkSize.LARGE if width > 1024 and height > 1024 else WatermarkSize.SMALL
|
||||
|
||||
|
||||
def _calculate_alpha_map(bg_capture: NDArray[Any]) -> NDArray[Any]:
|
||||
"""Calculate alpha map from a background capture.
|
||||
def get_watermark_config(width: int, height: int) -> WatermarkPosition:
|
||||
"""Return the observed standard placement for the selected size tier."""
|
||||
if get_watermark_size(width, height) is WatermarkSize.LARGE:
|
||||
return WatermarkPosition(64, 64, 96)
|
||||
return WatermarkPosition(32, 32, 48)
|
||||
|
||||
The alpha map represents how much the watermark affects each pixel.
|
||||
alpha = max(R, G, B) / 255.0
|
||||
"""
|
||||
if len(bg_capture.shape) == 2:
|
||||
gray = bg_capture.astype(np.float32)
|
||||
elif bg_capture.shape[2] >= 3:
|
||||
# Use max of channels for brightness
|
||||
gray = np.max(bg_capture[:, :, :3], axis=2).astype(np.float32)
|
||||
|
||||
def _calculate_alpha_map(background_capture: NDArray[Any]) -> NDArray[Any]:
|
||||
"""Convert a black-background sparkle capture to a normalized opacity map."""
|
||||
if background_capture.ndim == 2:
|
||||
intensity = background_capture
|
||||
elif background_capture.shape[2] >= 3:
|
||||
intensity = background_capture[:, :, :3].max(axis=2)
|
||||
else:
|
||||
gray = bg_capture[:, :, 0].astype(np.float32)
|
||||
|
||||
return gray / 255.0
|
||||
intensity = background_capture[:, :, 0]
|
||||
return intensity.astype(np.float32) / 255.0
|
||||
|
||||
|
||||
def _load_embedded_asset(name: str) -> NDArray[Any]:
|
||||
"""Load an embedded PNG asset and decode it with OpenCV."""
|
||||
asset_path = Path(__file__).parent / "assets" / name
|
||||
if not asset_path.exists():
|
||||
raise FileNotFoundError(f"Embedded asset not found: {asset_path}")
|
||||
|
||||
data = asset_path.read_bytes()
|
||||
buf = np.frombuffer(data, dtype=np.uint8)
|
||||
img = cv2.imdecode(buf, cv2.IMREAD_COLOR)
|
||||
if img is None:
|
||||
raise RuntimeError(f"Failed to decode embedded asset: {name}")
|
||||
return img
|
||||
def _load_capture(filename: str, expected_side: int) -> NDArray[Any]:
|
||||
capture = image_io.imread(Path(__file__).parent / "assets" / filename, cv2.IMREAD_COLOR)
|
||||
if capture is None:
|
||||
raise RuntimeError(f"Failed to decode embedded asset: {filename}")
|
||||
if capture.shape[:2] != (expected_side, expected_side):
|
||||
capture = cv2.resize(capture, (expected_side, expected_side), interpolation=cv2.INTER_AREA)
|
||||
return capture
|
||||
|
||||
|
||||
# Single source of truth for the multi-scale template ladder (aggressively downscaled to
|
||||
# slightly upscaled): the precomputed `_tmpl_cache` and the `_scan_scales` loop must use
|
||||
# the SAME scales or a scan scale would miss the cache and KeyError.
|
||||
_TEMPLATE_SCALES: tuple[int, ...] = tuple(range(16, 120, 2))
|
||||
def _gray_float(image: NDArray[Any]) -> NDArray[Any]:
|
||||
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) if image.ndim == 3 and image.shape[2] >= 3 else image
|
||||
return gray.astype(np.float32) / 255.0
|
||||
|
||||
|
||||
def _overlaps(candidate: _Candidate, selected: _Candidate) -> bool:
|
||||
radius = 0.5 * max(candidate.scale, selected.scale)
|
||||
return abs(candidate.x - selected.x) < radius and abs(candidate.y - selected.y) < radius
|
||||
|
||||
|
||||
_TEMPLATE_SCALES = tuple(range(16, 120, 2))
|
||||
|
||||
|
||||
class GeminiEngine:
|
||||
"""Detects and localizes the visible Gemini sparkle for the shared fill removal.
|
||||
"""Project-native detector and mask builder for the white Gemini sparkle."""
|
||||
|
||||
The multi-scale NCC detection is a Python port of the GeminiWatermarkTool C++
|
||||
Snap Engine; ``footprint_mask`` turns a detection into a removal mask.
|
||||
"""
|
||||
|
||||
# Body pixels at >= this fraction of the peak captured alpha define the sparkle
|
||||
# "core", sampled by the detection FP-gate's core-vs-ring brightness margin
|
||||
# (:meth:`_core_and_bg`).
|
||||
_CORE_ALPHA_FRAC = 0.8
|
||||
|
||||
# Sparkle false-positive gate. A real Gemini sparkle is a bright WHITE overlay,
|
||||
# so its core sits above the local background; a shape-only NCC match on ornate
|
||||
# or flat content (text, banners, hatching) can score >0.5 without that lift.
|
||||
# Demote a detection that is BOTH low-confidence AND low core-ring brightness
|
||||
# margin -- the joint signature of a content false positive (verified on the
|
||||
# detector calibration: demoted examples were visual false positives or a
|
||||
# near-invisible white-on-white sparkle whose AI verdict is held by metadata
|
||||
# anyway). Real sparkles escape via EITHER high confidence
|
||||
# (white-bg sparkles score >=0.79 despite a low margin) OR high margin (dark/mid
|
||||
# backgrounds, incl. the #36 faint-corner case, lift well clear), so both must
|
||||
# fail to demote.
|
||||
_SPARKLE_FP_CONF = 0.65
|
||||
_SPARKLE_FP_MARGIN = 5.0
|
||||
# Bright-background content false positives (2026-06-26 landing-page FPs: a snow+sky
|
||||
# photo and a white-background product render both scored ~0.51). The margin gate
|
||||
# above cannot catch them -- a bright background gives the "core" a HIGH core-ring
|
||||
# margin (it is genuinely brighter than its surroundings), so the brightness check
|
||||
# reads it as a real overlay. The discriminating signature is the GRADIENT NCC: a
|
||||
# real white sparkle is a crisp star silhouette (grad ~0.97-1.0 on the synthetic
|
||||
# composites, ~0.96 on the real #36 corner sparkle), while a smooth luminance blob
|
||||
# that shape-NCC-matches the rough outline has low gradient fidelity (the two FPs
|
||||
# measured 0.105 and 0.463). So ALSO demote a low-confidence match whose gradient
|
||||
# NCC is below this floor, regardless of margin -- 0.55 sits well above the worst FP
|
||||
# (0.463) and far below every real sparkle (>=0.8). This only ADDS demotions on
|
||||
# bright backgrounds (a real bright-bg sparkle keeps grad ~0.97), so it cannot
|
||||
# regress a dark/mid sparkle (already kept by margin) or a white-bg one (kept by
|
||||
# confidence >= 0.65, above the gate).
|
||||
_SPARKLE_FP_GRAD = 0.55
|
||||
|
||||
# White-core rescue for the gate above. A real but FAINT sparkle -- a soft white
|
||||
# star on a bright/textured background -- has a high core-ring margin but low
|
||||
# gradient fidelity, the SAME signature the grad gate uses to demote the smooth
|
||||
# colored-corner FP, so faint real sparkles get demoted with it. The separator the
|
||||
# grad gate discards is the CORE COLOR: a real Gemini sparkle core is near-WHITE
|
||||
# (low saturation), while a clean bright corner that shape-matches (sky, sun, a warm
|
||||
# light) is COLORED. So do NOT demote a low-grad match that already clears the trust
|
||||
# confidence (_SPARKLE_KEEP_CONF -- the registry's 0.5 sparkle gate plus a small
|
||||
# margin so the ~0.51 bright-background FPs the grad gate was added for stay demoted)
|
||||
# AND has a bright (margin) near-neutral core (_core_saturation <= _SPARKLE_WHITE_SAT).
|
||||
# Calibrated on metadata-stripped faint sparkles to recover low-gradient marks
|
||||
# without materially increasing clean false fires.
|
||||
_SPARKLE_KEEP_CONF = 0.52
|
||||
_SPARKLE_WHITE_SAT = 0.20
|
||||
|
||||
# Corner promotion (issue #36): the size weight that suppresses tiny-patch
|
||||
# false positives also buries a small, near-perfect sparkle when a larger,
|
||||
# mediocre match sits elsewhere (e.g. a bright collar in a portrait). A small
|
||||
# faint sparkle on a busy background therefore loses the global argmax and the
|
||||
# image reads as clean -- the regression osachub reported when the search
|
||||
# window widened 256px -> 512px (v0.7.2's tighter window still found it).
|
||||
# Remedy: if the bottom-right corner holds a very-high-fidelity raw-NCC match,
|
||||
# trust it regardless of size, without reverting the wider window (which is
|
||||
# needed for variant margins). The threshold sits midway between the worst
|
||||
# real-photo corner match (~0.78 across native + downscaled real photos) and a
|
||||
# genuine faint sparkle (~0.93), so it adds true detections without adding
|
||||
# false ones; it only ever overrides a lower-fidelity global pick, so it cannot
|
||||
# weaken an existing detection.
|
||||
_CORNER_PROMOTE_NCC = 0.85
|
||||
# Bottom-right corner side for the promotion search, as a fraction of the
|
||||
# image's short side, clamped to an absolute pixel band. Relative so the corner
|
||||
# stays a true corner at every scale: a fixed 256 px is a genuine corner on a
|
||||
# large image but covers ~70% of a small portrait, where a busy real photo can
|
||||
# then raw-match the star template at ~0.81 (only 0.04 below the promote gate).
|
||||
# Scaling the side down on small images drops that worst case to ~0.69, while
|
||||
# the upper clamp stops it ballooning on huge images (more corner area = more
|
||||
# random texture to false-match -- a real photo reached ~0.83 at 512 px). The
|
||||
# Gemini sparkle sits ~60-160 px from the corner (fixed margins, not
|
||||
# proportional), and the [96, 384] band covers that at every measured size.
|
||||
_CORNER_PROMOTE_FRAC = 0.20
|
||||
_CORNER_PROMOTE_MIN = 96
|
||||
_CORNER_PROMOTE_MAX = 384
|
||||
|
||||
# Number of top size-weighted spatial candidates scored by full fusion before one
|
||||
# is selected. The single size-weighted argmax can bury a genuine mid-size sparkle
|
||||
# under a LARGER, lower-fidelity shape match (the 256->512 search-widening
|
||||
# regression: a real corner sparkle at raw ~0.77 lost to a decoy at raw ~0.63).
|
||||
# Scoring the top-K by gradient-bearing fusion rescues it. Top-K (NOT the raw-NCC
|
||||
# argmax) keeps the tiny-patch suppression intact: a coincidental 16 px match never
|
||||
# ranks in the size-weighted top-K, so widening selection cannot add a false
|
||||
# positive on non-Gemini content (verified on the doubao/jimeng visible corpora).
|
||||
_SELECT_TOPK = 3
|
||||
_MASK_ALPHA = 0.04
|
||||
_MASK_DILATE_FRAC = 0.18
|
||||
|
||||
def __init__(self, logo_value: float = 255.0) -> None:
|
||||
"""Initialize the engine with embedded alpha maps.
|
||||
|
||||
Args:
|
||||
logo_value: The logo brightness value (default 255.0 = white).
|
||||
"""
|
||||
self.logo_value = logo_value
|
||||
|
||||
# Load embedded background captures
|
||||
bg_small = _load_embedded_asset("gemini_bg_48.png")
|
||||
bg_large = _load_embedded_asset("gemini_bg_96.png")
|
||||
|
||||
# Ensure correct sizes
|
||||
if bg_small.shape[:2] != (48, 48):
|
||||
bg_small = cv2.resize(bg_small, (48, 48), interpolation=cv2.INTER_AREA)
|
||||
if bg_large.shape[:2] != (96, 96):
|
||||
bg_large = cv2.resize(bg_large, (96, 96), interpolation=cv2.INTER_AREA)
|
||||
|
||||
# Calculate alpha maps
|
||||
self._alpha_small = _calculate_alpha_map(bg_small)
|
||||
self._alpha_large = _calculate_alpha_map(bg_large)
|
||||
|
||||
# Per-scale resized templates are constant (``_alpha_large`` never changes),
|
||||
# so precompute the whole fixed 16..118 ladder once: ``_scan_scales`` runs it on
|
||||
# every image (twice -- global + corner), and re-``resize``-ing the 96x96 source
|
||||
# each time is pure repeated work. Prebuilt (not lazy) so the dict is read-only
|
||||
# after construction and safe to share across threads via the module singleton.
|
||||
self._alpha_small = _calculate_alpha_map(_load_capture("gemini_bg_48.png", 48))
|
||||
self._alpha_large = _calculate_alpha_map(_load_capture("gemini_bg_96.png", 96))
|
||||
self._tmpl_cache: dict[int, NDArray[Any]] = {
|
||||
scale: cv2.resize(self._alpha_large, (scale, scale), interpolation=cv2.INTER_AREA)
|
||||
for scale in _TEMPLATE_SCALES
|
||||
side: cv2.resize(self._alpha_large, (side, side), interpolation=cv2.INTER_AREA) for side in _TEMPLATE_SCALES
|
||||
}
|
||||
|
||||
logger.debug(
|
||||
"Alpha maps loaded: small=%s, large=%s",
|
||||
self._alpha_small.shape,
|
||||
self._alpha_large.shape,
|
||||
)
|
||||
|
||||
def get_alpha_map(self, size: WatermarkSize) -> NDArray[Any]:
|
||||
"""Get the base alpha map for a specific standard size."""
|
||||
if size == WatermarkSize.SMALL:
|
||||
return self._alpha_small
|
||||
return self._alpha_large
|
||||
return self._alpha_small if size is WatermarkSize.SMALL else self._alpha_large
|
||||
|
||||
def get_interpolated_alpha(self, size_px: int) -> NDArray[Any]:
|
||||
"""Create an interpolated alpha map dynamically scaled from the high-res 96x96 base."""
|
||||
source = self._alpha_large
|
||||
if size_px == source.shape[1]:
|
||||
return source.copy()
|
||||
|
||||
interp = cv2.INTER_LINEAR if size_px > source.shape[1] else cv2.INTER_AREA
|
||||
return cv2.resize(source, (size_px, size_px), interpolation=interp)
|
||||
|
||||
# ── Detection ────────────────────────────────────────────────────
|
||||
if size_px == self._alpha_large.shape[1]:
|
||||
return self._alpha_large.copy()
|
||||
method = cv2.INTER_LINEAR if size_px > self._alpha_large.shape[1] else cv2.INTER_AREA
|
||||
return cv2.resize(self._alpha_large, (size_px, size_px), interpolation=method)
|
||||
|
||||
def _scan_scales(self, gray: NDArray[Any]) -> Iterator[tuple[int, float, tuple[int, int]]]:
|
||||
"""Yield ``(scale, max_ncc, max_loc)`` for the alpha template matched at each scale.
|
||||
|
||||
Shared multi-scale ``TM_CCOEFF_NORMED`` primitive over a normalized [0, 1]
|
||||
grayscale region, used by both the size-weighted global search in
|
||||
``detect_watermark`` and the raw-NCC corner pass in ``_corner_promote`` --
|
||||
each applies its own scoring/argmax to the yielded values. The 96x96
|
||||
``_alpha_large`` is the high-quality source downscaled per scale; the range
|
||||
covers aggressively downscaled to slightly upscaled logos.
|
||||
"""
|
||||
for scale in _TEMPLATE_SCALES:
|
||||
if scale > gray.shape[0] or scale > gray.shape[1]:
|
||||
"""Yield the strongest normalized template match at every usable scale."""
|
||||
height, width = gray.shape[:2]
|
||||
for side, template in self._tmpl_cache.items():
|
||||
if side > height or side > width:
|
||||
continue
|
||||
match_res = cv2.matchTemplate(gray, self._tmpl_cache[scale], cv2.TM_CCOEFF_NORMED)
|
||||
_, max_val, _, max_loc = cv2.minMaxLoc(match_res)
|
||||
yield scale, float(max_val), max_loc
|
||||
response = cv2.matchTemplate(gray, template, cv2.TM_CCOEFF_NORMED)
|
||||
_minimum, maximum, _min_location, max_location = cv2.minMaxLoc(response)
|
||||
yield side, float(maximum), max_location
|
||||
|
||||
def _global_candidates(self, image: NDArray[Any]) -> list[_Candidate]:
|
||||
height, width = image.shape[:2]
|
||||
search_side = min(height, width, 512)
|
||||
origin_x, origin_y = width - search_side, height - search_side
|
||||
gray = _gray_float(image[origin_y:height, origin_x:width])
|
||||
ranked = sorted(
|
||||
(
|
||||
(
|
||||
score * min(1.0, (side / 96.0) ** 0.5),
|
||||
_Candidate(side, origin_x + location[0], origin_y + location[1], score),
|
||||
)
|
||||
for side, score, location in self._scan_scales(gray)
|
||||
),
|
||||
key=lambda item: (item[0], item[1].scale, item[1].spatial, item[1].x, item[1].y),
|
||||
reverse=True,
|
||||
)
|
||||
selected: list[_Candidate] = []
|
||||
for _weighted, candidate in ranked:
|
||||
if any(_overlaps(candidate, prior) for prior in selected):
|
||||
continue
|
||||
selected.append(candidate)
|
||||
if len(selected) == self._SELECT_TOPK:
|
||||
break
|
||||
return selected
|
||||
|
||||
def _score_candidate(self, image: NDArray[Any], candidate: _Candidate) -> _Candidate:
|
||||
if candidate.spatial < 0.25:
|
||||
return candidate
|
||||
gradient, variance = self._grad_var_scores(image, candidate.scale, candidate.x, candidate.y)
|
||||
return _Candidate(candidate.scale, candidate.x, candidate.y, candidate.spatial, gradient, variance)
|
||||
|
||||
def detect_watermark(
|
||||
self,
|
||||
@@ -306,241 +199,94 @@ class GeminiEngine:
|
||||
*,
|
||||
trust_provenance: bool = False,
|
||||
) -> DetectionResult:
|
||||
"""Detect Gemini watermark using multi-scale Snap Engine logic (ported from C++ vendor algorithm).
|
||||
|
||||
``trust_provenance`` signals that external metadata already proves this is a
|
||||
Google generation (C2PA issuer "Google"/"Gemini"). The false-positive gate
|
||||
exists only to reject content that shape-matches the sparkle on NON-Google
|
||||
images (Doubao text, ornate corners); when provenance confirms Google, that
|
||||
gate would demote a genuine sparkle the vendor moved/re-rendered (bigger,
|
||||
lighter, shifted), so it is skipped. The caller (registry) still applies the
|
||||
relaxed provenance trust gate to the returned confidence."""
|
||||
"""Return the strongest sparkle-shaped bottom-right candidate."""
|
||||
result = DetectionResult()
|
||||
|
||||
if image is None or image.size == 0:
|
||||
return result
|
||||
|
||||
# Normalize to 3-channel BGR: the multi-scale search tolerates grayscale, but
|
||||
# the FP-gate / alpha-gain helpers (_core_and_bg) reduce over axis=2 and would
|
||||
# crash on a 2D/BGRA input reaching this public entry point (e.g. via the
|
||||
# registry detect adapter or the library API).
|
||||
image = image_io.to_bgr(image)
|
||||
h, w = image.shape[:2]
|
||||
base_size = force_size or get_watermark_size(w, h)
|
||||
result.size = base_size
|
||||
|
||||
# Dynamically search bottom-right corner. 512 covers up to 512px from the
|
||||
# corner -- enough for known Gemini margin variations (standard: 64+96=160px;
|
||||
# observed variants up to ~300px). 256 was too tight and caused misses.
|
||||
search_size = int(min(min(w, h), 512))
|
||||
sx1 = max(0, w - search_size)
|
||||
sy1 = max(0, h - search_size)
|
||||
|
||||
search_region = image[sy1:h, sx1:w]
|
||||
if len(search_region.shape) == 3 and search_region.shape[2] >= 3:
|
||||
gray_sr = cv2.cvtColor(search_region, cv2.COLOR_BGR2GRAY)
|
||||
else:
|
||||
gray_sr = search_region.copy()
|
||||
|
||||
gray_sr_f = gray_sr.astype(np.float32) / 255.0
|
||||
|
||||
# Phase 1 & 2: multi-scale spatial NCC search. The size weight (mimicking the
|
||||
# C++ vendor weight) overcomes the NCC bias toward tiny patches, but its single
|
||||
# argmax can bury a genuine mid-size sparkle under a LARGER, lower-fidelity
|
||||
# shape match (the 256->512 search-widening regression). So score the top-K
|
||||
# size-weighted candidates by the FULL fusion and keep the highest -- the
|
||||
# gradient term separates a true white sparkle from a shape-only decoy. See
|
||||
# _SELECT_TOPK for why top-K (not the raw-NCC argmax) preserves tiny-patch
|
||||
# suppression and so cannot add a false positive on non-Gemini content.
|
||||
scored: list[tuple[float, int, int, int, float]] = [] # (adj, scale, raw, x, y)
|
||||
for scale, max_val, max_loc in self._scan_scales(gray_sr_f):
|
||||
adj_val = max_val * min(1.0, (scale / 96.0) ** 0.5)
|
||||
scored.append((adj_val, scale, max_val, sx1 + max_loc[0], sy1 + max_loc[1]))
|
||||
scored.sort(reverse=True)
|
||||
|
||||
# Top-K candidates at distinct locations (NMS: drop a lower-ranked match that
|
||||
# overlaps an already-kept one -- the same sparkle matches at adjacent scales).
|
||||
candidates: list[tuple[int, int, int, float]] = []
|
||||
for _adj, scale, raw, x, y in scored:
|
||||
if any(
|
||||
abs(x - px) < 0.5 * max(scale, ps) and abs(y - py) < 0.5 * max(scale, ps)
|
||||
for ps, px, py, _ in candidates
|
||||
):
|
||||
continue
|
||||
candidates.append((scale, x, y, raw))
|
||||
if len(candidates) >= self._SELECT_TOPK:
|
||||
break
|
||||
|
||||
# Corner promotion: a near-perfect small bottom-right sparkle the size weight
|
||||
# buries even below the top-K (see _CORNER_PROMOTE_NCC) -- add it as a candidate.
|
||||
promoted = self._corner_promote(image, candidates[0][3] if candidates else -1.0)
|
||||
source = image_io.to_bgr(image)
|
||||
height, width = source.shape[:2]
|
||||
result.size = force_size or get_watermark_size(width, height)
|
||||
candidates = self._global_candidates(source)
|
||||
promoted = self._corner_promote(source, candidates[0].spatial if candidates else -1.0)
|
||||
if promoted is not None:
|
||||
candidates.append(promoted)
|
||||
|
||||
# No candidate at any scale: the search region is smaller than the 16px template
|
||||
# floor (an image whose short side is < 16px), so nothing is detectable. Return
|
||||
# the empty (detected=False) result rather than dereferencing candidates[0].
|
||||
candidates.append(_Candidate(promoted[0], promoted[1], promoted[2], promoted[3]))
|
||||
if not candidates:
|
||||
return result
|
||||
|
||||
# Select the candidate with the highest full-fusion confidence (pre-FP-gate).
|
||||
best_scale, pos_x, pos_y, best_raw_ncc = candidates[0]
|
||||
grad_score, var_score, best_fused = 0.0, 0.0, -1.0
|
||||
for c_scale, c_x, c_y, c_raw in candidates:
|
||||
if c_raw < 0.25:
|
||||
c_grad, c_var, c_fused = 0.0, 0.0, max(0.0, c_raw * 0.5)
|
||||
else:
|
||||
c_grad, c_var = self._grad_var_scores(image, c_scale, c_x, c_y)
|
||||
c_fused = c_raw * 0.50 + c_grad * 0.30 + c_var * 0.20
|
||||
if c_fused > best_fused:
|
||||
best_fused = c_fused
|
||||
best_scale, pos_x, pos_y = c_scale, c_x, c_y
|
||||
best_raw_ncc, grad_score, var_score = c_raw, c_grad, c_var
|
||||
best = max((self._score_candidate(source, candidate) for candidate in candidates), key=lambda item: item.fused)
|
||||
result.region = (best.x, best.y, best.scale, best.scale)
|
||||
result.spatial_score = float(best.spatial)
|
||||
result.gradient_score = float(best.gradient)
|
||||
result.variance_score = float(best.variance)
|
||||
|
||||
result.region = (pos_x, pos_y, best_scale, best_scale)
|
||||
result.spatial_score = float(best_raw_ncc)
|
||||
result.gradient_score = float(grad_score)
|
||||
result.variance_score = float(var_score)
|
||||
|
||||
if result.spatial_score < 0.25:
|
||||
result.confidence = float(max(0.0, result.spatial_score * 0.5))
|
||||
return result
|
||||
|
||||
# ── Fusion ───────────────────────────────────────────────────
|
||||
# best_fused is the selected candidate's spatial*0.5 + grad*0.3 + var*0.2.
|
||||
confidence = best_fused
|
||||
|
||||
# False-positive gate: a low-confidence match that shows NEITHER real-sparkle
|
||||
# signature is a content false positive, not a white sparkle overlay. A real
|
||||
# sparkle proves itself by a bright core (high core-ring margin, on dark/mid
|
||||
# backgrounds) OR a crisp star silhouette (high gradient NCC, on any background
|
||||
# incl. bright). Demote when both are weak -- this catches the dark/mid no-core
|
||||
# FP (low margin) AND the bright-background smooth-blob FP (high margin but low
|
||||
# gradient), which the margin check alone misses. See _SPARKLE_FP_GRAD.
|
||||
if confidence < self._SPARKLE_FP_CONF and not trust_provenance:
|
||||
alpha = self.get_interpolated_alpha(best_scale)
|
||||
pos = (pos_x, pos_y)
|
||||
margin = self._core_ring_margin(image, alpha, pos)
|
||||
low_margin = margin is not None and margin < self._SPARKLE_FP_MARGIN
|
||||
low_grad = grad_score < self._SPARKLE_FP_GRAD
|
||||
if low_margin or low_grad:
|
||||
# White-core rescue: a real faint sparkle clears the trust confidence,
|
||||
# has a bright core (not low_margin), and a near-WHITE core -- unlike the
|
||||
# colored-corner FP the low-grad demotion targets. See _SPARKLE_WHITE_SAT.
|
||||
core_sat = self._core_saturation(image, alpha, pos)
|
||||
white_core = not low_margin and core_sat is not None and core_sat <= self._SPARKLE_WHITE_SAT
|
||||
if not (confidence >= self._SPARKLE_KEEP_CONF and white_core):
|
||||
logger.debug(
|
||||
"Sparkle FP gate: conf=%.3f, margin=%s, grad=%.3f, core_sat=%s; demoting.",
|
||||
confidence,
|
||||
f"{margin:.1f}" if margin is not None else "n/a",
|
||||
grad_score,
|
||||
f"{core_sat:.2f}" if core_sat is not None else "n/a",
|
||||
)
|
||||
confidence = min(confidence, 0.30)
|
||||
|
||||
result.confidence = float(max(0.0, min(1.0, confidence)))
|
||||
confidence = best.fused
|
||||
if best.spatial >= 0.25 and confidence < self._SPARKLE_FP_CONF and not trust_provenance:
|
||||
confidence = self._apply_false_positive_gate(source, best, confidence)
|
||||
result.confidence = float(np.clip(confidence, 0.0, 1.0))
|
||||
result.detected = result.confidence >= 0.35
|
||||
|
||||
logger.debug(
|
||||
"Detection: spatial=%.3f, grad=%.3f, var=%.3f → conf=%.3f (%s)",
|
||||
result.spatial_score,
|
||||
result.gradient_score,
|
||||
var_score,
|
||||
result.confidence,
|
||||
"DETECTED" if result.detected else "not detected",
|
||||
)
|
||||
|
||||
return result
|
||||
|
||||
def _grad_var_scores(
|
||||
self,
|
||||
image: NDArray[Any],
|
||||
scale: int,
|
||||
pos_x: int,
|
||||
pos_y: int,
|
||||
) -> tuple[float, float]:
|
||||
"""Return ``(gradient_score, variance_score)`` for a candidate sparkle.
|
||||
|
||||
Factored out of ``detect_watermark`` so each top-K candidate can be scored by
|
||||
the full fusion before one is selected. The gradient NCC correlates
|
||||
Sobel-magnitude maps (shape fidelity, contrast-robust); the variance score
|
||||
rewards a flat overlay region against the row band above it.
|
||||
"""
|
||||
h, w = image.shape[:2]
|
||||
x1, y1 = pos_x, pos_y
|
||||
x2, y2 = min(w, x1 + scale), min(h, y1 + scale)
|
||||
region = image[y1:y2, x1:x2]
|
||||
gray_region = cv2.cvtColor(region, cv2.COLOR_BGR2GRAY) if region.ndim == 3 and region.shape[2] >= 3 else region
|
||||
gray_f = gray_region.astype(np.float32) / 255.0
|
||||
alpha_region = self.get_interpolated_alpha(scale)[: y2 - y1, : x2 - x1]
|
||||
|
||||
# ── Gradient NCC ──
|
||||
img_gmag = cv2.magnitude(
|
||||
cv2.Sobel(gray_f, cv2.CV_32F, 1, 0, ksize=3), cv2.Sobel(gray_f, cv2.CV_32F, 0, 1, ksize=3)
|
||||
def _apply_false_positive_gate(self, image: NDArray[Any], candidate: _Candidate, confidence: float) -> float:
|
||||
alpha = self.get_interpolated_alpha(candidate.scale)
|
||||
position = (candidate.x, candidate.y)
|
||||
margin = self._core_ring_margin(image, alpha, position)
|
||||
low_margin = margin is not None and margin < self._SPARKLE_FP_MARGIN
|
||||
low_gradient = candidate.gradient < self._SPARKLE_FP_GRAD
|
||||
if not low_margin and not low_gradient:
|
||||
return confidence
|
||||
saturation = self._core_saturation(image, alpha, position)
|
||||
neutral_core = not low_margin and saturation is not None and saturation <= self._SPARKLE_WHITE_SAT
|
||||
if confidence >= self._SPARKLE_KEEP_CONF and neutral_core:
|
||||
return confidence
|
||||
logger.debug(
|
||||
"Sparkle candidate demoted: confidence=%.3f, margin=%s, gradient=%.3f, saturation=%s",
|
||||
confidence,
|
||||
margin,
|
||||
candidate.gradient,
|
||||
saturation,
|
||||
)
|
||||
alpha_gmag = cv2.magnitude(
|
||||
cv2.Sobel(alpha_region, cv2.CV_32F, 1, 0, ksize=3), cv2.Sobel(alpha_region, cv2.CV_32F, 0, 1, ksize=3)
|
||||
return min(confidence, 0.30)
|
||||
|
||||
def _grad_var_scores(self, image: NDArray[Any], scale: int, pos_x: int, pos_y: int) -> tuple[float, float]:
|
||||
height, width = image.shape[:2]
|
||||
x2, y2 = min(width, pos_x + scale), min(height, pos_y + scale)
|
||||
region = image[pos_y:y2, pos_x:x2]
|
||||
gray = _gray_float(region)
|
||||
alpha = self.get_interpolated_alpha(scale)[: y2 - pos_y, : x2 - pos_x]
|
||||
|
||||
image_edges = cv2.magnitude(
|
||||
cv2.Sobel(gray, cv2.CV_32F, 1, 0, ksize=3),
|
||||
cv2.Sobel(gray, cv2.CV_32F, 0, 1, ksize=3),
|
||||
)
|
||||
_, grad_score, _, _ = cv2.minMaxLoc(cv2.matchTemplate(img_gmag, alpha_gmag, cv2.TM_CCOEFF_NORMED))
|
||||
|
||||
# ── Variance ──
|
||||
var_score = 0.0
|
||||
ref_h = min(y1, scale)
|
||||
if ref_h > 8:
|
||||
ref_region = image[y1 - ref_h : y1, x1:x2]
|
||||
gray_ref = cv2.cvtColor(ref_region, cv2.COLOR_BGR2GRAY) if ref_region.ndim == 3 else ref_region
|
||||
_, s_wm = cv2.meanStdDev(gray_region)
|
||||
_, s_ref = cv2.meanStdDev(gray_ref)
|
||||
if s_ref[0][0] > 5.0:
|
||||
var_score = max(0.0, min(1.0, 1.0 - (s_wm[0][0] / s_ref[0][0])))
|
||||
return float(grad_score), float(var_score)
|
||||
|
||||
def _corner_promote(
|
||||
self,
|
||||
image: NDArray[Any],
|
||||
current_raw_ncc: float,
|
||||
) -> tuple[int, int, int, float] | None:
|
||||
"""Search the bottom-right corner for a very-high-fidelity sparkle match.
|
||||
|
||||
Returns ``(scale, x, y, raw_ncc)`` when the corner holds a match with raw
|
||||
NCC >= ``_CORNER_PROMOTE_NCC`` that beats the global pick's ``current_raw_ncc``,
|
||||
else None. Used to rescue a small sparkle that the size weight buried under
|
||||
a larger, lower-fidelity match elsewhere. See ``_CORNER_PROMOTE_NCC`` and
|
||||
``_CORNER_PROMOTE_FRAC`` for the corner sizing.
|
||||
"""
|
||||
h, w = image.shape[:2]
|
||||
side = max(
|
||||
self._CORNER_PROMOTE_MIN, min(self._CORNER_PROMOTE_MAX, round(min(w, h) * self._CORNER_PROMOTE_FRAC))
|
||||
alpha_edges = cv2.magnitude(
|
||||
cv2.Sobel(alpha, cv2.CV_32F, 1, 0, ksize=3),
|
||||
cv2.Sobel(alpha, cv2.CV_32F, 0, 1, ksize=3),
|
||||
)
|
||||
cs = int(min(min(w, h), side))
|
||||
cx1, cy1 = max(0, w - cs), max(0, h - cs)
|
||||
corner = image[cy1:h, cx1:w]
|
||||
gray = cv2.cvtColor(corner, cv2.COLOR_BGR2GRAY) if corner.ndim == 3 and corner.shape[2] >= 3 else corner
|
||||
gray = gray.astype(np.float32) / 255.0
|
||||
response = cv2.matchTemplate(image_edges, alpha_edges, cv2.TM_CCOEFF_NORMED)
|
||||
_minimum, gradient, _min_location, _max_location = cv2.minMaxLoc(response)
|
||||
|
||||
best_raw = -1.0
|
||||
best_scale = 0
|
||||
best_loc = (0, 0)
|
||||
for scale, max_val, max_loc in self._scan_scales(gray):
|
||||
if max_val > best_raw:
|
||||
best_raw = max_val
|
||||
best_scale = scale
|
||||
best_loc = max_loc
|
||||
variance = 0.0
|
||||
reference_height = min(pos_y, scale)
|
||||
if reference_height > 8:
|
||||
reference = image[pos_y - reference_height : pos_y, pos_x:x2]
|
||||
reference_gray = cv2.cvtColor(reference, cv2.COLOR_BGR2GRAY) if reference.ndim == 3 else reference
|
||||
_mean, region_std = cv2.meanStdDev((gray * 255.0).astype(np.uint8))
|
||||
_reference_mean, reference_std = cv2.meanStdDev(reference_gray)
|
||||
if reference_std[0][0] > 5.0:
|
||||
variance = float(np.clip(1.0 - region_std[0][0] / reference_std[0][0], 0.0, 1.0))
|
||||
return float(gradient), variance
|
||||
|
||||
if best_raw >= self._CORNER_PROMOTE_NCC and best_raw > current_raw_ncc:
|
||||
return best_scale, cx1 + best_loc[0], cy1 + best_loc[1], float(best_raw)
|
||||
return None
|
||||
|
||||
# ── Removal ──────────────────────────────────────────────────────
|
||||
|
||||
# Footprint mask for the localize -> fill removal path. The mask must cover the
|
||||
# WHOLE sparkle including its faint semi-transparent halo, not just the bright
|
||||
# core, or the fill leaves a visible ring. Threshold the captured alpha low
|
||||
# (>_MASK_ALPHA catches the halo the core-only 0.10 misses) then dilate by a
|
||||
# sparkle-relative margin so alignment slop and the outermost halo are absorbed.
|
||||
_MASK_ALPHA = 0.04
|
||||
_MASK_DILATE_FRAC = 0.18 # dilation radius as a fraction of the sparkle scale
|
||||
def _corner_promote(self, image: NDArray[Any], current_raw_ncc: float) -> tuple[int, int, int, float] | None:
|
||||
height, width = image.shape[:2]
|
||||
desired = round(min(width, height) * self._CORNER_PROMOTE_FRAC)
|
||||
side = min(min(width, height), max(self._CORNER_PROMOTE_MIN, min(self._CORNER_PROMOTE_MAX, desired)))
|
||||
origin_x, origin_y = width - side, height - side
|
||||
matches = self._scan_scales(_gray_float(image[origin_y:height, origin_x:width]))
|
||||
best = max(matches, key=lambda item: item[1], default=None)
|
||||
if best is None or best[1] < self._CORNER_PROMOTE_NCC or best[1] <= current_raw_ncc:
|
||||
return None
|
||||
return best[0], origin_x + best[2][0], origin_y + best[2][1], float(best[1])
|
||||
|
||||
def footprint_mask(
|
||||
self,
|
||||
@@ -550,50 +296,37 @@ class GeminiEngine:
|
||||
dilate: int | None = None,
|
||||
region: tuple[int, int, int, int] | None = None,
|
||||
) -> NDArray[Any] | None:
|
||||
"""Full-frame uint8 mask (255 = sparkle) of the sparkle footprint, for the
|
||||
shared fill removal path (cv2 / MI-GAN / LaMa), or None.
|
||||
|
||||
The footprint is the interpolated captured alpha at the detected scale,
|
||||
thresholded LOW so the faint halo is included, then dilated by a
|
||||
sparkle-relative margin. When ``force`` and nothing is detected, falls back to
|
||||
the default sparkle slot for the image size (the ``--no-detect`` path).
|
||||
|
||||
``region`` is the already-resolved ``(x, y, scale)`` from the caller's detection
|
||||
(the registry passes the decision's provenance-aware region). When given, the
|
||||
mask is built from it directly WITHOUT a second internal detect -- otherwise a
|
||||
provenance/assume-relaxed sparkle would be re-demoted by the strict re-detect and
|
||||
yield no mask (reported-removed-but-unchanged). Absent ``region``, direct callers
|
||||
keep the detect-then-force behavior.
|
||||
"""
|
||||
"""Build a full-frame mask from a resolved or newly detected sparkle."""
|
||||
if image is None or image.size == 0:
|
||||
return None # guard before to_bgr (cvtColor raises on an empty Mat); mirror detect_watermark
|
||||
image = image_io.to_bgr(image)
|
||||
h, w = image.shape[:2]
|
||||
return None
|
||||
source = image_io.to_bgr(image)
|
||||
height, width = source.shape[:2]
|
||||
if region is not None:
|
||||
x, y, scale = region[0], region[1], region[2]
|
||||
x, y, scale = region[:3]
|
||||
else:
|
||||
det = self.detect_watermark(image)
|
||||
if det.detected:
|
||||
x, y, scale = det.region[0], det.region[1], det.region[2]
|
||||
detection = self.detect_watermark(source)
|
||||
if detection.detected:
|
||||
x, y, scale = detection.region[:3]
|
||||
elif force:
|
||||
cfg = get_watermark_config(w, h)
|
||||
x, y = cfg.get_position(w, h)
|
||||
scale = cfg.logo_size
|
||||
config = get_watermark_config(width, height)
|
||||
x, y = config.get_position(width, height)
|
||||
scale = config.logo_size
|
||||
else:
|
||||
return None
|
||||
alpha = self.get_interpolated_alpha(scale)
|
||||
fp = self._footprint_indices(alpha, (x, y), image.shape)
|
||||
if fp is None:
|
||||
|
||||
placed = self._footprint_indices(self.get_interpolated_alpha(scale), (x, y), source.shape)
|
||||
if placed is None:
|
||||
return None
|
||||
aroi, (y1, y2, x1, x2) = fp
|
||||
sil = (aroi > self._MASK_ALPHA).astype(np.uint8) * 255
|
||||
if int((sil > 0).sum()) == 0:
|
||||
alpha, (y1, y2, x1, x2) = placed
|
||||
silhouette = (alpha > self._MASK_ALPHA).astype(np.uint8) * 255
|
||||
if not silhouette.any():
|
||||
return None
|
||||
mask = np.zeros((h, w), np.uint8)
|
||||
mask[y1:y2, x1:x2] = sil
|
||||
d = dilate if dilate is not None else max(13, int(scale * self._MASK_DILATE_FRAC))
|
||||
if d > 0:
|
||||
mask = cv2.dilate(mask, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2 * d + 1, 2 * d + 1)))
|
||||
mask = np.zeros((height, width), dtype=np.uint8)
|
||||
mask[y1:y2, x1:x2] = silhouette
|
||||
radius = dilate if dilate is not None else max(13, int(scale * self._MASK_DILATE_FRAC))
|
||||
if radius > 0:
|
||||
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2 * radius + 1, 2 * radius + 1))
|
||||
mask = cv2.dilate(mask, kernel)
|
||||
return mask
|
||||
|
||||
def _footprint_indices(
|
||||
@@ -602,21 +335,35 @@ class GeminiEngine:
|
||||
position: tuple[int, int],
|
||||
image_shape: tuple[int, ...],
|
||||
) -> tuple[NDArray[Any], tuple[int, int, int, int]] | None:
|
||||
"""Return (alpha_roi, (y1, y2, x1, x2)) for the placed footprint, or None.
|
||||
|
||||
Shared by the over-subtraction test and the inpaint mask so both operate on
|
||||
exactly the same clipped, in-bounds region.
|
||||
"""
|
||||
x, y = position
|
||||
ah, aw = alpha_map.shape[:2]
|
||||
ih, iw = image_shape[:2]
|
||||
alpha_height, alpha_width = alpha_map.shape[:2]
|
||||
image_height, image_width = image_shape[:2]
|
||||
x1, y1 = max(0, x), max(0, y)
|
||||
x2, y2 = min(iw, x + aw), min(ih, y + ah)
|
||||
x2, y2 = min(image_width, x + alpha_width), min(image_height, y + alpha_height)
|
||||
if x1 >= x2 or y1 >= y2:
|
||||
return None
|
||||
ax1, ay1 = x1 - x, y1 - y
|
||||
alpha_roi = alpha_map[ay1 : ay1 + (y2 - y1), ax1 : ax1 + (x2 - x1)]
|
||||
return alpha_roi, (y1, y2, x1, x2)
|
||||
alpha_x, alpha_y = x1 - x, y1 - y
|
||||
clipped = alpha_map[alpha_y : alpha_y + y2 - y1, alpha_x : alpha_x + x2 - x1]
|
||||
return clipped, (y1, y2, x1, x2)
|
||||
|
||||
def _core_mask_and_box(
|
||||
self,
|
||||
image: NDArray[Any],
|
||||
alpha_map: NDArray[Any],
|
||||
position: tuple[int, int],
|
||||
) -> tuple[NDArray[Any], NDArray[Any], tuple[int, int, int, int], float] | None:
|
||||
placed = self._footprint_indices(alpha_map, position, image.shape)
|
||||
if placed is None:
|
||||
return None
|
||||
alpha, bounds = placed
|
||||
peak = float(alpha.max())
|
||||
if peak < 0.2:
|
||||
return None
|
||||
core = alpha >= peak * self._CORE_ALPHA_FRAC
|
||||
if not core.any():
|
||||
return None
|
||||
y1, y2, x1, x2 = bounds
|
||||
return core, image[y1:y2, x1:x2], bounds, peak
|
||||
|
||||
def _core_and_bg(
|
||||
self,
|
||||
@@ -624,41 +371,22 @@ class GeminiEngine:
|
||||
alpha_map: NDArray[Any],
|
||||
position: tuple[int, int],
|
||||
) -> tuple[float, float, float] | None:
|
||||
"""Return ``(core_obs, bg, a_cap)`` for the placed sparkle, or None.
|
||||
|
||||
``core_obs`` is the bright-core brightness (75th pct over the high-alpha
|
||||
core), ``bg`` the local background ring median, ``a_cap`` the captured peak
|
||||
alpha. Shared by the alpha-gain estimate and the false-positive margin gate.
|
||||
None when the footprint or the background ring cannot be sampled.
|
||||
"""
|
||||
placed = self._footprint_indices(alpha_map, position, image.shape)
|
||||
if placed is None:
|
||||
sample = self._core_mask_and_box(image, alpha_map, position)
|
||||
if sample is None:
|
||||
return None
|
||||
alpha_roi, (y1, y2, x1, x2) = placed
|
||||
a_cap = float(alpha_roi.max())
|
||||
if a_cap < 0.2:
|
||||
return None
|
||||
core = alpha_roi >= a_cap * self._CORE_ALPHA_FRAC
|
||||
if not bool(core.any()):
|
||||
return None
|
||||
# Convert only the footprint+ring crop to gray, not the whole image: every
|
||||
# sample below lives inside the ring box, so a full-image mean is wasted work
|
||||
# that scales with resolution (~70 ms on a 12 MP image, recomputed for both
|
||||
# the alpha-gain estimate and the over-subtraction gate). The crop is sized by
|
||||
# the footprint, so this is O(footprint^2) regardless of image size.
|
||||
ih, iw = image.shape[:2]
|
||||
pad = int((x2 - x1) * 0.7)
|
||||
ry1, ry2 = max(0, y1 - pad), min(ih, y2 + pad)
|
||||
rx1, rx2 = max(0, x1 - pad), min(iw, x2 + pad)
|
||||
ring = image[ry1:ry2, rx1:rx2].astype(np.float32).mean(axis=2)
|
||||
# Footprint box expressed in ring-crop coordinates.
|
||||
core, _box, (y1, y2, x1, x2), peak = sample
|
||||
height, width = image.shape[:2]
|
||||
padding = int((x2 - x1) * 0.7)
|
||||
ry1, ry2 = max(0, y1 - padding), min(height, y2 + padding)
|
||||
rx1, rx2 = max(0, x1 - padding), min(width, x2 + padding)
|
||||
luminance = image[ry1:ry2, rx1:rx2].astype(np.float32).mean(axis=2)
|
||||
fy1, fy2, fx1, fx2 = y1 - ry1, y2 - ry1, x1 - rx1, x2 - rx1
|
||||
core_obs = float(np.percentile(ring[fy1:fy2, fx1:fx2][core], 75))
|
||||
ring_mask = np.ones(ring.shape, dtype=bool)
|
||||
ring_mask[fy1:fy2, fx1:fx2] = False
|
||||
if int(ring_mask.sum()) < 10:
|
||||
core_value = float(np.percentile(luminance[fy1:fy2, fx1:fx2][core], 75))
|
||||
background = np.ones(luminance.shape, dtype=bool)
|
||||
background[fy1:fy2, fx1:fx2] = False
|
||||
if background.sum() < 10:
|
||||
return None
|
||||
return core_obs, float(np.median(ring[ring_mask])), a_cap
|
||||
return core_value, float(np.median(luminance[background])), peak
|
||||
|
||||
def _core_ring_margin(
|
||||
self,
|
||||
@@ -666,14 +394,8 @@ class GeminiEngine:
|
||||
alpha_map: NDArray[Any],
|
||||
position: tuple[int, int],
|
||||
) -> float | None:
|
||||
"""Bright-core brightness minus the local background ring (gray levels).
|
||||
|
||||
A real white sparkle overlay lifts its core above the surroundings; a
|
||||
shape-only NCC false positive on ornate/flat content does not. None when the
|
||||
background ring cannot be sampled.
|
||||
"""
|
||||
cb = self._core_and_bg(image, alpha_map, position)
|
||||
return None if cb is None else cb[0] - cb[1]
|
||||
sample = self._core_and_bg(image, alpha_map, position)
|
||||
return None if sample is None else sample[0] - sample[1]
|
||||
|
||||
def _core_saturation(
|
||||
self,
|
||||
@@ -681,57 +403,24 @@ class GeminiEngine:
|
||||
alpha_map: NDArray[Any],
|
||||
position: tuple[int, int],
|
||||
) -> float | None:
|
||||
"""Median color saturation of the sparkle core (0 = white/neutral, higher =
|
||||
colored). A real Gemini sparkle is a white star, so its core is near-neutral;
|
||||
a clean bright corner that shape-matches (sky, sun, a warm light) is colored,
|
||||
so a high core saturation flags the false positive the brightness/gradient
|
||||
gates miss. Samples the same high-alpha core pixels as :meth:`_core_and_bg`.
|
||||
None when the footprint cannot be placed or the core is empty.
|
||||
"""
|
||||
placed = self._footprint_indices(alpha_map, position, image.shape)
|
||||
if placed is None:
|
||||
sample = self._core_mask_and_box(image, alpha_map, position)
|
||||
if sample is None:
|
||||
return None
|
||||
alpha_roi, (y1, y2, x1, x2) = placed
|
||||
a_cap = float(alpha_roi.max())
|
||||
if a_cap < 0.2:
|
||||
return None
|
||||
core = alpha_roi >= a_cap * self._CORE_ALPHA_FRAC
|
||||
box = image[y1:y2, x1:x2]
|
||||
if box.shape[:2] != core.shape or not bool(core.any()):
|
||||
return None
|
||||
px = box[core].astype(np.float32) # (N, 3) BGR core pixels
|
||||
hi = px.max(axis=1)
|
||||
lo = px.min(axis=1)
|
||||
return float(np.median((hi - lo) / (hi + 1.0)))
|
||||
core, box, _bounds, _peak = sample
|
||||
pixels = box[core].astype(np.float32)
|
||||
brightest = pixels.max(axis=1)
|
||||
darkest = pixels.min(axis=1)
|
||||
return float(np.median((brightest - darkest) / (brightest + 1.0)))
|
||||
|
||||
|
||||
@functools.lru_cache(maxsize=1)
|
||||
def _shared_engine() -> GeminiEngine:
|
||||
"""Process-wide default ``GeminiEngine`` singleton.
|
||||
|
||||
The engine holds only constant assets (embedded captures, alpha maps, the
|
||||
precomputed template ladder) and takes the image as a method argument, so one
|
||||
instance is reused across every ``detect_sparkle_confidence`` call instead of
|
||||
reloading assets + recomputing alpha maps + rebuilding the template cache on
|
||||
each of the ~34k images an ``identify`` batch scans. Output is identical."""
|
||||
return GeminiEngine()
|
||||
|
||||
|
||||
def detect_sparkle_confidence(image_path: Path, *, image: NDArray[Any] | None = None) -> float | None:
|
||||
"""Visible-sparkle detection confidence for a file, for provenance use.
|
||||
|
||||
Loads the image with cv2 and runs :meth:`GeminiEngine.detect_watermark`.
|
||||
Returns the NCC confidence in [0, 1], or None if the image cannot be read
|
||||
(cv2 returns None for unsupported containers such as HEIC). Kept here so the
|
||||
cv2 dependency stays in this module; callers apply their own threshold.
|
||||
|
||||
``image`` lets a caller that has already decoded the file (e.g. ``identify``
|
||||
running several visible-mark detectors) pass the BGR array to avoid a second
|
||||
full decode; when None the file is read from ``image_path``.
|
||||
"""
|
||||
from remove_ai_watermarks import image_io
|
||||
|
||||
img = image if image is not None else image_io.imread(image_path)
|
||||
if img is None:
|
||||
"""Return the local sparkle confidence, or None when decoding fails."""
|
||||
decoded = image if image is not None else image_io.imread(image_path)
|
||||
if decoded is None:
|
||||
return None
|
||||
return float(_shared_engine().detect_watermark(img).confidence)
|
||||
return float(_shared_engine().detect_watermark(decoded).confidence)
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
"""Post-processing filters for the cleaned output.
|
||||
|
||||
``apply_analog_humanizer`` injects film grain and chromatic aberration to defeat
|
||||
digital AI-perfection classifiers (ported from NeuralBleach); ``unsharp_mask``
|
||||
``apply_analog_humanizer`` injects film grain and chromatic aberration to reduce
|
||||
overly uniform digital surfaces; ``unsharp_mask``
|
||||
counters the soft, over-smoothed look that the diffusion pass leaves behind
|
||||
(itself a common "this is AI" tell).
|
||||
"""
|
||||
@@ -16,10 +16,7 @@ from numpy.typing import NDArray
|
||||
|
||||
def apply_analog_humanizer(image: NDArray, grain_intensity: float = 4.0, chromatic_shift: int = 1) -> NDArray:
|
||||
"""
|
||||
Apply Analog Humanizer (film grain and chromatic aberration) to an image.
|
||||
This simulates analog film imperfections to defeat digital AI perfection classifiers.
|
||||
|
||||
Ported from NeuralBleach.
|
||||
Apply shared-luminance grain and a small lateral color offset.
|
||||
|
||||
Args:
|
||||
image: BGR image as numpy array (uint8).
|
||||
@@ -33,26 +30,22 @@ def apply_analog_humanizer(image: NDArray, grain_intensity: float = 4.0, chromat
|
||||
if len(image.shape) != 3 or image.shape[2] != 3:
|
||||
return image.copy()
|
||||
|
||||
# Split channels (OpenCV uses BGR)
|
||||
# B = 0, G = 1, R = 2
|
||||
# Translate the outer color channels without circular edge wrapping.
|
||||
b, g, r = cv2.split(image)
|
||||
|
||||
# 1. Chromatic Aberration
|
||||
# Shift R channel left, B channel right. np.roll is circular, so it wraps
|
||||
# the opposite edge into a thin colored fringe at the L/R borders; replicate
|
||||
# the original edge columns there to keep the intended offset interior-only.
|
||||
# Clamp so the edge-replication slices below always have a source column: a shift
|
||||
# >= width would leave them empty and crash the broadcast (r[:, -shift:] = (H, 0)).
|
||||
shift = min(chromatic_shift, image.shape[1] - 1)
|
||||
shift = min(max(0, chromatic_shift), max(0, image.shape[1] - 1))
|
||||
if shift > 0:
|
||||
r = np.roll(r, -shift, axis=1)
|
||||
r[:, -shift:] = r[:, -shift - 1 : -shift]
|
||||
b = np.roll(b, shift, axis=1)
|
||||
b[:, :shift] = b[:, shift : shift + 1]
|
||||
shifted_b = np.empty_like(b)
|
||||
shifted_b[:, :shift] = b[:, :1]
|
||||
shifted_b[:, shift:] = b[:, :-shift]
|
||||
b = shifted_b
|
||||
|
||||
shifted_r = np.empty_like(r)
|
||||
shifted_r[:, :-shift] = r[:, shift:]
|
||||
shifted_r[:, -shift:] = r[:, -1:]
|
||||
r = shifted_r
|
||||
|
||||
merged = cv2.merge((b, g, r))
|
||||
|
||||
# 2. Film Grain (Gaussian Noise)
|
||||
if grain_intensity > 0:
|
||||
img_f = merged.astype(np.float32)
|
||||
noise = np.random.normal(0, grain_intensity, img_f.shape).astype(np.float32)
|
||||
|
||||
@@ -20,12 +20,23 @@ never as "clean". See CLAUDE.md "SynthID detection is metadata-only".
|
||||
from __future__ import annotations
|
||||
|
||||
import base64
|
||||
import contextlib
|
||||
import itertools
|
||||
import logging
|
||||
from dataclasses import dataclass, field
|
||||
from typing import TYPE_CHECKING, Any, cast
|
||||
|
||||
from remove_ai_watermarks._internal.c2pa import (
|
||||
c2pa_info_from_manifest_store,
|
||||
cbor_text_after,
|
||||
extract_c2pa_info,
|
||||
soft_binding_vendors_in,
|
||||
)
|
||||
from remove_ai_watermarks._internal.constants import (
|
||||
C2PA_AI_TOOLS,
|
||||
C2PA_AI_VENDORS,
|
||||
C2PA_IDENTITY_AI_ORGS,
|
||||
C2PA_ISSUERS,
|
||||
)
|
||||
from remove_ai_watermarks.metadata import (
|
||||
AI_METADATA_KEYS,
|
||||
AIGC_MARKERS,
|
||||
@@ -47,18 +58,6 @@ from remove_ai_watermarks.metadata import (
|
||||
xai_signature,
|
||||
xai_signature_pair,
|
||||
)
|
||||
from remove_ai_watermarks.noai.c2pa import (
|
||||
c2pa_info_from_manifest_store,
|
||||
cbor_text_after,
|
||||
extract_c2pa_info,
|
||||
soft_binding_vendors_in,
|
||||
)
|
||||
from remove_ai_watermarks.noai.constants import (
|
||||
C2PA_AI_TOOLS,
|
||||
C2PA_AI_VENDORS,
|
||||
C2PA_IDENTITY_AI_ORGS,
|
||||
C2PA_ISSUERS,
|
||||
)
|
||||
from remove_ai_watermarks.watermark_registry import GEMINI_SPARKLE_TRUST_CONF
|
||||
|
||||
if TYPE_CHECKING:
|
||||
@@ -182,8 +181,11 @@ def _external_metadata(value: Any) -> tuple[list[tuple[str, Any]], bytes]:
|
||||
continue
|
||||
if isinstance(nested, str) and (key_text == "base64" or key_text.endswith("_base64")):
|
||||
encoded = nested.split("...TRUNCATED", 1)[0]
|
||||
with contextlib.suppress(ValueError, TypeError):
|
||||
try:
|
||||
parts.append(base64.b64decode(encoded, validate=True))
|
||||
continue
|
||||
except (ValueError, TypeError):
|
||||
pass
|
||||
visit(nested)
|
||||
elif isinstance(item, (list, tuple)):
|
||||
sequence = cast("list[Any] | tuple[Any, ...]", item)
|
||||
@@ -192,10 +194,13 @@ def _external_metadata(value: Any) -> tuple[list[tuple[str, Any]], bytes]:
|
||||
elif isinstance(item, bytes):
|
||||
parts.append(item)
|
||||
elif isinstance(item, str):
|
||||
parts.append(item.encode("utf-8", "replace"))
|
||||
if item.startswith("hex:"):
|
||||
with contextlib.suppress(ValueError):
|
||||
try:
|
||||
parts.append(bytes.fromhex(item[4:]))
|
||||
return
|
||||
except ValueError:
|
||||
pass
|
||||
parts.append(item.encode("utf-8", "replace"))
|
||||
elif item is not None:
|
||||
parts.append(str(item).encode("utf-8", "replace"))
|
||||
|
||||
@@ -228,7 +233,7 @@ def _external_exif_generator(pairs: list[tuple[str, Any]], scan: bytes) -> str |
|
||||
"creatortool",
|
||||
}
|
||||
candidates = [
|
||||
str(value)
|
||||
_external_text(value)
|
||||
for key, value in pairs
|
||||
if key.lower().removeprefix("info:") in candidate_keys and isinstance(value, (str, bytes))
|
||||
]
|
||||
@@ -817,7 +822,7 @@ def _identify_from_evidence(
|
||||
issuers = [info["issuer"]] if info.get("issuer") else _issuers_in(head)
|
||||
# Full AI generation (trainedAlgorithmicMedia) vs an AI-enhanced real photo
|
||||
# (compositeWithTrainedAlgorithmicMedia). The structured kind is parsed once in
|
||||
# noai.c2pa._populate_registry_fields (covers PNG + any container the c2pa-python
|
||||
# _internal.c2pa._populate_registry_fields (covers PNG + any container the c2pa-python
|
||||
# reader handles); fall back to a raw head scan for the non-PNG raw-blob path
|
||||
# where extract_c2pa_info returns {}. Full generation wins when both appear.
|
||||
c2pa_source_kind = info.get("ai_source_kind")
|
||||
|
||||
@@ -1,7 +1,4 @@
|
||||
"""Invisible watermark removal engine.
|
||||
|
||||
Wraps the vendored noai-watermark code for removing invisible AI watermarks
|
||||
(SynthID, StableSignature, TreeRing) via diffusion-based regeneration.
|
||||
"""Diffusion engine for regenerating images that carry invisible AI watermarks.
|
||||
|
||||
This module requires the 'gpu' extra dependencies:
|
||||
uv pip install 'remove-ai-watermarks[diffusion]'
|
||||
@@ -19,10 +16,10 @@ import warnings
|
||||
from pathlib import Path
|
||||
from typing import TYPE_CHECKING, Any
|
||||
|
||||
from .noai.watermark_profiles import (
|
||||
from ._internal.watermark_profiles import (
|
||||
DEFAULT_MODEL_ID as DEFAULT_SDXL_MODEL_ID,
|
||||
)
|
||||
from .noai.watermark_profiles import (
|
||||
from ._internal.watermark_profiles import (
|
||||
resolve_seed,
|
||||
)
|
||||
|
||||
@@ -81,9 +78,6 @@ def _target_size(width: int, height: int, max_resolution: int, min_resolution: i
|
||||
class InvisibleEngine:
|
||||
"""Remove invisible AI watermarks using diffusion model regeneration.
|
||||
|
||||
Based on noai-watermark by mertizci:
|
||||
https://github.com/mertizci/noai-watermark
|
||||
|
||||
The approach encodes the image into latent space, injects controlled noise
|
||||
to break watermark patterns, and reconstructs via reverse diffusion.
|
||||
"""
|
||||
@@ -124,7 +118,7 @@ class InvisibleEngine:
|
||||
residency. CUDA only.
|
||||
"""
|
||||
|
||||
from remove_ai_watermarks.noai.watermark_remover import WatermarkRemover
|
||||
from remove_ai_watermarks._internal.watermark_remover import WatermarkRemover
|
||||
|
||||
effective_model = model_id or self.DEFAULT_MODEL_ID
|
||||
|
||||
|
||||
@@ -1,7 +1,4 @@
|
||||
"""AI metadata detection and removal.
|
||||
|
||||
Wraps the noai-watermark metadata handling for stripping AI-generation
|
||||
metadata (EXIF, PNG text chunks, C2PA provenance) from images.
|
||||
"""Detect and remove AI provenance metadata from image containers.
|
||||
|
||||
For metadata-only operations, the heavy ML dependencies are NOT required.
|
||||
"""
|
||||
@@ -10,12 +7,15 @@ from __future__ import annotations
|
||||
|
||||
import contextlib
|
||||
import functools
|
||||
import itertools
|
||||
import json
|
||||
import logging
|
||||
import re
|
||||
import struct
|
||||
from typing import TYPE_CHECKING, Any
|
||||
from typing import TYPE_CHECKING, Any, cast
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from collections.abc import Iterable
|
||||
from pathlib import Path
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
@@ -101,7 +101,7 @@ IPTC_AI_MARKERS: tuple[bytes, ...] = (
|
||||
# (Meta / Instagram / MidJourney) use ``trainedAlgorithmicMedia``. Including the bare
|
||||
# token flagged clean procedural images as AI (is_ai=high + has_invisible_target=True ->
|
||||
# a diffusion scrub of clean content), contradicting the c2pa layer, which sets
|
||||
# source_type without ai_source for it (tests/test_noai.py::test_plain_algorithmic_media_not_flagged_ai).
|
||||
# source_type without ai_source for it (tests/test_metadata_internals.py::test_plain_algorithmic_media_not_flagged_ai).
|
||||
# It is not a substring of the trained/composite tokens, so its removal does not affect
|
||||
# their detection.
|
||||
|
||||
@@ -120,12 +120,13 @@ IPTC_AI_FIELD_MARKERS: tuple[bytes, ...] = (
|
||||
# the container level (image, video, audio -- all ISOBMFF). A content sniff
|
||||
# (``ftyp``) is also accepted, so this is a fast-path hint, not the sole gate.
|
||||
_ISOBMFF_EXTS: frozenset[str] = frozenset({".avif", ".heif", ".heic", ".jxl", ".mp4", ".mov", ".m4v", ".m4a"})
|
||||
_STREAMING_ISOBMFF_EXTS: frozenset[str] = frozenset({".mp4", ".mov", ".m4v", ".m4a"})
|
||||
|
||||
# Non-ISOBMFF audio/video the ISOBMFF box walker can't reach (EBML / framed /
|
||||
# RIFF / Vorbis). remove_ai_metadata strips their container metadata losslessly
|
||||
# via ffmpeg (`-c copy`), so it needs ffmpeg on PATH for these.
|
||||
_FFMPEG_STRIP_EXTS: frozenset[str] = frozenset(
|
||||
{".webm", ".mkv", ".mka", ".mp3", ".wav", ".flac", ".ogg", ".oga", ".opus", ".aac"}
|
||||
{".webm", ".mkv", ".mka", ".avi", ".flv", ".mp3", ".wav", ".flac", ".ogg", ".oga", ".opus", ".aac"}
|
||||
)
|
||||
|
||||
# China's mandatory AI-content labeling (TC260, the national cybersecurity
|
||||
@@ -143,7 +144,7 @@ AIGC_MARKERS: tuple[bytes, ...] = (
|
||||
# the same object as a PNG ``tEXt`` chunk keyed ``AIGC`` (raw JSON, not XMP), so
|
||||
# a JSON object carrying at least one of these is accepted as a valid TC260
|
||||
# label even when the namespaced XMP element is absent.
|
||||
_TC260_FIELDS: frozenset[str] = frozenset(
|
||||
TC260_AIGC_FIELDS: frozenset[str] = frozenset(
|
||||
{
|
||||
# Producer-side schema (Doubao and most China-served generators).
|
||||
"Label",
|
||||
@@ -161,6 +162,22 @@ _TC260_FIELDS: frozenset[str] = frozenset(
|
||||
"ServiceUser",
|
||||
}
|
||||
)
|
||||
MAX_TC260_VALUE_BYTES = 1024 * 1024
|
||||
|
||||
|
||||
def parse_tc260_aigc_json(value: bytes) -> dict[str, str] | None:
|
||||
"""Parse a bounded JSON object carrying at least one normative TC260 field."""
|
||||
if len(value) > MAX_TC260_VALUE_BYTES:
|
||||
return None
|
||||
try:
|
||||
parsed = json.loads(value.rstrip(b"\x00 ").decode("utf-8"))
|
||||
except (UnicodeDecodeError, ValueError):
|
||||
return None
|
||||
if not isinstance(parsed, dict):
|
||||
return None
|
||||
fields = {str(key): str(item) for key, item in cast("dict[object, object]", parsed).items()}
|
||||
return fields if TC260_AIGC_FIELDS & fields.keys() else None
|
||||
|
||||
|
||||
# HuggingFace-hosted GPU jobs (Jobs / Spaces) stamp generated PNGs with this
|
||||
# ``tEXt`` chunk key holding the job UUID. It marks the hosting job, not a
|
||||
@@ -198,7 +215,7 @@ def _is_ai_value(value: str) -> bool:
|
||||
detection: NovelAI stamps a generic ``Title``/``Source`` text chunk (an
|
||||
AI-shaped value under a non-AI key) that ``_is_ai_key`` alone would keep.
|
||||
"""
|
||||
from remove_ai_watermarks.noai.constants import AI_GENERATOR_TOKENS
|
||||
from remove_ai_watermarks._internal.constants import AI_GENERATOR_TOKENS
|
||||
|
||||
value_lower = value.lower()
|
||||
return any(token in value_lower for token in AI_GENERATOR_TOKENS)
|
||||
@@ -290,7 +307,7 @@ def _scan_head_impl(image_path: Path, size: int) -> bytes:
|
||||
with open(image_path, "rb") as f:
|
||||
head = f.read(size)
|
||||
# Lazy import: isobmff imports this module's constants at top level.
|
||||
from remove_ai_watermarks.noai import isobmff
|
||||
from remove_ai_watermarks._internal import isobmff
|
||||
|
||||
if isobmff.is_isobmff(head):
|
||||
region = isobmff.scan_c2pa_region(image_path)
|
||||
@@ -328,7 +345,7 @@ def has_ai_metadata(image_path: Path) -> bool:
|
||||
# Check C2PA — via the official c2pa-python reader first (spec-tracking, every
|
||||
# container it supports), then a binary scan that also catches AVIF/HEIF/JPEG-XL
|
||||
# containers and synthetic/partial blobs the validator rejects.
|
||||
from remove_ai_watermarks.noai.c2pa import read_manifest_store_json
|
||||
from remove_ai_watermarks._internal.c2pa import read_manifest_store_json
|
||||
|
||||
if read_manifest_store_json(image_path) is not None:
|
||||
return True
|
||||
@@ -363,16 +380,15 @@ def aigc_label_from_metadata(data: bytes, candidates: tuple[str, ...] = ()) -> d
|
||||
from typing import cast
|
||||
|
||||
def _parse(text: str, *, require_tc260_field: bool) -> dict[str, str] | None:
|
||||
if require_tc260_field:
|
||||
return parse_tc260_aigc_json(text.encode("utf-8"))
|
||||
try:
|
||||
parsed = json.loads(text)
|
||||
except ValueError:
|
||||
return None
|
||||
if not isinstance(parsed, dict):
|
||||
return None
|
||||
fields = {str(k): str(v) for k, v in cast("dict[object, object]", parsed).items()}
|
||||
if require_tc260_field and not (_TC260_FIELDS & fields.keys()):
|
||||
return None
|
||||
return fields
|
||||
return {str(k): str(v) for k, v in cast("dict[object, object]", parsed).items()}
|
||||
|
||||
for candidate in candidates:
|
||||
if result := _parse(candidate, require_tc260_field=True):
|
||||
@@ -407,10 +423,15 @@ def aigc_label_from_metadata(data: bytes, candidates: tuple[str, ...] = ()) -> d
|
||||
def aigc_label(image_path: Path) -> dict[str, str] | None:
|
||||
"""Parse a China TC260 AI-labeling block, if present.
|
||||
|
||||
Three serializations are recognized:
|
||||
Supported serializations are:
|
||||
|
||||
- a PNG ``tEXt``/``iTXt`` chunk keyed ``AIGC`` carrying the raw JSON object
|
||||
(as written by Doubao / ByteDance), read via PIL;
|
||||
- a native MP4/MOV ``AIGC`` key in ``moov.udta.meta.keys`` whose matching
|
||||
``ilst`` item carries the raw JSON object;
|
||||
- a native MKV/WebM ``AIGC`` simple tag carrying the raw JSON object;
|
||||
- a native AVI ``LIST/INFO/AIGC`` chunk or FLV
|
||||
``script.onMetaData.AIGC`` string carrying the raw JSON object;
|
||||
- an XMP ``<TC260:AIGC>{...}</TC260:AIGC>`` block (HTML-entity encoded text),
|
||||
found by a container-agnostic raw-byte scan (PNG/JPEG/WebP alike); and
|
||||
- a raw-JSON ``{"AIGC":{...}}`` block with no namespace, as embedded in JPEG
|
||||
@@ -423,7 +444,7 @@ def aigc_label(image_path: Path) -> dict[str, str] | None:
|
||||
Returns the decoded JSON (e.g. ``{"Label": "1", "ContentProducer": ...}``)
|
||||
or None. The generic forms (the PNG-chunk key ``AIGC``, the bare
|
||||
``{"AIGC":...}`` object, and the bare ``AIGC{...}`` blob) are accepted only
|
||||
if they carry at least one known TC260 field (``_TC260_FIELDS``); the
|
||||
if they carry at least one known TC260 field (``TC260_AIGC_FIELDS``); the
|
||||
namespaced XMP element is unambiguous, so any JSON object is accepted.
|
||||
"""
|
||||
try:
|
||||
@@ -434,9 +455,47 @@ def aigc_label(image_path: Path) -> dict[str, str] | None:
|
||||
except Exception as exc:
|
||||
logger.debug("PIL could not open %s for AIGC chunk scan: %s", image_path, exc)
|
||||
value = None
|
||||
|
||||
if isinstance(value, str) and (result := aigc_label_from_metadata(b"", (value,))):
|
||||
return result
|
||||
|
||||
# Native MP4/MOV TC260 metadata (TC260-PG-20257A): the ``AIGC`` key lives
|
||||
# in ``moov.udta.meta.keys`` and points to a raw JSON value in ``ilst``.
|
||||
# Read it through the bounded box walker so a tail ``moov`` after a large
|
||||
# ``mdat`` is found without loading or scanning the media payload.
|
||||
from remove_ai_watermarks._internal.isobmff import tc260_aigc_payloads
|
||||
|
||||
isobmff_candidates = tuple(payload.decode("utf-8", "replace") for payload in tc260_aigc_payloads(image_path))
|
||||
if result := aigc_label_from_metadata(b"", isobmff_candidates):
|
||||
return result
|
||||
|
||||
# Native MKV/WebM TC260 metadata: ``Segment.Tags.Tag.SimpleTag`` carries
|
||||
# ``TagName=AIGC`` and the raw JSON in ``TagString``. The EBML walker seeks
|
||||
# over clusters and reads only bounded metadata values.
|
||||
from remove_ai_watermarks._internal.ebml import tc260_aigc_payloads as ebml_tc260_aigc_payloads
|
||||
|
||||
ebml_candidates = tuple(payload.decode("utf-8", "replace") for payload in ebml_tc260_aigc_payloads(image_path))
|
||||
if result := aigc_label_from_metadata(b"", ebml_candidates):
|
||||
return result
|
||||
|
||||
# Native AVI and FLV TC260 metadata. Both readers walk their container
|
||||
# structures and skip media payloads instead of relying on a raw substring
|
||||
# that could collide inside compressed video.
|
||||
legacy_payloads: tuple[bytes, ...] = ()
|
||||
if image_path.suffix.lower() == ".avi":
|
||||
from remove_ai_watermarks._internal.riff import tc260_aigc_payloads as riff_tc260_aigc_payloads
|
||||
|
||||
legacy_payloads = riff_tc260_aigc_payloads(image_path)
|
||||
elif image_path.suffix.lower() == ".flv":
|
||||
from remove_ai_watermarks._internal.flv import tc260_aigc_payloads as flv_tc260_aigc_payloads
|
||||
|
||||
legacy_payloads = flv_tc260_aigc_payloads(image_path)
|
||||
legacy_candidates = tuple(payload.decode("utf-8", "replace") for payload in legacy_payloads)
|
||||
if result := aigc_label_from_metadata(b"", legacy_candidates):
|
||||
return result
|
||||
|
||||
data = scan_head(image_path)
|
||||
candidates = (value,) if isinstance(value, str) else ()
|
||||
return aigc_label_from_metadata(data, candidates)
|
||||
return aigc_label_from_metadata(data)
|
||||
|
||||
|
||||
# C2PA "Durable Content Credentials" manifest repositories (C2PA 2.4). When the
|
||||
@@ -610,7 +669,7 @@ def synthid_source(image_path: Path) -> str | None:
|
||||
Returns:
|
||||
Comma-joined vendor name(s) (e.g. ``"OpenAI"``) or None.
|
||||
"""
|
||||
from remove_ai_watermarks.noai.c2pa import extract_c2pa_info, synthid_vendors_in
|
||||
from remove_ai_watermarks._internal.c2pa import extract_c2pa_info, synthid_vendors_in
|
||||
|
||||
# PNG: the caBX chunk parser gives a clean, structured issuer.
|
||||
vendors = extract_c2pa_info(image_path).get("synthid_vendors")
|
||||
@@ -630,15 +689,15 @@ def synthid_source(image_path: Path) -> str | None:
|
||||
return ", ".join(matched) if matched else None
|
||||
|
||||
|
||||
def generator_from_metadata(candidates: list[str], scan: bytes = b"") -> str | None:
|
||||
def generator_from_metadata(candidates: Iterable[str], scan: bytes = b"") -> str | None:
|
||||
"""Return a known AI generator from collected EXIF, PNG, or XMP values."""
|
||||
from remove_ai_watermarks.noai.constants import AI_GENERATOR_TOKENS
|
||||
from remove_ai_watermarks._internal.constants import AI_GENERATOR_TOKENS
|
||||
|
||||
candidates.extend(
|
||||
creator_tools = (
|
||||
match.group(1).decode("latin1", "replace")
|
||||
for match in re.finditer(rb"CreatorTool[>\"'=\s]{1,4}([^<\"']{1,80})", scan)
|
||||
)
|
||||
for value in candidates:
|
||||
for value in itertools.chain(candidates, creator_tools):
|
||||
if any(token in value.lower() for token in AI_GENERATOR_TOKENS):
|
||||
return value.strip()
|
||||
return None
|
||||
@@ -752,7 +811,7 @@ def _is_aigc_exif_value(raw: object) -> bool:
|
||||
Mirrors ``aigc_label``'s EXIF path: the ``{"AIGC":{...}}`` wrapper embedded in
|
||||
``UserComment`` / ``ImageDescription`` by China-served generators (Doubao's
|
||||
producer schema AND Tencent Cloud's service-provider schema, both keyed under
|
||||
``_TC260_FIELDS``). Gated on both the ``AIGC`` marker and a TC260 field so a
|
||||
``TC260_AIGC_FIELDS``). Gated on both the ``AIGC`` marker and a TC260 field so a
|
||||
coincidental token cannot false-drop a genuine caption/comment. Accepts a ``str``
|
||||
too (a PNG ``tEXt``/``iTXt`` value), not only EXIF bytes.
|
||||
"""
|
||||
@@ -763,7 +822,7 @@ def _is_aigc_exif_value(raw: object) -> bool:
|
||||
if b"AIGC" not in raw:
|
||||
return False
|
||||
text = bytes(raw).decode("latin-1", "ignore")
|
||||
return any(field in text for field in _TC260_FIELDS)
|
||||
return any(field in text for field in TC260_AIGC_FIELDS)
|
||||
|
||||
|
||||
def _ai_exif_targets(loaded: dict[str, Any]) -> list[tuple[str, int, bytes, str]]:
|
||||
@@ -782,7 +841,7 @@ def _ai_exif_targets(loaded: dict[str, Any]) -> list[tuple[str, int, bytes, str]
|
||||
"""
|
||||
import piexif
|
||||
|
||||
from remove_ai_watermarks.noai.constants import AI_GENERATOR_TOKENS
|
||||
from remove_ai_watermarks._internal.constants import AI_GENERATOR_TOKENS
|
||||
|
||||
ifd0: dict[int, Any] = loaded.get("0th") or {}
|
||||
ifde: dict[int, Any] = loaded.get("Exif") or {}
|
||||
@@ -841,7 +900,7 @@ def get_ai_metadata(image_path: Path) -> dict[str, str]:
|
||||
"""
|
||||
from PIL import Image
|
||||
|
||||
from remove_ai_watermarks.noai.c2pa import extract_c2pa_info, soft_binding_vendors_in, synthid_verdict
|
||||
from remove_ai_watermarks._internal.c2pa import extract_c2pa_info, soft_binding_vendors_in, synthid_verdict
|
||||
|
||||
result: dict[str, str] = {}
|
||||
|
||||
@@ -861,7 +920,7 @@ def get_ai_metadata(image_path: Path) -> dict[str, str]:
|
||||
except Exception as exc:
|
||||
logger.debug("PIL could not open %s for AI-metadata scan: %s", image_path, exc)
|
||||
|
||||
# C2PA manifest fields from the single canonical parser (noai/c2pa.py).
|
||||
# C2PA manifest fields from the single canonical parser (_internal/c2pa.py).
|
||||
c2pa = extract_c2pa_info(image_path)
|
||||
for key in (
|
||||
"c2pa_manifest",
|
||||
@@ -1149,39 +1208,56 @@ def remove_ai_metadata(
|
||||
# strip C2PA + AI-label boxes at the container level without re-encoding.
|
||||
# Avoids needing PIL plugins (pillow-heif / pillow-jxl) and preserves the
|
||||
# codestream bit-for-bit. MP4/MOV/M4A are ISOBMFF too, so the same top-level
|
||||
# uuid/jumb box walker applies. Route by suffix OR by an ``ftyp`` content
|
||||
# sniff, so a correctly-shaped container is handled whatever its extension.
|
||||
from remove_ai_watermarks.noai.isobmff import (
|
||||
# uuid/jumb box walker applies. Known media suffixes take the bounded,
|
||||
# offset-preserving streaming path; images retain the in-memory item scrub
|
||||
# needed for XMP/EXIF inside mdat/idat. Route the remaining formats by suffix
|
||||
# OR by an ``ftyp`` content sniff.
|
||||
from remove_ai_watermarks._internal.isobmff import (
|
||||
blank_ai_exif_tokens,
|
||||
blank_ai_xmp_packets,
|
||||
blank_tc260_aigc_tags,
|
||||
is_isobmff,
|
||||
strip_c2pa_boxes,
|
||||
strip_isobmff_media_file,
|
||||
)
|
||||
|
||||
with open(source_path, "rb") as f:
|
||||
head = f.read(12)
|
||||
if source_path.suffix.lower() in _STREAMING_ISOBMFF_EXTS and is_isobmff(head):
|
||||
stripped, tc260_blanked = strip_isobmff_media_file(source_path, output_path)
|
||||
logger.info(
|
||||
"Stream-blanked %d AI-provenance box(es) and %d native TC260 tag(s) → %s",
|
||||
stripped,
|
||||
tc260_blanked,
|
||||
output_path,
|
||||
)
|
||||
return output_path
|
||||
if source_path.suffix.lower() in _ISOBMFF_EXTS or is_isobmff(head):
|
||||
data = source_path.read_bytes()
|
||||
# Top-level uuid/jumb boxes (C2PA + AI-label XMP), then the meta-box items
|
||||
# the top-level stripper can't reach (HEIF/AVIF store them in mdat/idat):
|
||||
# AI-label XMP packets and AI-generator tokens in an Exif item -- both
|
||||
# blanked in place (same length) so box sizes and iloc offsets stay valid
|
||||
# and the coded image is untouched.
|
||||
# Native TC260 tags, AI-label XMP packets, and AI-generator tokens in an
|
||||
# Exif item are blanked in place (same length) so box sizes and iloc /
|
||||
# media offsets stay valid and the coded content is untouched.
|
||||
cleaned, stripped = strip_c2pa_boxes(data)
|
||||
cleaned, tc260_blanked = blank_tc260_aigc_tags(cleaned)
|
||||
cleaned, blanked = blank_ai_xmp_packets(cleaned)
|
||||
cleaned, exif_blanked = blank_ai_exif_tokens(cleaned)
|
||||
output_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
output_path.write_bytes(cleaned)
|
||||
logger.info(
|
||||
"Stripped %d AI-provenance box(es), blanked %d meta-box XMP packet(s) + %d EXIF token(s) → %s",
|
||||
"Stripped %d AI-provenance box(es), blanked %d native TC260 tag(s) + "
|
||||
"%d meta-box XMP packet(s) + %d EXIF token(s) → %s",
|
||||
stripped,
|
||||
tc260_blanked,
|
||||
blanked,
|
||||
exif_blanked,
|
||||
output_path,
|
||||
)
|
||||
return output_path
|
||||
|
||||
# Non-ISOBMFF audio/video (WebM/Matroska EBML, MP3 ID3, WAV/FLAC/OGG): the
|
||||
# Non-ISOBMFF audio/video (WebM/Matroska EBML, AVI/FLV, MP3 ID3,
|
||||
# WAV/FLAC/OGG): the
|
||||
# box walker can't reach these, so strip container metadata losslessly via
|
||||
# ffmpeg (-c copy -- codec data untouched, only tags/chapters dropped).
|
||||
if source_path.suffix.lower() in _FFMPEG_STRIP_EXTS:
|
||||
|
||||
@@ -1,562 +0,0 @@
|
||||
"""C2PA (Coalition for Content Provenance and Authenticity) metadata handling.
|
||||
|
||||
Reading goes through the official c2pa-python ``Reader`` first (any container it
|
||||
supports), via ``extract_c2pa_info`` / ``read_manifest_store_json``. The
|
||||
hand-rolled PNG ``caBX`` JUMBF-chunk tools below (``has_c2pa_metadata`` /
|
||||
``extract_c2pa_chunk`` / ``inject_c2pa_chunk`` and the ``_extract_c2pa_info_png``
|
||||
fallback) cover raw-chunk extraction, re-injection, and the cases the validator
|
||||
rejects (synthetic/partial blobs, a broken/absent wheel). Known issuers:
|
||||
|
||||
- Google Imagen
|
||||
- Adobe Firefly
|
||||
- Microsoft Designer
|
||||
- OpenAI (ChatGPT, GPT-4o, Sora, DALL-E)
|
||||
- Truepic (signing authority)
|
||||
|
||||
The fallback parser uses byte-level scanning — it does not validate JUMBF/CBOR
|
||||
structure but reliably identifies known signatures, issuers, tools, and actions.
|
||||
The vendor / source-type / SynthID / soft-binding registry scan
|
||||
(``_populate_registry_fields``) is shared by both the reader and fallback paths.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import contextlib
|
||||
import functools
|
||||
import json
|
||||
import logging
|
||||
import re
|
||||
import struct
|
||||
from pathlib import Path
|
||||
from typing import Any, cast
|
||||
|
||||
from remove_ai_watermarks.noai.constants import (
|
||||
C2PA_ACTIONS,
|
||||
C2PA_AI_TOOLS,
|
||||
C2PA_CHUNK_TYPE,
|
||||
C2PA_ISSUERS,
|
||||
C2PA_SIGNATURES,
|
||||
C2PA_SOFT_BINDINGS,
|
||||
PNG_SIGNATURE,
|
||||
SYNTHID_C2PA_ISSUERS,
|
||||
)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Official C2PA reader (c2pa-python, a default dependency). It is the primary,
|
||||
# spec-tracking manifest parser; the hand-rolled caBX/CBOR scanner below stays as
|
||||
# a fallback for synthetic/partial blobs the validator rejects. The import is
|
||||
# guarded so a partially-broken install degrades to the byte-scan rather than
|
||||
# crashing the dependency-light identify path.
|
||||
_C2paReader: Any = None
|
||||
with contextlib.suppress(Exception): # broken/absent wheel -> byte-scan fallback
|
||||
from c2pa import Reader as _C2paReader # pyright: ignore[reportMissingTypeStubs]
|
||||
_C2PA_READER_AVAILABLE = _C2paReader is not None
|
||||
|
||||
|
||||
def reader_available() -> bool:
|
||||
"""True when the official c2pa-python Reader imported successfully."""
|
||||
return _C2PA_READER_AVAILABLE
|
||||
|
||||
|
||||
def read_manifest_store_json(image_path: Path) -> str | None:
|
||||
"""Return the full C2PA manifest-store JSON for ``image_path``, or None.
|
||||
|
||||
Uses the official c2pa-python ``Reader`` (any container it supports: PNG,
|
||||
JPEG, WebP, AVIF/HEIF, MP4, ...). Returns None when the reader is unavailable,
|
||||
the file carries no parseable manifest, or parsing fails. The JSON is the
|
||||
WHOLE store (every manifest plus ingredient manifests), matching the
|
||||
whole-chunk semantics of the legacy byte scan -- an AI-source marker in a
|
||||
parent/ingredient manifest (e.g. a ChatGPT edit of a Sora generation) is
|
||||
still seen.
|
||||
|
||||
Memoized per (path, mtime): one identify/get_ai_metadata call invokes the
|
||||
structured parser ~3 times on the same file, so the cache turns the repeated
|
||||
crypto-validating reads into one.
|
||||
"""
|
||||
if not _C2PA_READER_AVAILABLE:
|
||||
return None
|
||||
try:
|
||||
mtime = image_path.stat().st_mtime_ns
|
||||
except OSError:
|
||||
return _read_manifest_store_impl(str(image_path))
|
||||
return _read_manifest_store_cached(str(image_path), mtime)
|
||||
|
||||
|
||||
@functools.lru_cache(maxsize=8)
|
||||
def _read_manifest_store_cached(path_str: str, _mtime_ns: int) -> str | None:
|
||||
"""Cache shim: ``_mtime_ns`` is part of the key only (invalidates on change)."""
|
||||
return _read_manifest_store_impl(path_str)
|
||||
|
||||
|
||||
def _read_manifest_store_impl(path_str: str) -> str | None:
|
||||
# try_create returns None when there is no manifest; a default Reader does no
|
||||
# trust enforcement, so an untrusted signer still yields the manifest content
|
||||
# (we report what is in the file, we do not gate on certificate trust).
|
||||
try:
|
||||
reader = _C2paReader.try_create(path_str)
|
||||
except Exception as exc: # malformed manifest, unsupported container, etc.
|
||||
logger.debug("c2pa Reader could not parse %s: %s", path_str, exc)
|
||||
return None
|
||||
if reader is None:
|
||||
return None
|
||||
try:
|
||||
with reader:
|
||||
return reader.json()
|
||||
except Exception as exc: # pragma: no cover - reader opened but json() failed
|
||||
logger.debug("c2pa Reader.json() failed on %s: %s", path_str, exc)
|
||||
return None
|
||||
|
||||
|
||||
def has_c2pa_metadata(image_path: Path) -> bool:
|
||||
"""
|
||||
Check if an image contains C2PA metadata.
|
||||
|
||||
Args:
|
||||
image_path: Path to the image file.
|
||||
|
||||
Returns:
|
||||
True if C2PA metadata is detected, False otherwise.
|
||||
"""
|
||||
image_path = Path(image_path)
|
||||
|
||||
if image_path.suffix.lower() != ".png":
|
||||
return False
|
||||
|
||||
try:
|
||||
with open(image_path, "rb") as f:
|
||||
signature = f.read(8)
|
||||
if signature != PNG_SIGNATURE:
|
||||
return False
|
||||
|
||||
file_size = f.seek(0, 2)
|
||||
f.seek(8)
|
||||
|
||||
while True:
|
||||
chunk_header = f.read(8)
|
||||
if len(chunk_header) < 8:
|
||||
break
|
||||
|
||||
length = struct.unpack(">I", chunk_header[:4])[0]
|
||||
chunk_type = chunk_header[4:8]
|
||||
# Clamp the attacker-controlled 32-bit length to the bytes that
|
||||
# actually remain, so a malformed huge length can't allocate GBs.
|
||||
safe_length = max(0, min(length, file_size - f.tell()))
|
||||
|
||||
if chunk_type == C2PA_CHUNK_TYPE:
|
||||
chunk_data = f.read(safe_length)
|
||||
# Check for any C2PA signature
|
||||
for sig in C2PA_SIGNATURES:
|
||||
if sig in chunk_data:
|
||||
return True
|
||||
# Also check if chunk_data itself contains C2PA-like patterns
|
||||
if b"jumb" in chunk_data.lower() or b"c2pa" in chunk_data.lower():
|
||||
return True
|
||||
f.read(4)
|
||||
else:
|
||||
f.seek(safe_length + 4, 1)
|
||||
|
||||
if chunk_type == b"IEND":
|
||||
break
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
return False
|
||||
|
||||
|
||||
def _claim_generator_from_store(store: dict[str, Any]) -> str | None:
|
||||
"""Structured claim-generator name from the active manifest of a store dict.
|
||||
|
||||
Prefers the top-level ``claim_generator`` string (Firefly: "Adobe_Firefly"),
|
||||
falling back to the first ``claim_generator_info[].name`` (ChatGPT keys it
|
||||
only there). isprintable() guards against odd binary-ish values.
|
||||
"""
|
||||
active = _active_manifest(store)
|
||||
generator: Any = active.get("claim_generator")
|
||||
if not (isinstance(generator, str) and generator):
|
||||
info_list: list[Any] = active.get("claim_generator_info") or []
|
||||
if info_list and isinstance(first := info_list[0], dict):
|
||||
generator = cast("dict[str, Any]", first).get("name")
|
||||
return generator if isinstance(generator, str) and generator and generator.isprintable() else None
|
||||
|
||||
|
||||
def _active_manifest(store: dict[str, Any]) -> dict[str, Any]:
|
||||
"""The active manifest dict from a manifest-store dict, or {} when absent."""
|
||||
manifests: Any = store.get("manifests")
|
||||
if not isinstance(manifests, dict):
|
||||
return {}
|
||||
active = cast("dict[str, Any]", manifests).get(store.get("active_manifest", ""))
|
||||
return cast("dict[str, Any]", active) if isinstance(active, dict) else {}
|
||||
|
||||
|
||||
def _info_from_store(store: dict[str, Any], store_bytes: bytes) -> dict[str, Any]:
|
||||
"""Build normalized C2PA info from one parsed manifest store."""
|
||||
c2pa_info: dict[str, Any] = {
|
||||
"has_c2pa": True,
|
||||
"type": "C2PA (Coalition for Content Provenance and Authenticity)",
|
||||
"c2pa_manifest": f"C2PA manifest store ({len(store_bytes)} bytes)",
|
||||
}
|
||||
# The whole-store JSON carries every vendor / source-type / SynthID /
|
||||
# soft-binding signature (across active + ingredient manifests), so the same
|
||||
# registry scan that runs on the raw caBX chunk applies unchanged here.
|
||||
_populate_registry_fields(store_bytes, c2pa_info)
|
||||
|
||||
if generator := _claim_generator_from_store(store):
|
||||
c2pa_info["claim_generator"] = generator
|
||||
sig: Any = _active_manifest(store).get("signature_info")
|
||||
if isinstance(sig, dict) and (time := cast("dict[str, Any]", sig).get("time")):
|
||||
c2pa_info["timestamp"] = str(time)
|
||||
return c2pa_info
|
||||
|
||||
|
||||
def _info_from_store_json(store_json: str) -> dict[str, Any]:
|
||||
"""Build the C2PA info dict from a c2pa-python manifest-store JSON string."""
|
||||
store_bytes = store_json.encode("utf-8")
|
||||
try:
|
||||
parsed: Any = json.loads(store_json)
|
||||
except (ValueError, TypeError):
|
||||
parsed = {}
|
||||
store = cast("dict[str, Any]", parsed) if isinstance(parsed, dict) else {}
|
||||
return _info_from_store(store, store_bytes)
|
||||
|
||||
|
||||
def c2pa_info_from_manifest_store(store: str | dict[str, Any]) -> dict[str, Any]:
|
||||
"""Build normalized C2PA evidence from an externally collected manifest store.
|
||||
|
||||
``store`` may be the JSON string returned by ``c2pa.Reader.json()`` or its
|
||||
decoded dictionary form. This is the non-file-backed counterpart to
|
||||
:func:`extract_c2pa_info`.
|
||||
"""
|
||||
if isinstance(store, dict):
|
||||
parsed = store
|
||||
try:
|
||||
store_json = json.dumps(store, ensure_ascii=False)
|
||||
except (TypeError, ValueError):
|
||||
return {}
|
||||
else:
|
||||
store_json = store
|
||||
try:
|
||||
decoded: Any = json.loads(store_json)
|
||||
except (TypeError, ValueError):
|
||||
return {}
|
||||
if not isinstance(decoded, dict):
|
||||
return {}
|
||||
parsed = cast("dict[str, Any]", decoded)
|
||||
if not store_json or not parsed or parsed.get("error"):
|
||||
return {}
|
||||
return _info_from_store(parsed, store_json.encode("utf-8"))
|
||||
|
||||
|
||||
def extract_c2pa_info(image_path: Path) -> dict[str, Any]:
|
||||
"""
|
||||
Extract C2PA metadata information from an image.
|
||||
|
||||
Uses the official c2pa-python reader first (any supported container), falling
|
||||
back to the hand-rolled PNG caBX parser when the reader is unavailable or the
|
||||
file carries no parseable manifest (synthetic/partial blobs).
|
||||
|
||||
Args:
|
||||
image_path: Path to the image file.
|
||||
|
||||
Returns:
|
||||
Dictionary containing C2PA metadata info, or {} when none is found.
|
||||
"""
|
||||
image_path = Path(image_path)
|
||||
|
||||
if (store_json := read_manifest_store_json(image_path)) is not None:
|
||||
return _info_from_store_json(store_json)
|
||||
|
||||
return _extract_c2pa_info_png(image_path)
|
||||
|
||||
|
||||
def _extract_c2pa_info_png(image_path: Path) -> dict[str, Any]:
|
||||
"""Fallback PNG caBX parser, used when the c2pa-python reader finds nothing."""
|
||||
c2pa_info: dict[str, Any] = {}
|
||||
|
||||
if not has_c2pa_metadata(image_path):
|
||||
return c2pa_info
|
||||
|
||||
c2pa_info["has_c2pa"] = True
|
||||
c2pa_info["type"] = "C2PA (Coalition for Content Provenance and Authenticity)"
|
||||
|
||||
try:
|
||||
with open(image_path, "rb") as f:
|
||||
signature = f.read(8)
|
||||
if signature != PNG_SIGNATURE:
|
||||
return c2pa_info
|
||||
|
||||
file_size = f.seek(0, 2)
|
||||
f.seek(8)
|
||||
|
||||
while True:
|
||||
chunk_header = f.read(8)
|
||||
if len(chunk_header) < 8:
|
||||
break
|
||||
|
||||
length = struct.unpack(">I", chunk_header[:4])[0]
|
||||
chunk_type = chunk_header[4:8]
|
||||
# Clamp the attacker-controlled 32-bit length to the bytes that
|
||||
# actually remain, so a malformed huge length can't allocate GBs.
|
||||
safe_length = max(0, min(length, file_size - f.tell()))
|
||||
|
||||
if chunk_type == C2PA_CHUNK_TYPE:
|
||||
chunk_data = f.read(safe_length)
|
||||
_parse_c2pa_chunk(chunk_data, c2pa_info)
|
||||
f.read(4)
|
||||
else:
|
||||
f.seek(safe_length + 4, 1)
|
||||
|
||||
if chunk_type == b"IEND":
|
||||
break
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
return c2pa_info
|
||||
|
||||
|
||||
def cbor_text_after(payload: bytes, key: bytes) -> str | None:
|
||||
"""Return the CBOR text-string immediately following ``key`` in ``payload``.
|
||||
|
||||
Handles CBOR major-type 3 length prefixes: direct (0x60-0x77), 1-byte
|
||||
(0x78 NN), and 2-byte (0x79 NN NN). This reads the actual encoded value, so
|
||||
it avoids the byte-grabbing artifacts a loose regex produces (e.g. the
|
||||
leading length byte showing up as ``fGPT-4o``).
|
||||
"""
|
||||
idx = payload.find(key)
|
||||
if idx < 0:
|
||||
return None
|
||||
p = idx + len(key)
|
||||
if p >= len(payload):
|
||||
return None
|
||||
head = payload[p]
|
||||
if 0x60 <= head <= 0x77:
|
||||
length, start = head - 0x60, p + 1
|
||||
elif head == 0x78 and p + 1 < len(payload):
|
||||
length, start = payload[p + 1], p + 2
|
||||
elif head == 0x79 and p + 2 < len(payload):
|
||||
length, start = (payload[p + 1] << 8) | payload[p + 2], p + 3
|
||||
else:
|
||||
return None
|
||||
raw_str = payload[start : start + length]
|
||||
try:
|
||||
return raw_str.decode("utf-8")
|
||||
except UnicodeDecodeError:
|
||||
return raw_str.decode("latin1", errors="replace")
|
||||
|
||||
|
||||
def synthid_verdict(vendors: str) -> str:
|
||||
"""Human-readable SynthID-source verdict, shared by all callers."""
|
||||
return f"likely present ({vendors} embeds SynthID with C2PA)"
|
||||
|
||||
|
||||
def synthid_vendors_in(buffer: bytes) -> list[str]:
|
||||
"""Return SynthID-using C2PA issuer names whose signature appears in ``buffer``.
|
||||
|
||||
Shared by the PNG caBX parser and the format-agnostic binary scan so both
|
||||
apply the same SYNTHID_C2PA_ISSUERS rule against their respective bytes.
|
||||
"""
|
||||
return sorted({name for sig, name in C2PA_ISSUERS.items() if sig in buffer and sig in SYNTHID_C2PA_ISSUERS})
|
||||
|
||||
|
||||
def soft_binding_vendors_in(buffer: bytes) -> list[str]:
|
||||
"""Return forensic-watermark vendor names whose C2PA soft-binding ``alg``
|
||||
identifier appears in ``buffer``.
|
||||
|
||||
A ``c2pa.soft-binding`` assertion names the watermark scheme that stamped the
|
||||
pixels (Adobe TrustMark, Digimarc, Imatag, Steg.AI, ...). Shared by the PNG
|
||||
caBX parser and the format-agnostic binary scan so both apply the same
|
||||
C2PA_SOFT_BINDINGS rule against their respective bytes.
|
||||
"""
|
||||
return sorted({name for sig, name in C2PA_SOFT_BINDINGS.items() if sig in buffer})
|
||||
|
||||
|
||||
def _populate_registry_fields(buf: bytes, c2pa_info: dict[str, Any]) -> bool:
|
||||
"""Populate the registry-driven C2PA fields by scanning ``buf``.
|
||||
|
||||
Shared by the legacy caBX-chunk parser and the c2pa-python store-JSON path so
|
||||
both produce an identical dict shape. ``buf`` is the raw manifest bytes for
|
||||
the former and the manifest-store JSON (UTF-8) for the latter; the vendor /
|
||||
tool / action / source-type / SynthID / soft-binding signatures appear in
|
||||
both. Sets ``issuer``, ``ai_tool``, ``actions``, ``source_type``,
|
||||
``synthid_vendors`` / ``synthid_watermark``, ``soft_binding_vendors`` /
|
||||
``soft_binding`` when present and returns whether the source type is AI.
|
||||
"""
|
||||
if issuers := [name for sig, name in C2PA_ISSUERS.items() if sig in buf]:
|
||||
c2pa_info["issuer"] = ", ".join(dict.fromkeys(issuers))
|
||||
|
||||
if ai_tools := [name for sig, name in C2PA_AI_TOOLS.items() if sig in buf]:
|
||||
c2pa_info["ai_tool"] = ", ".join(dict.fromkeys(ai_tools))
|
||||
|
||||
if actions := [name for sig, name in C2PA_ACTIONS.items() if sig in buf]:
|
||||
c2pa_info["actions"] = ", ".join(actions)
|
||||
|
||||
# Digital source type (matched anywhere in the store, including ingredient
|
||||
# manifests -- a ChatGPT edit of a Sora generation carries the AI marker on
|
||||
# the parent, not the active manifest).
|
||||
# ``ai_source_kind`` is the structured generated-vs-enhanced split the caller
|
||||
# branches on (full-frame scrub vs region-targeted clean); ``source_type`` is the
|
||||
# human-readable form. The two byte strings are unambiguous:
|
||||
# "compositeWithTrainedAlgorithmicMedia" capitalizes the inner "Trained", so a
|
||||
# lowercase "trainedAlgorithmicMedia" match is standalone full generation, which
|
||||
# wins when both appear (an edit chain).
|
||||
ai_source = False
|
||||
if b"trainedAlgorithmicMedia" in buf:
|
||||
c2pa_info["source_type"] = "trainedAlgorithmicMedia (AI-generated)"
|
||||
c2pa_info["ai_source_kind"] = "generated"
|
||||
ai_source = True
|
||||
elif b"compositeWithTrainedAlgorithmicMedia" in buf:
|
||||
# Checked BEFORE bare ``algorithmicMedia``: a manifest can carry both tokens
|
||||
# (an AI-enhanced composite with a procedural ingredient), and the bare-token
|
||||
# branch would otherwise fire first and misclassify the AI composite as non-AI.
|
||||
c2pa_info["source_type"] = "compositeWithTrainedAlgorithmicMedia (AI-enhanced)"
|
||||
c2pa_info["ai_source_kind"] = "enhanced"
|
||||
ai_source = True
|
||||
elif b"algorithmicMedia" in buf:
|
||||
c2pa_info["source_type"] = "algorithmicMedia"
|
||||
|
||||
# SynthID pixel-watermark proxy: a C2PA manifest from a SynthID-using
|
||||
# vendor (Google/OpenAI) on AI-generated content implies an invisible
|
||||
# SynthID watermark in the pixels (see SYNTHID_C2PA_ISSUERS).
|
||||
synthid_vendors = synthid_vendors_in(buf)
|
||||
if synthid_vendors and ai_source:
|
||||
c2pa_info["synthid_vendors"] = synthid_vendors
|
||||
c2pa_info["synthid_watermark"] = synthid_verdict(", ".join(synthid_vendors))
|
||||
|
||||
# Soft-binding: a forensic/third-party watermark vendor named in the
|
||||
# manifest (Adobe TrustMark, Digimarc, ...), independent of the issuer.
|
||||
soft_binding_vendors = soft_binding_vendors_in(buf)
|
||||
if soft_binding_vendors:
|
||||
c2pa_info["soft_binding_vendors"] = soft_binding_vendors
|
||||
c2pa_info["soft_binding"] = ", ".join(soft_binding_vendors)
|
||||
|
||||
return ai_source
|
||||
|
||||
|
||||
def _parse_c2pa_chunk(chunk_data: bytes, c2pa_info: dict[str, Any]) -> None:
|
||||
"""Parse a raw caBX chunk payload and populate the info dictionary.
|
||||
|
||||
The fallback path, used when the official c2pa-python reader is unavailable
|
||||
or rejects the file (synthetic/partial blobs, broken installs).
|
||||
"""
|
||||
c2pa_info["c2pa_manifest"] = f"C2PA manifest ({len(chunk_data)} bytes)"
|
||||
|
||||
_populate_registry_fields(chunk_data, c2pa_info)
|
||||
|
||||
# Claim generator and spec version: read the CBOR text-string values
|
||||
# directly (regex byte-grabbing produced artifacts like ``fGPT-4o``).
|
||||
# Guard with isprintable(): on some manifests (e.g. Microsoft Designer) the
|
||||
# first ``name`` key precedes a binary field (a hash), not the generator
|
||||
# string, which would otherwise surface as control-char garbage.
|
||||
if (generator := cbor_text_after(chunk_data, b"name")) and generator.isprintable():
|
||||
c2pa_info["claim_generator"] = generator
|
||||
if (spec := cbor_text_after(chunk_data, b"specVersion")) and spec.isprintable():
|
||||
c2pa_info["c2pa_spec"] = spec
|
||||
|
||||
# Find timestamps
|
||||
timestamp_matches = re.findall(rb"(\d{14}Z)", chunk_data)
|
||||
if timestamp_matches:
|
||||
c2pa_info["timestamp"] = timestamp_matches[0].decode("utf-8")
|
||||
if len(timestamp_matches) > 1:
|
||||
c2pa_info["timestamps"] = [t.decode("utf-8") for t in timestamp_matches[:3]]
|
||||
|
||||
|
||||
def extract_c2pa_chunk(image_path: Path) -> bytes | None:
|
||||
"""
|
||||
Extract the raw C2PA JUMBF chunk from a PNG file.
|
||||
|
||||
Args:
|
||||
image_path: Path to the source PNG file.
|
||||
|
||||
Returns:
|
||||
Raw bytes of the C2PA chunk or None.
|
||||
"""
|
||||
if image_path.suffix.lower() != ".png":
|
||||
return None
|
||||
|
||||
try:
|
||||
with open(image_path, "rb") as f:
|
||||
signature = f.read(8)
|
||||
if signature != PNG_SIGNATURE:
|
||||
return None
|
||||
|
||||
file_size = f.seek(0, 2)
|
||||
f.seek(8)
|
||||
|
||||
while True:
|
||||
chunk_header = f.read(8)
|
||||
if len(chunk_header) < 8:
|
||||
break
|
||||
|
||||
length = struct.unpack(">I", chunk_header[:4])[0]
|
||||
chunk_type = chunk_header[4:8]
|
||||
# Clamp the attacker-controlled 32-bit length to the bytes that
|
||||
# actually remain, so a malformed huge length can't allocate GBs.
|
||||
safe_length = max(0, min(length, file_size - f.tell()))
|
||||
|
||||
if chunk_type == C2PA_CHUNK_TYPE:
|
||||
chunk_data = f.read(safe_length)
|
||||
crc = f.read(4)
|
||||
|
||||
# Check for any C2PA signature
|
||||
for sig in C2PA_SIGNATURES:
|
||||
if sig in chunk_data:
|
||||
return chunk_header + chunk_data + crc
|
||||
|
||||
# Also check lowercase variants
|
||||
if b"jumb" in chunk_data.lower() or b"c2pa" in chunk_data.lower():
|
||||
return chunk_header + chunk_data + crc
|
||||
else:
|
||||
f.seek(safe_length + 4, 1)
|
||||
|
||||
if chunk_type == b"IEND":
|
||||
break
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def inject_c2pa_chunk(target_path: Path, output_path: Path, c2pa_chunk: bytes) -> None:
|
||||
"""
|
||||
Inject a C2PA JUMBF chunk into a PNG file.
|
||||
|
||||
Args:
|
||||
target_path: Path to the target PNG file.
|
||||
output_path: Path where the output file will be saved.
|
||||
c2pa_chunk: Raw bytes of the C2PA chunk to inject.
|
||||
|
||||
Raises:
|
||||
ValueError: If not PNG files.
|
||||
"""
|
||||
if target_path.suffix.lower() != ".png" or output_path.suffix.lower() != ".png":
|
||||
raise ValueError("C2PA chunk injection is only supported for PNG files")
|
||||
|
||||
output_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
with open(target_path, "rb") as f_in, open(output_path, "wb") as f_out:
|
||||
f_out.write(f_in.read(8))
|
||||
|
||||
c2pa_injected = False
|
||||
while True:
|
||||
chunk_header = f_in.read(8)
|
||||
if len(chunk_header) < 8:
|
||||
break
|
||||
|
||||
length = struct.unpack(">I", chunk_header[:4])[0]
|
||||
chunk_type = chunk_header[4:8]
|
||||
chunk_data = f_in.read(length)
|
||||
crc = f_in.read(4)
|
||||
|
||||
if chunk_type == b"IDAT" and not c2pa_injected:
|
||||
f_out.write(c2pa_chunk)
|
||||
c2pa_injected = True
|
||||
|
||||
if chunk_type == C2PA_CHUNK_TYPE:
|
||||
continue
|
||||
|
||||
f_out.write(chunk_header)
|
||||
f_out.write(chunk_data)
|
||||
f_out.write(crc)
|
||||
|
||||
if chunk_type == b"IEND":
|
||||
break
|
||||
@@ -1,342 +0,0 @@
|
||||
"""Shared constants for AI metadata detection, C2PA parsing, and format support.
|
||||
|
||||
All modules reference these constants rather than hard-coding values,
|
||||
so adding a new AI tool or metadata key requires updating only this file.
|
||||
"""
|
||||
|
||||
from typing import NamedTuple
|
||||
|
||||
# Supported image formats for the pixel/removal path (CLI input validation + batch
|
||||
# discovery). PNG/JPEG/WebP decode+encode via cv2; HEIC/HEIF/AVIF via the optional
|
||||
# pillow-heif dep (image_io.imread Pillow fallback + imwrite _pil_write), so batch
|
||||
# now picks them up and the CLI no longer warns on an iPhone HEIC. JPEG-XL is left
|
||||
# out on purpose -- it is metadata/strip-only (no pixel decoder without pillow-jxl).
|
||||
SUPPORTED_FORMATS = {".png", ".jpg", ".jpeg", ".webp", ".heic", ".heif", ".avif"}
|
||||
|
||||
# AI-generated image metadata keys (Stable Diffusion, ComfyUI, Midjourney, etc.)
|
||||
AI_METADATA_KEYS = [
|
||||
"parameters", # Stable Diffusion WebUI (AUTOMATIC1111, Vladmandic)
|
||||
"postprocessing", # SD WebUI post-processing info
|
||||
"extras", # SD WebUI extras
|
||||
"workflow", # ComfyUI workflow JSON
|
||||
"prompt", # Some AI tools
|
||||
"Dream", # DreamStudio
|
||||
"SD:mode", # Stability AI
|
||||
"StableDiffusionVersion", # SD version info
|
||||
"generation_time", # Generation time info
|
||||
"Model", # Model name
|
||||
"Model hash", # Model hash
|
||||
"Seed", # Seed value
|
||||
]
|
||||
|
||||
# Standard PNG metadata keys
|
||||
PNG_METADATA_KEYS = [
|
||||
"Author",
|
||||
"Title",
|
||||
"Description",
|
||||
"Copyright",
|
||||
"Creation Time",
|
||||
"Software",
|
||||
"Disclaimer",
|
||||
"Warning",
|
||||
"Source",
|
||||
"Comment",
|
||||
]
|
||||
|
||||
# AI-related keywords for detection
|
||||
AI_KEYWORDS = [
|
||||
"prompt",
|
||||
"negative_prompt",
|
||||
"sampler",
|
||||
"cfg_scale",
|
||||
"lora",
|
||||
"diffusion",
|
||||
"comfy",
|
||||
"midjourney",
|
||||
"dall-e",
|
||||
"dalle",
|
||||
"imagen",
|
||||
"firefly",
|
||||
"c2pa",
|
||||
"chatgpt",
|
||||
"gpt-4",
|
||||
"sora",
|
||||
"openai",
|
||||
"truepic",
|
||||
"stable_diffusion",
|
||||
"invokeai",
|
||||
]
|
||||
|
||||
# C2PA (Coalition for Content Provenance and Authenticity) constants
|
||||
# Used by Google Imagen, Adobe Firefly, Microsoft Designer, OpenAI, etc.
|
||||
C2PA_CHUNK_TYPE = b"caBX" # JUMBF container chunk type for C2PA
|
||||
C2PA_SIGNATURES = [
|
||||
b"c2pa",
|
||||
b"C2PA",
|
||||
b"jumb",
|
||||
b"jumd",
|
||||
b"JUMBF",
|
||||
b"jumbf",
|
||||
b"cbor",
|
||||
b"contentcreds",
|
||||
b"digid",
|
||||
b"assertions",
|
||||
b"manifest",
|
||||
]
|
||||
|
||||
|
||||
# Single source of truth for every C2PA-signing vendor. The three per-vendor
|
||||
# facts that used to live in separate tables -- the issuer byte signature
|
||||
# (C2PA_ISSUERS), the SynthID pairing (SYNTHID_C2PA_ISSUERS), and the human
|
||||
# platform label (identify._ISSUER_PLATFORM) -- are all fields here, so adding a
|
||||
# new C2PA vendor is a single append below; the views derive automatically.
|
||||
class C2paAiVendor(NamedTuple):
|
||||
issuer: bytes # distinctive byte signature scanned in the manifest (cert org / signer)
|
||||
org: str # resolved issuer/cert-org display name (the old C2PA_ISSUERS value)
|
||||
# Human platform label for identify; None marks a signing authority / non-generator
|
||||
# (e.g. Truepic), which never names an AI platform on its own.
|
||||
platform: str | None
|
||||
# Substring matched against the joined issuer-org names for platform attribution
|
||||
# (usually a shorter form of org, e.g. "Google" for "Google LLC"); None when platform is.
|
||||
needle: str | None
|
||||
synthid: bool = False # vendor pairs an invisible SynthID pixel watermark with its C2PA manifest
|
||||
# The vendor's mere presence in the manifest asserts AI generation even without
|
||||
# a digitalSourceType (``trainedAlgorithmicMedia``) assertion. Set ONLY for a
|
||||
# pure-generator brand whose issuer/generator byte string is unambiguous (e.g.
|
||||
# "Dreamina"). Do NOT set for common-word issuers (Adobe/Google/OpenAI/Microsoft):
|
||||
# those appear incidentally in unrelated XMP/trust-chain bytes, so they stay
|
||||
# source-type-gated in identify._attribute_platform.
|
||||
asserts_ai: bool = False
|
||||
|
||||
|
||||
# C2PA known vendors, ORDERED for first-match-wins platform attribution: when a
|
||||
# manifest names several issuers (Microsoft Designer signs as "OpenAI, Microsoft"),
|
||||
# the earlier entry wins so the product, not the backend engine, is named.
|
||||
# Used by Google Imagen, Adobe Firefly, Microsoft Designer, OpenAI, etc.
|
||||
C2PA_AI_VENDORS: tuple[C2paAiVendor, ...] = (
|
||||
# Microsoft signs both Designer and Bing Image Creator; Bing now runs its own
|
||||
# MAI-Image model (not DALL-E), so the label stays model-neutral.
|
||||
C2paAiVendor(b"Microsoft", "Microsoft", "Microsoft (Bing Image Creator / Designer)", "Microsoft"),
|
||||
C2paAiVendor(b"Adobe", "Adobe", "Adobe Firefly", "Adobe"),
|
||||
C2paAiVendor(b"OpenAI", "OpenAI", "OpenAI (ChatGPT / gpt-image / DALL-E / Sora)", "OpenAI", synthid=True),
|
||||
C2paAiVendor(b"Google", "Google LLC", "Google (Gemini / Imagen)", "Google", synthid=True),
|
||||
# Stability AI signs C2PA as "Stability AI" (cert org "Stability AI Ltd").
|
||||
# Verified on a live Brand Studio (DreamStudio successor) output, 2026-05-24.
|
||||
C2paAiVendor(b"Stability AI", "Stability AI", "Stability AI (Stable Image / DreamStudio)", "Stability AI"),
|
||||
# Black Forest Labs (FLUX) API output: claim_generator_info "Black Forest
|
||||
# Labs API" + a c2pa.ai_generated_content assertion + trainedAlgorithmicMedia.
|
||||
# Verified on a real signed FLUX JPEG, 2026-05-29.
|
||||
C2paAiVendor(b"Black Forest Labs", "Black Forest Labs", "Black Forest Labs (FLUX)", "Black Forest Labs"),
|
||||
# ByteDance's Volcano Engine (Volcengine) signs its AI image output with a
|
||||
# cert from certificate_center@volcengine.com -- the platform behind Doubao /
|
||||
# Jimeng. Verified on two real signed JPEGs, 2026-05-29.
|
||||
C2paAiVendor(
|
||||
b"volcengine", "ByteDance (Volcano Engine)", "ByteDance (Doubao / Jimeng / Volcano Engine)", "ByteDance"
|
||||
),
|
||||
# Some Volcano Engine certs name the signer with the Chinese legal entity
|
||||
# "北京火山引擎科技有限公司" (Beijing Volcano Engine Technology Co., Ltd.) rather
|
||||
# than the latin "volcengine" -- the latin needle misses it entirely. The issuer is the
|
||||
# UTF-8 of the Chinese name (it appears UTF-8-encoded in the manifest-store
|
||||
# JSON and the raw caBX bytes alike); it normalizes to the same "ByteDance"
|
||||
# needle and platform as the volcengine row, so the two collapse together for
|
||||
# clash detection. Verified against compatible signed samples.
|
||||
C2paAiVendor(
|
||||
"北京火山引擎科技有限公司".encode(),
|
||||
"ByteDance (Volcano Engine)",
|
||||
"ByteDance (Doubao / Jimeng / Volcano Engine)",
|
||||
"ByteDance",
|
||||
),
|
||||
# ByteDance's international brand (BytePlus / Seedream / Seededit) signs its
|
||||
# cert as "Byteplus Pte. Ltd." -- the bare ``volcengine`` needle misses it, so
|
||||
# real BytePlus AI output was mis-attributed (an incidental "Adobe XMP" string
|
||||
# in the file's XMP made it read "Adobe Firefly"). Adding the issuer means the
|
||||
# clean manifest issuer matches "BytePlus (ByteDance)" directly. The platform
|
||||
# string mirrors the volcengine row: both share the "ByteDance" needle, so the
|
||||
# earlier row's label wins anyway -- they normalize together for clash
|
||||
# detection. Verified on compatible signed samples.
|
||||
C2paAiVendor(b"Byteplus", "BytePlus (ByteDance)", "ByteDance (Doubao / Jimeng / Volcano Engine)", "ByteDance"),
|
||||
# Dreamina (ByteDance's international Jimeng brand) signs C2PA as "Bytedance
|
||||
# Pte. Ltd." with a "Dreamina/x.y" claim generator and, unlike the Volcano
|
||||
# Engine output, NO digitalSourceType assertion -- so the generator name is the
|
||||
# only AI signal. It is registered by that generator token (which the caBX /
|
||||
# store-JSON byte scan sees across active + ingredient manifests, where the
|
||||
# active manifest is often a plain c2pa-tool transcode). ``asserts_ai`` lets the
|
||||
# issuer alone flag AI without trainedAlgorithmicMedia; "Dreamina" is a
|
||||
# distinctive brand string, so it does not risk the incidental-mention problem
|
||||
# the common-word issuers have. Verified on compatible signed samples.
|
||||
# Normalizes to the same "ByteDance" needle/platform as the
|
||||
# volcengine row (they collapse together for clash detection).
|
||||
C2paAiVendor(
|
||||
b"Dreamina",
|
||||
"ByteDance (Dreamina)",
|
||||
"ByteDance (Doubao / Jimeng / Volcano Engine)",
|
||||
"ByteDance",
|
||||
asserts_ai=True,
|
||||
),
|
||||
# Canva Magic Media signs AI-generated images as "Canva" with a generic
|
||||
# c2pa-rs claim generator + trainedAlgorithmicMedia; without this entry the
|
||||
# source read AI but no platform was attributed. Verified on compatible signed
|
||||
# samples. Canva does not use SynthID.
|
||||
C2paAiVendor(b"Canva", "Canva", "Canva (Magic Media)", "Canva"),
|
||||
# ElevenLabs is a pure generative-AI company (AI voice / audio, and image /
|
||||
# video via its API); it signs output as "Eleven Labs Inc.", so the C2PA
|
||||
# manifest alone marks AI generation. Verified on compatible signed samples.
|
||||
# ElevenLabs does not use SynthID.
|
||||
C2paAiVendor(b"Eleven Labs", "ElevenLabs", "ElevenLabs", "ElevenLabs"),
|
||||
# fal.ai (generative inference platform, issuer "fal - Features & Labels
|
||||
# Inc." / common name "fal.ai", claim generators like "fal-ai/seedvr",
|
||||
# "fal-ai/gpt-image-2"). The files carry trainedAlgorithmicMedia, so the
|
||||
# verdict already fired, but the platform stayed unattributed. fal.ai is
|
||||
# a pure generative platform, so ``asserts_ai`` also covers its output
|
||||
# that omits the source-type.
|
||||
C2paAiVendor(b"fal-ai", "fal.ai", "fal.ai", "fal.ai", asserts_ai=True),
|
||||
# Bria AI (bria.ai, generative platform) signs as "Bria Artificial
|
||||
# Intelligence" with a "Bria Ai" claim generator and source type
|
||||
# ``empty`` (NOT trainedAlgorithmicMedia), so a real signed file was
|
||||
# completely missed by identify. A pure-AI
|
||||
# vendor with distinctive strings, so ``asserts_ai`` is safe here.
|
||||
C2paAiVendor(b"Bria", "Bria Artificial Intelligence", "Bria AI", "Bria", asserts_ai=True),
|
||||
# Truepic is a C2PA signing authority, not an AI generator: no platform label,
|
||||
# never asserts is_ai (the verdict comes from the digital-source-type).
|
||||
C2paAiVendor(b"Truepic", "Truepic", None, None),
|
||||
)
|
||||
|
||||
# Deliberately NOT registered as AI-generation vendors:
|
||||
# - TikTok Inc.: signs C2PA as a content-provenance / AI-labeling authority on
|
||||
# uploads, not as an image generator. The is_ai verdict keys off the
|
||||
# digitalSourceType (trainedAlgorithmicMedia), which is already honored; a
|
||||
# bare TikTok signer marks distribution provenance, not generation, so adding
|
||||
# it as a generator needle would mis-label human uploads as AI.
|
||||
# - PixelBin.io (issuer "Fynd"): an image transformation / optimization / CDN
|
||||
# service. Its C2PA stamps a transform/upload step, not a generation event.
|
||||
# Both are excluded to avoid false-positive AI attribution; re-evaluate only
|
||||
# against a real signed file whose manifest carries a trainedAlgorithmicMedia
|
||||
# digital-source type produced by the vendor itself.
|
||||
|
||||
# Derived view -- add a vendor to C2PA_AI_VENDORS above, not here.
|
||||
# C2PA issuer signature -> resolved org name, for the manifest byte-scan.
|
||||
C2PA_ISSUERS: dict[bytes, str] = {v.issuer: v.org for v in C2PA_AI_VENDORS}
|
||||
|
||||
# Resolved org names of the vendors whose presence asserts AI generation on its
|
||||
# own (no digitalSourceType needed) -- see the ``asserts_ai`` field. identify uses
|
||||
# this to lift the AI verdict for an identity-AI issuer (e.g. Dreamina) that ships
|
||||
# no trainedAlgorithmicMedia. Derived from the flag -- set it on the vendor, not here.
|
||||
C2PA_IDENTITY_AI_ORGS: frozenset[str] = frozenset(v.org for v in C2PA_AI_VENDORS if v.asserts_ai)
|
||||
|
||||
# C2PA issuers whose signed outputs also carry an invisible SynthID pixel
|
||||
# watermark -- a metadata proxy for "SynthID is in the pixels":
|
||||
# - Google (Imagen/Gemini): embeds SynthID, long-standing (DeepMind docs).
|
||||
# - OpenAI (ChatGPT/Codex/API): pairs SynthID with C2PA since ~2026-05-20.
|
||||
# Confirmed by OpenAI's Help Center ("C2PA and SynthID in OpenAI-generated
|
||||
# images", updated 2026-05-21): "Images generated with ChatGPT, Codex, and
|
||||
# our API include both C2PA metadata and SynthID watermarks." OpenAI also
|
||||
# notes a signal may be absent if "the image was created before these
|
||||
# signals were available" -- so OpenAI images from before the rollout can
|
||||
# carry C2PA without SynthID. For OpenAI the proxy is therefore "likely",
|
||||
# not certain; the verdict string is hedged accordingly. OpenAI's own oracle
|
||||
# is openai.com/verify (Google's is the Gemini app "Verify with SynthID").
|
||||
# The issuer byte ("OpenAI"/"Google") is verified locally against data/fixtures/provenance;
|
||||
# the SynthID pairing is documented behavior (Google: DeepMind; OpenAI: above).
|
||||
# Adobe Firefly and Microsoft Designer sign C2PA but do NOT use SynthID, so a
|
||||
# C2PA manifest alone is not a SynthID signal -- the issuer is. The pixel
|
||||
# watermark is not locally detectable (proprietary decoder); the C2PA companion
|
||||
# is the proxy, and only while the manifest is intact.
|
||||
# Derived from the `synthid` flag on C2PA_AI_VENDORS -- set it there, not here.
|
||||
SYNTHID_C2PA_ISSUERS: frozenset[bytes] = frozenset(v.issuer for v in C2PA_AI_VENDORS if v.synthid)
|
||||
|
||||
# C2PA known AI tools
|
||||
C2PA_AI_TOOLS = {
|
||||
b"GPT-4o": "GPT-4o",
|
||||
b"ChatGPT": "ChatGPT",
|
||||
b"Sora": "Sora",
|
||||
b"DALL-E": "DALL-E",
|
||||
b"DALL": "DALL-E",
|
||||
b"Imagen": "Imagen",
|
||||
b"Firefly": "Firefly",
|
||||
}
|
||||
|
||||
# C2PA ``c2pa.soft-binding`` algorithm identifiers -> the forensic-watermark
|
||||
# vendor that stamped the pixels. The manifest's ``alg`` field names the
|
||||
# watermark scheme even when the watermark itself cannot be decoded locally, so
|
||||
# a byte-scan for these (keyed on a distinctive prefix to catch all variants)
|
||||
# tells us a third-party forensic watermark is present and whose. Verified
|
||||
# against the official C2PA registry (github.com/c2pa-org/softbinding-algorithm-list).
|
||||
# Adobe TrustMark is additionally decodable locally (see ``trustmark_detector``);
|
||||
# the rest (Digimarc, Imatag, Steg.AI, etc.) are proprietary oracle-only decoders.
|
||||
C2PA_SOFT_BINDINGS = {
|
||||
b"com.adobe.trustmark": "Adobe TrustMark",
|
||||
b"com.adobe.icn": "Adobe (content fingerprint)",
|
||||
b"com.digimarc": "Digimarc",
|
||||
b"com.imatag.lamark": "Imatag (Lamark)",
|
||||
b"ai.steg": "Steg.AI",
|
||||
b"com.microsoft.invismark": "Microsoft InvisMark",
|
||||
b"com.microsoft.wavmark": "Microsoft WavMark",
|
||||
b"com.verimatrix": "Verimatrix",
|
||||
b"com.nagra.nexguard": "NAGRA NexGuard",
|
||||
b"com.aiwatermark": "AIWatermark (Meta PixelSeal)",
|
||||
b"ai.trufo": "Trufo",
|
||||
b"app.overlai": "Overlai",
|
||||
b"com.markany": "MarkAny",
|
||||
b"com.mentaport": "Mentaport",
|
||||
b"es.lumatrace": "LumaTrace",
|
||||
b"ai.verda": "VerdaAI",
|
||||
b"ai.contentlens": "ContentLens",
|
||||
b"io.iscc": "ISCC (content code)",
|
||||
}
|
||||
|
||||
# Lowercased substrings that mark an AI generator when found in an EXIF
|
||||
# ``Software`` / XMP ``CreatorTool`` value. Conservative on purpose: plain
|
||||
# editors like "Adobe Photoshop" or "GIMP" must NOT match (no AI token), so only
|
||||
# generator names land here. Add new generators here, not inline.
|
||||
AI_GENERATOR_TOKENS: frozenset[str] = frozenset(
|
||||
{
|
||||
"firefly",
|
||||
"dall-e",
|
||||
"dalle",
|
||||
"midjourney",
|
||||
"stable diffusion",
|
||||
"stable-diffusion",
|
||||
"stablediffusion",
|
||||
"comfyui",
|
||||
"automatic1111",
|
||||
"invokeai",
|
||||
"imagen",
|
||||
"gpt-image",
|
||||
"nightcafe",
|
||||
"ideogram",
|
||||
"leonardo",
|
||||
"flux",
|
||||
"dreamstudio",
|
||||
# Generator stamps without C2PA:
|
||||
# - NovelAI (anime SD): PNG tEXt Software="NovelAI", Source="NovelAI
|
||||
# Diffusion V4.5 <hash>", Title="NovelAI generated image".
|
||||
# - Reve Image (reve.com): EXIF Software / XMP CreatorTool = "reve.com"
|
||||
# (the bare token "reve" would false-positive on "forever"/"reverie").
|
||||
# - Aphrodite AI: EXIF Make / Software = "Aphrodite AI[ v1.0]".
|
||||
"novelai",
|
||||
"reve.com",
|
||||
"aphrodite ai",
|
||||
# Additional verified markers:
|
||||
# - Apple Photos Clean Up (Apple Intelligence object removal): XMP
|
||||
# photoshop:Credit / IPTC credit value; composite source-type
|
||||
# covered detection, this token covers removal parity.
|
||||
# - fal-ai: generative-platform generator string.
|
||||
"apple photos clean up",
|
||||
"fal-ai",
|
||||
}
|
||||
)
|
||||
|
||||
# C2PA action types
|
||||
C2PA_ACTIONS = {
|
||||
b"c2pa.created": "created",
|
||||
b"c2pa.converted": "converted",
|
||||
b"c2pa.edited": "edited",
|
||||
b"c2pa.filtered": "filtered",
|
||||
b"c2pa.cropped": "cropped",
|
||||
b"c2pa.resized": "resized",
|
||||
b"c2pa.opened": "opened",
|
||||
b"c2pa.placed": "placed",
|
||||
}
|
||||
|
||||
# PNG signature
|
||||
PNG_SIGNATURE = b"\x89PNG\r\n\x1a\n"
|
||||
@@ -1,155 +0,0 @@
|
||||
"""Read-only metadata extraction from PNG and JPEG images.
|
||||
|
||||
Provides functions to pull all metadata, AI-only metadata, or a
|
||||
human-readable summary without modifying the source file.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import TYPE_CHECKING, Any, cast
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from pathlib import Path
|
||||
|
||||
import piexif
|
||||
from PIL import Image
|
||||
|
||||
from remove_ai_watermarks.noai.c2pa import extract_c2pa_chunk, extract_c2pa_info, has_c2pa_metadata
|
||||
from remove_ai_watermarks.noai.constants import AI_KEYWORDS, AI_METADATA_KEYS, PNG_METADATA_KEYS
|
||||
|
||||
|
||||
def extract_metadata(source_path: Path) -> dict[str, Any]:
|
||||
"""
|
||||
Extract all metadata from a PNG or JPG file.
|
||||
|
||||
Args:
|
||||
source_path: Path to the source image file.
|
||||
|
||||
Returns:
|
||||
Dictionary containing all extracted metadata.
|
||||
"""
|
||||
metadata: dict[str, Any] = {}
|
||||
|
||||
with Image.open(source_path) as img:
|
||||
# Extract EXIF data
|
||||
if "exif" in img.info:
|
||||
try:
|
||||
exif_dict = piexif.load(img.info["exif"])
|
||||
metadata["exif"] = exif_dict
|
||||
except Exception:
|
||||
metadata["exif_raw"] = img.info["exif"]
|
||||
|
||||
# Extract standard PNG metadata
|
||||
for key in PNG_METADATA_KEYS:
|
||||
if key in img.info:
|
||||
metadata[key] = img.info[key]
|
||||
|
||||
# Extract all other metadata including AI-specific
|
||||
for key, value in img.info.items():
|
||||
if not isinstance(key, str):
|
||||
continue
|
||||
if key not in metadata and key not in ["exif"]:
|
||||
metadata[key] = value
|
||||
|
||||
# Extract DPI and gamma if present
|
||||
if "dpi" in img.info:
|
||||
metadata["dpi"] = img.info["dpi"]
|
||||
if "gamma" in img.info:
|
||||
metadata["gamma"] = img.info["gamma"]
|
||||
|
||||
# Check for C2PA metadata
|
||||
if has_c2pa_metadata(source_path):
|
||||
metadata["c2pa"] = extract_c2pa_info(source_path)
|
||||
c2pa_chunk = extract_c2pa_chunk(source_path)
|
||||
if c2pa_chunk:
|
||||
metadata["c2pa_chunk"] = c2pa_chunk
|
||||
|
||||
return metadata
|
||||
|
||||
|
||||
def extract_ai_metadata(source_path: Path) -> dict[str, Any]:
|
||||
"""
|
||||
Extract only AI-generated metadata from a PNG or JPG file.
|
||||
|
||||
Args:
|
||||
source_path: Path to the source image file.
|
||||
|
||||
Returns:
|
||||
Dictionary containing only AI-related metadata.
|
||||
"""
|
||||
ai_metadata: dict[str, Any] = {}
|
||||
|
||||
with Image.open(source_path) as img:
|
||||
for key in AI_METADATA_KEYS:
|
||||
if key in img.info:
|
||||
ai_metadata[key] = img.info[key]
|
||||
|
||||
for key, value in img.info.items():
|
||||
if not isinstance(key, str):
|
||||
continue
|
||||
key_lower = key.lower()
|
||||
if key not in ai_metadata and any(kw in key_lower for kw in AI_KEYWORDS):
|
||||
ai_metadata[key] = value
|
||||
|
||||
# Check for C2PA metadata
|
||||
if has_c2pa_metadata(source_path):
|
||||
ai_metadata["c2pa"] = extract_c2pa_info(source_path)
|
||||
c2pa_chunk = extract_c2pa_chunk(source_path)
|
||||
if c2pa_chunk:
|
||||
ai_metadata["c2pa_chunk"] = c2pa_chunk
|
||||
|
||||
return ai_metadata
|
||||
|
||||
|
||||
def has_ai_metadata(image_path: Path) -> bool:
|
||||
"""
|
||||
Check if an image contains AI-generated metadata.
|
||||
|
||||
Args:
|
||||
image_path: Path to the image file.
|
||||
|
||||
Returns:
|
||||
True if AI metadata is detected, False otherwise.
|
||||
"""
|
||||
with Image.open(image_path) as img:
|
||||
for key in AI_METADATA_KEYS:
|
||||
if key in img.info:
|
||||
return True
|
||||
|
||||
return bool(has_c2pa_metadata(image_path))
|
||||
|
||||
|
||||
def get_ai_metadata_summary(source_path: Path) -> str:
|
||||
"""
|
||||
Get a human-readable summary of AI metadata.
|
||||
|
||||
Args:
|
||||
source_path: Path to the source image file.
|
||||
|
||||
Returns:
|
||||
Formatted string with AI metadata summary.
|
||||
"""
|
||||
ai_meta = extract_ai_metadata(source_path)
|
||||
|
||||
if not ai_meta:
|
||||
return "No AI metadata found."
|
||||
|
||||
lines = ["AI Image Metadata:"]
|
||||
lines.append("-" * 40)
|
||||
|
||||
for key, value in ai_meta.items():
|
||||
if key == "c2pa_chunk":
|
||||
continue
|
||||
if key == "c2pa" and isinstance(value, dict):
|
||||
lines.append("C2PA Metadata:")
|
||||
for ck, cv in cast("dict[str, Any]", value).items():
|
||||
lines.append(f" {ck}: {cv}")
|
||||
elif isinstance(value, str) and len(value) > 100:
|
||||
value = value[:100] + "..."
|
||||
lines.append(f"{key}: {value}")
|
||||
elif isinstance(value, bytes):
|
||||
lines.append(f"{key}: <binary data ({len(value)} bytes)>")
|
||||
else:
|
||||
lines.append(f"{key}: {value}")
|
||||
|
||||
return "\n".join(lines)
|
||||
@@ -1,161 +0,0 @@
|
||||
"""Img2img pipeline execution with progress monitoring and MPS fallback.
|
||||
|
||||
Extracted from ``watermark_remover.py`` to keep the ``WatermarkRemover``
|
||||
class focused on orchestration.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import contextlib
|
||||
import logging
|
||||
from typing import TYPE_CHECKING, Any
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from collections.abc import Callable
|
||||
|
||||
from PIL import Image
|
||||
|
||||
from remove_ai_watermarks.noai.progress import is_mps_error, make_pipeline_progress
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def run_img2img(
|
||||
pipeline: Any,
|
||||
image: Image.Image,
|
||||
strength: float,
|
||||
num_inference_steps: int,
|
||||
guidance_scale: float,
|
||||
generator: Any,
|
||||
device: str,
|
||||
set_progress: Callable[[str], None],
|
||||
extra_kwargs: dict[str, Any] | None = None,
|
||||
) -> Image.Image:
|
||||
"""Execute img2img with live progress and return the generated image.
|
||||
|
||||
``extra_kwargs`` overlays additional pipeline arguments (e.g. the ControlNet
|
||||
``control_image`` / ``controlnet_conditioning_scale`` and a non-empty prompt),
|
||||
so a ControlNet img2img pass reuses the same progress + fallback machinery.
|
||||
"""
|
||||
effective_steps = max(1, int(num_inference_steps * strength))
|
||||
|
||||
step_cb, first_step, done_ev, start_updater = make_pipeline_progress(
|
||||
effective_steps,
|
||||
device,
|
||||
set_progress,
|
||||
)
|
||||
start_updater()
|
||||
|
||||
try:
|
||||
result = _call_pipeline(
|
||||
pipeline, image, strength, num_inference_steps, guidance_scale, generator, step_cb, extra_kwargs
|
||||
)
|
||||
done_ev.set()
|
||||
return result.images[0]
|
||||
except TypeError as exc:
|
||||
# The only TypeError we retry is the deprecated-callback case: `_call_pipeline`
|
||||
# passes the legacy `callback`/`callback_steps` kwargs, and a diffusers version
|
||||
# that removed them raises TypeError("... unexpected keyword argument
|
||||
# 'callback'"). We then re-run once WITHOUT the progress callback. Any OTHER
|
||||
# TypeError (e.g. a bad control_image/dtype in the forward pass) is a real error
|
||||
# -- re-raise it instead of silently re-running the whole diffusion pass and
|
||||
# masking the cause.
|
||||
if "callback" not in str(exc):
|
||||
raise
|
||||
first_step.set()
|
||||
result = _call_pipeline(
|
||||
pipeline, image, strength, num_inference_steps, guidance_scale, generator, None, extra_kwargs
|
||||
)
|
||||
done_ev.set()
|
||||
return result.images[0]
|
||||
finally:
|
||||
first_step.set()
|
||||
done_ev.set()
|
||||
|
||||
|
||||
def run_img2img_with_mps_fallback(
|
||||
load_pipeline: Callable[[], Any],
|
||||
image: Image.Image,
|
||||
strength: float,
|
||||
num_inference_steps: int,
|
||||
guidance_scale: float,
|
||||
generator: Any,
|
||||
device: str,
|
||||
set_progress: Callable[[str], None],
|
||||
*,
|
||||
reload_on_cpu: Callable[[], Any],
|
||||
extra_kwargs: dict[str, Any] | None = None,
|
||||
) -> tuple[Image.Image, str]:
|
||||
"""Run img2img; on MPS error, fall back to CPU.
|
||||
|
||||
``extra_kwargs`` overlays extra pipeline arguments (used by the ControlNet
|
||||
path). Returns ``(result_image, final_device)`` — device may change to
|
||||
``"cpu"`` on fallback.
|
||||
"""
|
||||
pipeline = load_pipeline()
|
||||
|
||||
try:
|
||||
img = run_img2img(
|
||||
pipeline,
|
||||
image,
|
||||
strength,
|
||||
num_inference_steps,
|
||||
guidance_scale,
|
||||
generator,
|
||||
device,
|
||||
set_progress,
|
||||
extra_kwargs,
|
||||
)
|
||||
return img, device
|
||||
except RuntimeError as error:
|
||||
if device == "mps" and is_mps_error(error):
|
||||
logger.warning("MPS error detected: %s. Falling back to CPU.", error)
|
||||
set_progress("MPS error! Clearing cache and retrying on CPU...")
|
||||
try_empty_device_cache("mps")
|
||||
pipeline = reload_on_cpu()
|
||||
img = run_img2img(
|
||||
pipeline, image, strength, num_inference_steps, guidance_scale, None, "cpu", set_progress, extra_kwargs
|
||||
)
|
||||
return img, "cpu"
|
||||
raise
|
||||
|
||||
|
||||
def _call_pipeline(
|
||||
pipeline: Any,
|
||||
image: Image.Image,
|
||||
strength: float,
|
||||
num_inference_steps: int,
|
||||
guidance_scale: float,
|
||||
generator: Any,
|
||||
step_callback: Any,
|
||||
extra_kwargs: dict[str, Any] | None = None,
|
||||
) -> Any:
|
||||
kwargs: dict[str, Any] = {
|
||||
"prompt": "",
|
||||
"image": image,
|
||||
"strength": strength,
|
||||
"num_inference_steps": num_inference_steps,
|
||||
"guidance_scale": guidance_scale,
|
||||
"generator": generator,
|
||||
}
|
||||
if extra_kwargs:
|
||||
kwargs.update(extra_kwargs)
|
||||
if step_callback is not None:
|
||||
kwargs["callback"] = step_callback
|
||||
kwargs["callback_steps"] = 1
|
||||
return pipeline(**kwargs)
|
||||
|
||||
|
||||
def try_empty_device_cache(device: str) -> None:
|
||||
"""Best-effort free of cached GPU/MPS/XPU memory for ``device``.
|
||||
|
||||
``torch.<device>.empty_cache()`` exists for cuda/mps/xpu but not cpu (the
|
||||
hasattr guard skips the cpu no-op). Never raises -- callers use it as cleanup
|
||||
(the MPS->CPU fallback here, and the batch loop in watermark_remover).
|
||||
"""
|
||||
with contextlib.suppress(Exception):
|
||||
import torch
|
||||
|
||||
backend = getattr(torch, device, None)
|
||||
if backend is not None and hasattr(backend, "empty_cache"):
|
||||
backend.empty_cache() # type: ignore[attr-defined]
|
||||
@@ -1,284 +0,0 @@
|
||||
"""Minimal ISOBMFF box walker for stripping C2PA from AVIF / HEIF / MP4 / JPEG-XL.
|
||||
|
||||
The ISO Base Media File Format wraps content in nested ``[size:4][type:4][...]``
|
||||
boxes. C2PA stores its manifest in a top-level ``uuid`` box keyed by the
|
||||
C2PA UUID; JPEG-XL uses a ``jumb`` box (JUMBF) instead. To strip provenance
|
||||
without re-encoding the image, we walk the top-level box list, drop boxes that
|
||||
carry C2PA, and emit the rest verbatim. The codestream (``mdat`` for ISOBMFF,
|
||||
``jxlc`` / ``jxlp`` for JPEG-XL) is untouched, so pixel data is preserved
|
||||
bit-for-bit.
|
||||
|
||||
This file intentionally avoids dependencies on format-specific libraries
|
||||
(pillow-heif, pillow-jxl, pymp4) so it works on systems where they aren't
|
||||
installed.
|
||||
|
||||
Reference: ISO/IEC 14496-12 (ISOBMFF) and C2PA 2.1 spec §11.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import re
|
||||
import struct
|
||||
from typing import TYPE_CHECKING, Any
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from collections.abc import Iterator
|
||||
from pathlib import Path
|
||||
|
||||
from remove_ai_watermarks.metadata import (
|
||||
AIGC_MARKERS,
|
||||
C2PA_UUID,
|
||||
IPTC_AI_FIELD_MARKERS,
|
||||
IPTC_AI_MARKERS,
|
||||
)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Top-level box types that may carry AI provenance. ``uuid`` boxes are checked
|
||||
# against ``C2PA_UUID`` / AI-label markers before being stripped; ``jumb`` boxes
|
||||
# are always stripped (JPEG-XL uses them exclusively for JUMBF).
|
||||
C2PA_BOX_TYPES: frozenset[bytes] = frozenset({b"uuid", b"jumb"})
|
||||
|
||||
# AI-label byte markers (TC260 AIGC, IPTC "Made with AI", IPTC 2025.1 AI fields)
|
||||
# whose presence inside an XMP ``uuid`` box means the box carries an AI label.
|
||||
# Matching the payload rather than a fixed XMP UUID avoids the XMP-box UUID
|
||||
# byte-order ambiguity and stays surgical: only AI-bearing XMP is dropped, plain
|
||||
# XMP (copyright, camera info) is kept.
|
||||
_AI_LABEL_MARKERS: tuple[bytes, ...] = AIGC_MARKERS + IPTC_AI_MARKERS + IPTC_AI_FIELD_MARKERS
|
||||
|
||||
# Adobe XMP packet delimiters (XMP spec part 3). In HEIF/AVIF the XMP packet
|
||||
# sits inside a ``meta``-box ``mime`` item whose bytes live in ``mdat`` / ``idat``,
|
||||
# out of reach of the top-level box stripper, so an AI-label packet there is
|
||||
# blanked in place (see ``blank_ai_xmp_packets``).
|
||||
_XMP_PACKET_RE = re.compile(rb"<\?xpacket begin=.*?<\?xpacket end=[^>]*?\?>", re.DOTALL)
|
||||
|
||||
|
||||
def _iter_top_level_boxes(data: bytes) -> Iterator[tuple[int, int, bytes, int]]:
|
||||
"""Yield ``(start, end, type, payload_offset)`` for each top-level box.
|
||||
|
||||
Handles all three ISOBMFF box-size encodings:
|
||||
- ``size > 1``: 32-bit size field is the total box length.
|
||||
- ``size == 1``: 64-bit ``largesize`` follows after the type field.
|
||||
- ``size == 0``: box runs to end of file.
|
||||
"""
|
||||
pos = 0
|
||||
n = len(data)
|
||||
while pos + 8 <= n:
|
||||
size32 = struct.unpack_from(">I", data, pos)[0]
|
||||
box_type = data[pos + 4 : pos + 8]
|
||||
if size32 == 1:
|
||||
if pos + 16 > n:
|
||||
return
|
||||
size = struct.unpack_from(">Q", data, pos + 8)[0]
|
||||
payload_off = pos + 16
|
||||
elif size32 == 0:
|
||||
size = n - pos
|
||||
payload_off = pos + 8
|
||||
else:
|
||||
size = size32
|
||||
payload_off = pos + 8
|
||||
if size < (payload_off - pos) or pos + size > n:
|
||||
return
|
||||
yield pos, pos + size, box_type, payload_off
|
||||
pos += size
|
||||
|
||||
|
||||
def is_isobmff(data: bytes) -> bool:
|
||||
"""Cheap sniff: ISOBMFF files start with an ``ftyp`` box."""
|
||||
return len(data) >= 8 and data[4:8] == b"ftyp"
|
||||
|
||||
|
||||
def scan_c2pa_region(path: str | Path, *, max_total: int = 4 * 1024 * 1024) -> bytes:
|
||||
"""Concatenated payloads of top-level ``uuid`` / ``jumb`` boxes in an ISOBMFF
|
||||
file, found by seeking past other boxes (``mdat`` etc.) by size.
|
||||
|
||||
C2PA manifests and XMP packets (incl. AI labels) live in top-level ``uuid``
|
||||
boxes; JPEG-XL uses ``jumb``. In a streaming / non-faststart MP4 the manifest
|
||||
sits AFTER a multi-megabyte ``mdat``, so a fixed first-MB read misses it. This
|
||||
walks box headers (8-16 bytes each) and seeks past payloads it does not need,
|
||||
so it never loads ``mdat`` into memory and works on multi-GB files. Returns
|
||||
the relevant box payloads (capped at ``max_total``), or ``b""`` for a
|
||||
non-ISOBMFF file or on any read error.
|
||||
"""
|
||||
collected = bytearray()
|
||||
try:
|
||||
with open(path, "rb") as f:
|
||||
sniff = f.read(8)
|
||||
if len(sniff) < 8 or sniff[4:8] != b"ftyp":
|
||||
return b""
|
||||
f.seek(0, 2)
|
||||
file_size = f.tell()
|
||||
pos = 0
|
||||
while pos + 8 <= file_size and len(collected) < max_total:
|
||||
f.seek(pos)
|
||||
header = f.read(8)
|
||||
if len(header) < 8:
|
||||
break
|
||||
size32 = struct.unpack(">I", header[:4])[0]
|
||||
box_type = header[4:8]
|
||||
payload_off = pos + 8
|
||||
if size32 == 1:
|
||||
ext = f.read(8)
|
||||
if len(ext) < 8:
|
||||
break
|
||||
size = struct.unpack(">Q", ext)[0]
|
||||
payload_off = pos + 16
|
||||
elif size32 == 0:
|
||||
size = file_size - pos
|
||||
else:
|
||||
size = size32
|
||||
if size < (payload_off - pos) or pos + size > file_size:
|
||||
# Detection-only: a malformed box halts the walk, so a manifest
|
||||
# placed after it is missed (best-effort scan; no resync).
|
||||
break
|
||||
if box_type in C2PA_BOX_TYPES:
|
||||
f.seek(payload_off)
|
||||
to_read = min(pos + size - payload_off, max_total - len(collected))
|
||||
if to_read > 0:
|
||||
collected += f.read(to_read)
|
||||
pos += size
|
||||
except OSError:
|
||||
return b""
|
||||
return bytes(collected)
|
||||
|
||||
|
||||
def strip_c2pa_boxes(data: bytes) -> tuple[bytes, int]:
|
||||
"""Return ``(cleaned_bytes, stripped_count)`` with AI-provenance boxes removed.
|
||||
|
||||
Walks top-level boxes and drops:
|
||||
- any ``uuid`` box whose UUID equals ``C2PA_UUID`` (a C2PA manifest);
|
||||
- any ``uuid`` box whose payload carries an AI-label marker (an XMP packet
|
||||
with a TC260 / IPTC / IPTC-2025.1 AI field -- caught by content, not by the
|
||||
XMP UUID, so it works regardless of the UUID's byte order, and leaves plain
|
||||
non-AI XMP intact);
|
||||
- any ``jumb`` box (JPEG-XL JUMBF container).
|
||||
|
||||
All other boxes (incl. ``mdat`` / codestream) are emitted verbatim, so pixel
|
||||
and audio data is preserved bit-for-bit. Non-ISOBMFF input is returned
|
||||
unchanged. Despite the name this also covers MP4/MOV/M4A video and audio
|
||||
(all ISOBMFF). NOTE: this drops only top-level boxes. AI metadata stored as an
|
||||
*item inside the ``meta`` box* (typical for AVIF/HEIF) is handled separately and
|
||||
in place (same length, no offset rewrite): AI-label XMP by
|
||||
:func:`blank_ai_xmp_packets`, and AI-generator tokens in an ``Exif`` item by
|
||||
:func:`blank_ai_exif_tokens`.
|
||||
"""
|
||||
if not is_isobmff(data):
|
||||
return data, 0
|
||||
|
||||
out = bytearray()
|
||||
stripped = 0
|
||||
consumed = 0
|
||||
for start, end, box_type, payload_off in _iter_top_level_boxes(data):
|
||||
consumed = end
|
||||
if box_type == b"uuid":
|
||||
# uuid boxes carry the 16-byte UUID immediately after the type.
|
||||
is_c2pa = payload_off + 16 <= end and data[payload_off : payload_off + 16] == C2PA_UUID
|
||||
has_ai_label = any(marker in data[payload_off:end] for marker in _AI_LABEL_MARKERS)
|
||||
if is_c2pa or has_ai_label:
|
||||
stripped += 1
|
||||
continue
|
||||
elif box_type == b"jumb":
|
||||
stripped += 1
|
||||
continue
|
||||
out.extend(data[start:end])
|
||||
|
||||
# Fail-safe: the walker returns early on a malformed box (bad size, or a box
|
||||
# that runs past EOF), so anything after it was never visited. Emitting `out`
|
||||
# would silently truncate the file from the bad box to EOF -- worse than not
|
||||
# stripping. If the walk did not consume the whole input, return it unchanged.
|
||||
if consumed != len(data):
|
||||
logger.warning(
|
||||
"ISOBMFF box walk stopped at offset %d of %d (malformed box); "
|
||||
"returning input unchanged to avoid truncation",
|
||||
consumed,
|
||||
len(data),
|
||||
)
|
||||
return data, 0
|
||||
|
||||
return bytes(out), stripped
|
||||
|
||||
|
||||
def blank_ai_xmp_packets(data: bytes) -> tuple[bytes, int]:
|
||||
"""Overwrite (with spaces, in place) any XMP packet carrying an AI-label
|
||||
marker; return ``(data, blanked_count)``.
|
||||
|
||||
HEIF/AVIF store XMP as a ``meta``-box ``mime`` item whose bytes live in
|
||||
``mdat`` / ``idat``, which ``strip_c2pa_boxes`` cannot remove without
|
||||
meta-box surgery (``iinf`` / ``iloc`` rewrite). Instead, the XMP packet is
|
||||
located by its ``<?xpacket begin ... end?>`` delimiters and, when it carries
|
||||
an AI-label marker (TC260 AIGC / IPTC / IPTC-2025.1), overwritten with spaces.
|
||||
Because the replacement is the **same length**, every box size and ``iloc``
|
||||
offset stays valid and the coded image data is untouched -- only the AI label
|
||||
content is destroyed. Packets without an AI marker (plain copyright / camera
|
||||
XMP) are left intact, mirroring the top-level XMP-``uuid`` content match.
|
||||
"""
|
||||
blanked = 0
|
||||
|
||||
def _scrub(match: re.Match[bytes]) -> bytes:
|
||||
nonlocal blanked
|
||||
packet = match.group()
|
||||
if any(marker in packet for marker in _AI_LABEL_MARKERS):
|
||||
blanked += 1
|
||||
return b" " * len(packet)
|
||||
return packet
|
||||
|
||||
return _XMP_PACKET_RE.sub(_scrub, data), blanked
|
||||
|
||||
|
||||
# EXIF TIFF byte-order headers: little-endian (II 0x2a 0x00) and big-endian
|
||||
# (MM 0x00 0x2a). A HEIF/AVIF ``Exif`` meta-box item stores its TIFF block in
|
||||
# ``mdat`` / ``idat``, so the block (and these headers) appear in the raw bytes.
|
||||
_TIFF_HEADERS: tuple[bytes, ...] = (b"II\x2a\x00", b"MM\x00\x2a")
|
||||
# How far past a TIFF header an EXIF block plausibly extends; bounds the slice we
|
||||
# hand to piexif and search within (EXIF blocks are small kilobyte-scale).
|
||||
_EXIF_WINDOW = 256 * 1024
|
||||
|
||||
|
||||
def blank_ai_exif_tokens(data: bytes) -> tuple[bytes, int]:
|
||||
"""Overwrite (with spaces, in place) any AI-generator token in an EXIF block
|
||||
stored as an ISOBMFF ``meta``-box ``Exif`` item; return ``(data, blanked_count)``.
|
||||
|
||||
HEIF/AVIF can carry EXIF as a ``meta``-box ``Exif`` item whose TIFF bytes live
|
||||
in ``mdat`` / ``idat`` -- out of reach of the top-level box stripper, and (when
|
||||
no pillow-heif plugin is installed) of the PIL EXIF reader too, so an AI
|
||||
``Software`` / ``Make`` / ``Artist`` / ``ImageDescription`` tag there survived
|
||||
``remove_ai_metadata`` (a documented gap). This locates EXIF TIFF blocks by
|
||||
their byte-order header, **validates each with piexif** (so a coincidental
|
||||
II/MM run in pixel data is ignored -- it will not parse as a TIFF IFD), and
|
||||
overwrites any AI value with spaces of the SAME length. Because the replacement
|
||||
is same-length, every box size and ``iloc`` offset stays valid and the coded image
|
||||
is untouched -- only the AI tag content is destroyed; camera/editor EXIF without an
|
||||
AI token is left intact. This mirrors ``metadata._scrub_ai_exif`` in what it removes
|
||||
-- generator tokens (``Software``/``Make``/``Artist``/``ImageDescription``), the
|
||||
China TC260 ``{"AIGC":{...}}`` block (``ImageDescription``/``UserComment``), and the
|
||||
xAI/Grok ``Signature:`` + UUID-``Artist`` pair -- since on the ISOBMFF path this is
|
||||
the ONLY EXIF scrubber (``_scrub_ai_exif`` never runs there), so without parity a
|
||||
HEIC/AVIF AIGC/xAI tag is detected but not removed.
|
||||
"""
|
||||
import piexif
|
||||
|
||||
# The AI-EXIF rule set is defined ONCE in metadata._ai_exif_targets and shared by both
|
||||
# EXIF scrubbers (the JPEG _scrub_ai_exif pops the tag; here we blank the value bytes),
|
||||
# so their coverage cannot drift. Imported lazily to avoid import-order coupling with
|
||||
# metadata (which imports this module); a deliberate cross-module use, not an API leak.
|
||||
from remove_ai_watermarks.metadata import _ai_exif_targets # pyright: ignore[reportPrivateUsage]
|
||||
|
||||
out = bytearray(data)
|
||||
blanked = 0
|
||||
for header in _TIFF_HEADERS:
|
||||
pos = data.find(header)
|
||||
while pos != -1:
|
||||
window = bytes(out[pos : pos + _EXIF_WINDOW])
|
||||
try:
|
||||
loaded: dict[str, Any] = piexif.load(window)
|
||||
except Exception:
|
||||
loaded = {}
|
||||
for _ifd_key, _tag, value, _name in _ai_exif_targets(loaded):
|
||||
# Blank the value bytes in place, within this EXIF block only.
|
||||
vpos = out.find(value, pos, pos + _EXIF_WINDOW)
|
||||
if vpos != -1:
|
||||
out[vpos : vpos + len(value)] = b" " * len(value)
|
||||
blanked += 1
|
||||
pos = data.find(header, pos + len(header))
|
||||
return bytes(out), blanked
|
||||
@@ -1,332 +0,0 @@
|
||||
"""Terminal progress animation and library output suppression.
|
||||
|
||||
This module provides two main capabilities for the CLI:
|
||||
|
||||
1. ``run_with_progress`` — a styled two-line terminal animation that
|
||||
displays a bouncing highlight bar, a braille spinner, elapsed time,
|
||||
and a live operation message while a background task executes.
|
||||
|
||||
2. ``silence_library_output`` — a wrapper that suppresses noisy log
|
||||
output produced by third-party ML libraries (transformers, diffusers,
|
||||
huggingface_hub, tqdm) so the user only sees our own progress messages.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import contextlib
|
||||
import io
|
||||
import os
|
||||
import sys
|
||||
import threading
|
||||
import time
|
||||
import warnings
|
||||
from typing import TYPE_CHECKING, Any
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from collections.abc import Callable
|
||||
|
||||
# ── ANSI color constants ────────────────────────────────────────────
|
||||
_CYAN = "\033[36m"
|
||||
_YELLOW = "\033[33m"
|
||||
_GREEN = "\033[32m"
|
||||
_DIM = "\033[2m"
|
||||
_BOLD = "\033[1m"
|
||||
_RESET = "\033[0m"
|
||||
|
||||
# Bar geometry
|
||||
_BAR_WIDTH = 32
|
||||
_HIGHLIGHT_WIDTH = 5
|
||||
|
||||
|
||||
def _no_color() -> bool:
|
||||
"""Respect the NO_COLOR convention (https://no-color.org/)."""
|
||||
return bool(os.environ.get("NO_COLOR"))
|
||||
|
||||
|
||||
def _truncate(text: str, max_len: int = 72) -> str:
|
||||
"""Shorten a string with an ellipsis if it exceeds *max_len*."""
|
||||
return text if len(text) <= max_len else text[: max_len - 1] + "…"
|
||||
|
||||
|
||||
def _build_bar(step: int) -> str:
|
||||
"""Build a flowing highlight bar that bounces across the width.
|
||||
|
||||
The highlight segment (5 chars wide) travels left→right→left
|
||||
continuously, giving the user a visual "working" signal.
|
||||
"""
|
||||
cycle = _BAR_WIDTH * 2 - 2
|
||||
pos = step % cycle
|
||||
if pos >= _BAR_WIDTH:
|
||||
pos = cycle - pos
|
||||
|
||||
hl_start = max(0, pos - _HIGHLIGHT_WIDTH // 2)
|
||||
hl_end = min(_BAR_WIDTH, pos + _HIGHLIGHT_WIDTH // 2 + 1)
|
||||
before = "━" * hl_start
|
||||
highlight = "━" * (hl_end - hl_start)
|
||||
after = "━" * (_BAR_WIDTH - hl_end)
|
||||
|
||||
if _no_color():
|
||||
return before + highlight + after
|
||||
return f"{_DIM}{before}{_RESET}{_BOLD}{_YELLOW}{highlight}{_RESET}{_DIM}{after}{_RESET}"
|
||||
|
||||
|
||||
def run_with_progress(
|
||||
task: Callable[[], Any],
|
||||
progress_state: dict[str, str] | None = None,
|
||||
) -> Any:
|
||||
"""Execute *task* in a background thread while showing a progress animation.
|
||||
|
||||
The animation renders two lines to ``sys.__stderr__``:
|
||||
|
||||
- **Line 1**: braille spinner + bouncing bar + elapsed seconds
|
||||
- **Line 2**: current operation message from *progress_state*
|
||||
|
||||
When the task finishes, a green "Completed" line replaces the animation.
|
||||
|
||||
Args:
|
||||
task: A zero-argument callable to run in the background.
|
||||
progress_state: Mutable dict whose ``"message"`` key is read
|
||||
by the animation loop to display the current operation.
|
||||
|
||||
Returns:
|
||||
Whatever *task* returns.
|
||||
|
||||
Raises:
|
||||
Any exception raised by *task* is re-raised after the animation
|
||||
is cleaned up.
|
||||
"""
|
||||
done = threading.Event()
|
||||
output_holder: dict[str, Any] = {"result": None, "error": None}
|
||||
|
||||
def worker() -> None:
|
||||
try:
|
||||
output_holder["result"] = task()
|
||||
except Exception as error: # pragma: no cover - passthrough
|
||||
output_holder["error"] = error
|
||||
finally:
|
||||
done.set()
|
||||
|
||||
thread = threading.Thread(target=worker, daemon=True)
|
||||
thread.start()
|
||||
|
||||
spinner_frames = "⠋⠙⠹⠸⠼⠴⠦⠧⠇⠏"
|
||||
idx = 0
|
||||
start_time = time.time()
|
||||
no_color = _no_color()
|
||||
|
||||
def _get_operation() -> str:
|
||||
if isinstance(progress_state, dict):
|
||||
return progress_state.get("message", "Processing...")
|
||||
return "Processing..."
|
||||
|
||||
# ── Animation loop ──────────────────────────────────────────────
|
||||
while not done.is_set():
|
||||
spinner = spinner_frames[idx % len(spinner_frames)]
|
||||
elapsed = int(time.time() - start_time)
|
||||
bar_str = _build_bar(idx)
|
||||
operation = _truncate(_get_operation())
|
||||
|
||||
if no_color:
|
||||
line1 = f" {spinner} Processing {bar_str} {elapsed:>3}s"
|
||||
line2 = f" ╰─ {operation}"
|
||||
else:
|
||||
line1 = f" {_CYAN}{spinner}{_RESET} Processing {bar_str} {_BOLD}{_YELLOW}{elapsed:>3}s{_RESET}"
|
||||
line2 = f" {_DIM}╰─ {operation}{_RESET}"
|
||||
|
||||
print(
|
||||
f"\r\033[2K{line1}\n\033[2K{line2}\033[1A\r",
|
||||
end="",
|
||||
flush=True,
|
||||
file=sys.__stderr__,
|
||||
)
|
||||
time.sleep(0.08)
|
||||
idx += 1
|
||||
|
||||
# ── Final "done" frame ──────────────────────────────────────────
|
||||
thread.join()
|
||||
total = int(time.time() - start_time)
|
||||
final_operation = _truncate(_get_operation())
|
||||
done_bar = "━" * _BAR_WIDTH
|
||||
|
||||
if no_color:
|
||||
final_line1 = f" ✓ Completed {done_bar} {total:>3}s"
|
||||
final_line2 = f" ╰─ {final_operation}"
|
||||
else:
|
||||
final_line1 = (
|
||||
f" {_GREEN}{_BOLD}✓{_RESET} {_GREEN}Completed{_RESET} "
|
||||
f"{_GREEN}{done_bar}{_RESET} {_BOLD}{_GREEN}{total:>3}s{_RESET}"
|
||||
)
|
||||
final_line2 = f" {_DIM}╰─ {final_operation}{_RESET}"
|
||||
|
||||
print(
|
||||
f"\r\033[2K{final_line1}\n\033[2K{final_line2}",
|
||||
file=sys.__stderr__,
|
||||
)
|
||||
|
||||
if output_holder["error"] is not None:
|
||||
raise output_holder["error"]
|
||||
|
||||
return output_holder["result"]
|
||||
|
||||
|
||||
def silence_library_output(
|
||||
run_func: Callable[[], Any],
|
||||
set_progress: Callable[[str], None] | None = None,
|
||||
) -> Callable[[], Any]:
|
||||
"""Return a wrapper that silences noisy ML library output.
|
||||
|
||||
The wrapper:
|
||||
|
||||
1. Disables HuggingFace Hub progress bars via env var.
|
||||
2. Sets ``transformers``, ``diffusers``, and ``huggingface_hub``
|
||||
loggers to *error* level.
|
||||
3. Redirects ``stdout`` and ``stderr`` to ``io.StringIO`` sinks so
|
||||
that stray ``tqdm`` bars and model-loading chatter are invisible.
|
||||
4. Suppresses all Python warnings during the call.
|
||||
|
||||
Args:
|
||||
run_func: The callable to execute silently.
|
||||
set_progress: Optional callback to report phase changes.
|
||||
|
||||
Returns:
|
||||
A zero-argument callable that, when invoked, runs *run_func*
|
||||
inside the silent context.
|
||||
"""
|
||||
|
||||
def wrapped() -> Any:
|
||||
if set_progress:
|
||||
set_progress("Configuring runtime and suppressing noisy logs...")
|
||||
|
||||
os.environ.setdefault("HF_HUB_DISABLE_PROGRESS_BARS", "1")
|
||||
|
||||
for _silence in (
|
||||
lambda: __import__("transformers").logging.set_verbosity_error(),
|
||||
lambda: _silence_diffusers(),
|
||||
lambda: __import__("huggingface_hub").logging.set_verbosity_error(),
|
||||
):
|
||||
with contextlib.suppress(Exception):
|
||||
_silence()
|
||||
|
||||
with warnings.catch_warnings():
|
||||
warnings.simplefilter("ignore")
|
||||
with contextlib.redirect_stdout(io.StringIO()), contextlib.redirect_stderr(io.StringIO()):
|
||||
if set_progress:
|
||||
set_progress("Executing watermark removal pipeline...")
|
||||
return run_func()
|
||||
|
||||
return wrapped
|
||||
|
||||
|
||||
def _silence_diffusers() -> None:
|
||||
"""Silence diffusers logging and progress bars."""
|
||||
from diffusers.utils import logging as diffusers_logging
|
||||
|
||||
diffusers_logging.set_verbosity_error()
|
||||
if hasattr(diffusers_logging, "disable_progress_bar"):
|
||||
diffusers_logging.disable_progress_bar()
|
||||
|
||||
|
||||
# ── Shared pipeline progress helpers ─────────────────────────────────
|
||||
|
||||
_DEFAULT_PRE_PHASES: list[tuple[int, str]] = [
|
||||
(0, "Encoding image with VAE encoder"),
|
||||
(3, "Mapping pixel data → latent space"),
|
||||
(7, "Injecting noise into latent representation"),
|
||||
(12, "Building denoiser schedule"),
|
||||
(18, "Starting reverse diffusion sampler"),
|
||||
(30, "Running first denoising iteration"),
|
||||
(50, "Still processing — this can take a while"),
|
||||
(90, "Pipeline running — may take a few minutes"),
|
||||
]
|
||||
|
||||
_DEFAULT_POST_PHASES: list[tuple[int, str]] = [
|
||||
(0, "Denoising complete · Running VAE decoder"),
|
||||
(2, "Decoding latent channels → RGB color space"),
|
||||
(5, "Reconstructing pixel grid from latents"),
|
||||
(10, "Applying color space conversion and normalization"),
|
||||
(18, "Finalizing pixel output"),
|
||||
(30, "Still decoding — large images take longer"),
|
||||
(60, "Almost done — large images take longer to decode"),
|
||||
]
|
||||
|
||||
|
||||
def make_pipeline_progress(
|
||||
effective_steps: int,
|
||||
device: str,
|
||||
set_progress: Callable[[str], None],
|
||||
*,
|
||||
bar_len: int = 20,
|
||||
label: str = "Denoising",
|
||||
pre_phases: list[tuple[int, str]] | None = None,
|
||||
post_phases: list[tuple[int, str]] | None = None,
|
||||
) -> tuple[Callable[..., None], threading.Event, threading.Event, Callable[[], threading.Thread]]:
|
||||
"""Create step callback and background updater for a diffusion pipeline.
|
||||
|
||||
Returns:
|
||||
(step_callback, first_step_event, pipeline_done_event, start_updater)
|
||||
where ``start_updater()`` launches and returns the background thread.
|
||||
"""
|
||||
pre = pre_phases or [(s, f"{m} on {device}") for s, m in _DEFAULT_PRE_PHASES]
|
||||
post = post_phases or [(s, f"{m} on {device}") for s, m in _DEFAULT_POST_PHASES]
|
||||
|
||||
t0_holder: list[float] = [time.monotonic()]
|
||||
first_step = threading.Event()
|
||||
pipeline_done = threading.Event()
|
||||
last_cb_time: list[float] = [t0_holder[0]]
|
||||
|
||||
def _background_updater() -> None:
|
||||
idx = 0
|
||||
while not first_step.is_set():
|
||||
elapsed = time.monotonic() - t0_holder[0]
|
||||
while idx < len(pre) - 1 and elapsed >= pre[idx + 1][0]:
|
||||
idx += 1
|
||||
set_progress(pre[idx][1])
|
||||
first_step.wait(timeout=0.4)
|
||||
|
||||
idx = 0
|
||||
post_start: float | None = None
|
||||
while not pipeline_done.is_set():
|
||||
since_cb = time.monotonic() - last_cb_time[0]
|
||||
if since_cb >= 1.5:
|
||||
if post_start is None:
|
||||
post_start = time.monotonic()
|
||||
elapsed = time.monotonic() - post_start
|
||||
while idx < len(post) - 1 and elapsed >= post[idx + 1][0]:
|
||||
idx += 1
|
||||
set_progress(post[idx][1])
|
||||
else:
|
||||
post_start = None
|
||||
idx = 0
|
||||
pipeline_done.wait(timeout=0.4)
|
||||
|
||||
def step_callback(step: int, timestep: int, latents: Any) -> None:
|
||||
first_step.set()
|
||||
last_cb_time[0] = time.monotonic()
|
||||
elapsed = time.monotonic() - t0_holder[0]
|
||||
current = step + 1
|
||||
per_step = elapsed / max(1, current)
|
||||
remaining = per_step * max(0, effective_steps - current)
|
||||
filled = int(bar_len * current / max(1, effective_steps))
|
||||
bar = "█" * filled + "░" * (bar_len - filled)
|
||||
set_progress(
|
||||
f"{label} [{bar}] {current}/{effective_steps} | {elapsed:.0f}s elapsed, ~{remaining:.0f}s left | {device}"
|
||||
)
|
||||
|
||||
def start_updater() -> threading.Thread:
|
||||
t0_holder[0] = time.monotonic()
|
||||
last_cb_time[0] = t0_holder[0]
|
||||
first_step.clear()
|
||||
pipeline_done.clear()
|
||||
t = threading.Thread(target=_background_updater, daemon=True)
|
||||
t.start()
|
||||
return t
|
||||
|
||||
return step_callback, first_step, pipeline_done, start_updater
|
||||
|
||||
|
||||
# ── MPS fallback helper ──────────────────────────────────────────────
|
||||
|
||||
|
||||
def is_mps_error(error: Exception) -> bool:
|
||||
"""Check whether an exception is an MPS-related runtime error."""
|
||||
return "mps" in str(error).lower()
|
||||
@@ -1,43 +0,0 @@
|
||||
"""Low-level utility helpers used across the metadata pipeline.
|
||||
|
||||
Kept deliberately small — only format detection lives here so that
|
||||
higher-level modules can import without circular dependencies.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import TYPE_CHECKING
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from pathlib import Path
|
||||
|
||||
from remove_ai_watermarks.noai.constants import SUPPORTED_FORMATS
|
||||
|
||||
|
||||
def is_supported_format(file_path: Path) -> bool:
|
||||
"""
|
||||
Check if the file format is supported.
|
||||
|
||||
Args:
|
||||
file_path: Path to the image file.
|
||||
|
||||
Returns:
|
||||
True if the format is supported, False otherwise.
|
||||
"""
|
||||
return file_path.suffix.lower() in SUPPORTED_FORMATS
|
||||
|
||||
|
||||
def get_image_format(file_path: Path) -> str:
|
||||
"""
|
||||
Get the image format from file path.
|
||||
|
||||
Args:
|
||||
file_path: Path to the image file.
|
||||
|
||||
Returns:
|
||||
Format string (PNG, JPEG, etc.).
|
||||
"""
|
||||
suffix = file_path.suffix.lower()
|
||||
if suffix in {".jpg", ".jpeg"}:
|
||||
return "JPEG"
|
||||
return "PNG"
|
||||
@@ -1,207 +0,0 @@
|
||||
"""Watermark removal model profiles and the default strength.
|
||||
|
||||
Pure configuration and lookup functions with no ML dependencies.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import math
|
||||
from typing import TYPE_CHECKING, Literal
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from pathlib import Path
|
||||
|
||||
DEFAULT_MODEL_ID = "stabilityai/stable-diffusion-xl-base-1.0"
|
||||
|
||||
# Qwen-Image (20B MMDiT, Apache-2.0 code AND weights) base for the ``qwen`` pipeline:
|
||||
# an img2img alternative to SDXL with native text rendering (incl. CJK). Loaded only
|
||||
# when ``--pipeline qwen`` is selected; CUDA/cloud-class (does not fit MPS). CERTIFIED
|
||||
# oracle floors (2026-06-20): OpenAI **0.10** (seed-robust -- clean on seeds 0-4) and
|
||||
# Google/Gemini **0.25** (seed 0 verified on 2 images; pin a seed in prod, the Gemini
|
||||
# oracle rate-limits volume seed-repeat). The Gemini floor (0.25) is HIGHER than the
|
||||
# certified controlnet Gemini floor (0.15); ``resolve_strength(..., pipeline="qwen")``
|
||||
# now carries this via ``_QWEN_VENDOR_STRENGTH`` (below), so ``--pipeline qwen`` gets the
|
||||
# right floor automatically -- the old manual "pass --strength 0.25 for Gemini on qwen"
|
||||
# workaround is retired.
|
||||
# (Dispatch uses the bare "qwen" literal, matching the sdxl/controlnet sites, so there
|
||||
# is no QWEN_PROFILE constant -- only the model id is referenced from code.)
|
||||
QWEN_MODEL_ID = "Qwen/Qwen-Image"
|
||||
|
||||
# Canonical pipeline-profile names + the back-compat alias. The plain SDXL img2img
|
||||
# profile is ``sdxl``; ``default`` is kept as an accepted alias (it was the profile's
|
||||
# name before ``controlnet`` became the default-selected pipeline, 2026-06-09).
|
||||
SDXL_PROFILE = "sdxl"
|
||||
QWEN_ZIMAGE_PROFILE = "qwen-zimage"
|
||||
_PROFILE_ALIASES = {
|
||||
"default": SDXL_PROFILE,
|
||||
"qwen_zimage": QWEN_ZIMAGE_PROFILE,
|
||||
}
|
||||
|
||||
|
||||
def normalize_profile(profile: str) -> str:
|
||||
"""Canonicalize a pipeline-profile name, resolving the ``default`` -> ``sdxl`` alias."""
|
||||
normalized = profile.strip().lower()
|
||||
return _PROFILE_ALIASES.get(normalized, normalized)
|
||||
|
||||
|
||||
def resolve_steps(num_inference_steps: int | None, pipeline: str) -> int:
|
||||
"""Resolve a profile-specific step default while preserving explicit values.
|
||||
|
||||
The Lightning LoRA in ``qwen-zimage`` is distilled for four steps. Existing
|
||||
SDXL and Qwen profiles keep the long-standing 50-step CLI default.
|
||||
"""
|
||||
if num_inference_steps is not None:
|
||||
return num_inference_steps
|
||||
return 4 if normalize_profile(pipeline) == QWEN_ZIMAGE_PROFILE else 50
|
||||
|
||||
|
||||
def resolve_seed(seed: int | None, pipeline: str) -> int | None:
|
||||
"""Keep the oracle-verified qwen-zimage profile deterministic by default."""
|
||||
if seed is not None:
|
||||
return seed
|
||||
return 0 if normalize_profile(pipeline) == QWEN_ZIMAGE_PROFILE else None
|
||||
|
||||
|
||||
# The SDXL-native canny ControlNet used by the ``controlnet`` pipeline. The
|
||||
# ControlNet is an add-on to the SDXL base checkpoint (DEFAULT_MODEL_ID), not a
|
||||
# separate base model, so both the ``sdxl`` and ``controlnet`` profiles load the
|
||||
# same base weights and share the same vendor-adaptive strength ladder (see below).
|
||||
CONTROLNET_CANNY_MODEL = "xinsir/controlnet-canny-sdxl-1.0"
|
||||
|
||||
# Vendor-adaptive default denoising strength for the SDXL img2img scrub, overridable
|
||||
# from the CLI (`--strength`). The right strength depends on which vendor's SynthID is
|
||||
# present (detected from the C2PA issuer, metadata.synthid_source). The SAME ladder
|
||||
# applies to BOTH pipelines (`sdxl` plain img2img and `controlnet`) -- see "why one
|
||||
# ladder" below.
|
||||
#
|
||||
# Data basis (see docs/synthid.md sections 2.2 / 5.5): ORACLE-CERTIFIED controlnet floors.
|
||||
# Oracle re-testing
|
||||
# LOWERED the ladder back to OpenAI 0.10 / Google 0.15: each output verified on its own
|
||||
# oracle (openai.com/verify for OpenAI, the Google Gemini app for Google), all clean ->
|
||||
# - OpenAI 0.10: 2 photoreal images (1402 / 1448 px), SynthID not found on either.
|
||||
# - Google 0.15: 2 NATIVE-resolution images (both 2816x1536), SynthID not found on
|
||||
# either -- this directly retires the earlier "native ~2816 likely needs ~0.35+"
|
||||
# guess, which was speculative and never oracle-checked at that resolution.
|
||||
# This supersedes the 2026-06-04 cert (OpenAI 0.20 / Google 0.30), whose higher floor a
|
||||
# pixel-fidelity sweep showed was ~2x the removal floor and over-regenerated for no
|
||||
# efficacy gain (Google MAE -20% at 0.15 vs 0.30, no SynthID returning). Unknown vendor
|
||||
# tracks the Google (more robust watermark) value -> 0.15, still safe-by-default and the
|
||||
# floor that real (no-vendor) photos hit, so it also minimizes damage when there is in
|
||||
# fact nothing to remove. CAVEAT: the re-test is n=2 per vendor on photoreal / landscape
|
||||
# content; FLAT-GRAPHIC hard cases (the historical `sdxl` weak spot) were NOT in the
|
||||
# sample, so if an oracle still reads SynthID on a flat output, raise `--strength`.
|
||||
#
|
||||
# Why ONE ladder for both pipelines (2026-06-09): the certification was run on
|
||||
# controlnet, and it does NOT transfer to `sdxl` by symmetry -- the two pipelines have
|
||||
# OPPOSITE hard cases (controlnet leaves SynthID on photoreal, `sdxl` leaves it on flat
|
||||
# graphics; the content-x-pipeline table in docs/synthid.md §5.1). BUT on its OWN hard
|
||||
# case (flat fills) `sdxl` is the WEAKER remover -- plain img2img at low strength barely
|
||||
# perturbs a flat region -- so it needs AT LEAST as much strength as controlnet, not
|
||||
# less. Hence the certified controlnet floor is the right floor for `sdxl` too. The
|
||||
# higher strength costs little quality where it matters. `controlnet` is now the default
|
||||
# pipeline and `sdxl` is reached only through an explicit `--pipeline sdxl`. NOTE:
|
||||
# this is a MARGIN argument for `sdxl`, not a fresh certification -- there is no local
|
||||
# SynthID detector, so if an oracle still reads SynthID on a flat `sdxl` output, raise
|
||||
# `--strength`.
|
||||
OPENAI_STRENGTH = 0.10
|
||||
GEMINI_STRENGTH = 0.15
|
||||
UNKNOWN_STRENGTH = 0.15
|
||||
# Backwards-compatible alias: the vendor-unknown value (what a caller gets without a
|
||||
# detected vendor). Kept as DEFAULT_STRENGTH for existing references.
|
||||
DEFAULT_STRENGTH = UNKNOWN_STRENGTH
|
||||
|
||||
# Detected-vendor -> default strength. Vendor strings come from `vendor_for_strength`.
|
||||
_VENDOR_STRENGTH = {"openai": OPENAI_STRENGTH, "google": GEMINI_STRENGTH}
|
||||
|
||||
# Qwen has its OWN certified floors (Modal A100-80GB, 2026-06-20), DIFFERENT from the
|
||||
# SDXL ladder above: OpenAI 0.10 (seed-robust), Gemini 0.25 (HIGHER than controlnet's
|
||||
# 0.15 -- the 20B MMDiT perturbs less per denoising step, so it needs more strength to
|
||||
# clear Gemini SynthID). Unknown vendor tracks the higher (Gemini) value, safe-by-default.
|
||||
# `resolve_strength(..., pipeline="qwen")` uses this table so `--pipeline qwen` carries the
|
||||
# right floor automatically -- retiring the old manual "pass --strength 0.25 for Gemini on
|
||||
# qwen" workaround.
|
||||
QWEN_OPENAI_STRENGTH = 0.10
|
||||
QWEN_GEMINI_STRENGTH = 0.25
|
||||
QWEN_UNKNOWN_STRENGTH = 0.25
|
||||
_QWEN_VENDOR_STRENGTH = {"openai": QWEN_OPENAI_STRENGTH, "google": QWEN_GEMINI_STRENGTH}
|
||||
|
||||
|
||||
def strength_default_help() -> str:
|
||||
"""One-line description of the vendor-adaptive default, derived from the constants.
|
||||
|
||||
Single source of truth for the CLI ``--strength`` help so the numbers can never
|
||||
drift from the actual ladder (they did once when the per-pipeline split was unified).
|
||||
"""
|
||||
return (
|
||||
f"vendor-adaptive (OpenAI {OPENAI_STRENGTH} / Google {GEMINI_STRENGTH} / "
|
||||
f"unknown {UNKNOWN_STRENGTH}, from the C2PA issuer; qwen-zimage instead uses "
|
||||
"resolution-adaptive denoise)"
|
||||
)
|
||||
|
||||
|
||||
def resolve_strength(strength: float | None, vendor: str | None = None, pipeline: str | None = None) -> float:
|
||||
"""Resolve the denoising strength, applying the vendor default when unset.
|
||||
|
||||
``None`` means "the user did not pass ``--strength``", which resolves
|
||||
**vendor-adaptively**: ``vendor`` (``"openai"`` / ``"google"`` / None, from
|
||||
``vendor_for_strength``) selects the per-vendor floor. The ``sdxl`` and ``controlnet``
|
||||
pipelines share ONE ladder (``OPENAI_STRENGTH`` / ``GEMINI_STRENGTH`` /
|
||||
``UNKNOWN_STRENGTH`` -- see the module comment for why); ``qwen`` has its OWN higher
|
||||
ladder (``_QWEN_VENDOR_STRENGTH``, Gemini 0.25 vs controlnet 0.15), selected when
|
||||
``pipeline`` normalizes to ``"qwen"``. An explicit value always wins (including
|
||||
``0.0`` -- the check is ``is None``, not falsiness). Shared by the CLI (for display)
|
||||
and the engine (for execution) so the two never disagree -- both must pass the SAME
|
||||
``vendor`` and ``pipeline``.
|
||||
"""
|
||||
if strength is not None:
|
||||
return strength
|
||||
if pipeline is not None and normalize_profile(pipeline) == "qwen":
|
||||
return _QWEN_VENDOR_STRENGTH.get(vendor or "", QWEN_UNKNOWN_STRENGTH)
|
||||
return _VENDOR_STRENGTH.get(vendor or "", UNKNOWN_STRENGTH)
|
||||
|
||||
|
||||
def viable_steps(num_inference_steps: int, strength: float) -> int:
|
||||
"""The smallest step count >= ``num_inference_steps`` that actually denoises.
|
||||
|
||||
diffusers derives its img2img timesteps as ``int(steps * strength)``. When that
|
||||
rounds to ZERO the pipeline builds an empty latent and dies deep inside attention
|
||||
with ``cannot reshape tensor of 0 elements into shape [0, -1, 1, 512]`` -- an opaque
|
||||
torch error for what is really "these two options cannot work together".
|
||||
|
||||
The combination is reachable with entirely valid CLI arguments: at the default
|
||||
strength 0.15 every ``--steps`` below 7 crashed, and nothing told the user that
|
||||
``--steps`` and ``--strength`` interact (found by the release smoke matrix,
|
||||
2026-07-19). Raising the count to the minimum that denoises keeps the caller's intent
|
||||
-- they asked for "few steps", not "zero" -- and the engine logs the adjustment.
|
||||
|
||||
A non-positive ``strength`` cannot denoise at any step count; return the caller's
|
||||
value unchanged rather than dividing by zero.
|
||||
"""
|
||||
if strength <= 0:
|
||||
return num_inference_steps
|
||||
if int(num_inference_steps * strength) >= 1:
|
||||
return num_inference_steps
|
||||
return math.ceil(1 / strength)
|
||||
|
||||
|
||||
def vendor_for_strength(image_path: Path) -> Literal["openai", "google"] | None:
|
||||
"""Detect the SynthID vendor for strength selection: ``"openai"`` / ``"google"`` / None.
|
||||
|
||||
Reads the C2PA SynthID proxy (``metadata.synthid_source``) on the ORIGINAL input,
|
||||
so it must run before any pass that strips metadata. When both issuers appear (a
|
||||
rare multi-sign anomaly) Google wins -- the more-robust watermark -> safer (higher)
|
||||
strength. Returns None when metadata is stripped or the issuer is neither vendor,
|
||||
which maps to ``UNKNOWN_STRENGTH``. Lazy-imports ``metadata`` to keep this module
|
||||
dependency-light.
|
||||
"""
|
||||
try:
|
||||
from remove_ai_watermarks.metadata import synthid_source
|
||||
|
||||
src = (synthid_source(image_path) or "").lower()
|
||||
except Exception: # metadata unreadable -> treat as unknown vendor
|
||||
return None
|
||||
if "google" in src:
|
||||
return "google"
|
||||
if "openai" in src:
|
||||
return "openai"
|
||||
return None
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,818 @@
|
||||
"""High-level video processing API.
|
||||
|
||||
The product path covers provenance identification, container-level AI metadata
|
||||
removal, temporally stabilized visible Sora, Veo, Seedance, Dola, Hailuo, and
|
||||
Kling removal, and an oracle-certified opt-in VAE profile for video SynthID.
|
||||
The visible pixel path reuses the image package's shared fill backends.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from dataclasses import dataclass
|
||||
from pathlib import Path
|
||||
from tempfile import TemporaryDirectory
|
||||
from typing import TYPE_CHECKING, Literal
|
||||
|
||||
from remove_ai_watermarks.video_synthid import (
|
||||
DEFAULT_VIDEO_SYNTHID_FPS,
|
||||
DEFAULT_VIDEO_SYNTHID_LONG_SIDE,
|
||||
DEFAULT_VIDEO_SYNTHID_NOISE_STD,
|
||||
DEFAULT_VIDEO_SYNTHID_VAE,
|
||||
)
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from remove_ai_watermarks.video_invisible import RegenerationMetrics, VideoVaeRuntime
|
||||
from remove_ai_watermarks.video_visible import VideoScan
|
||||
|
||||
VIDEO_EXTENSIONS: frozenset[str] = frozenset({".mp4", ".mov", ".m4v", ".webm", ".mkv", ".avi", ".flv"})
|
||||
VIDEO_VISIBLE_MARKS = ("sora", "veo", "seedance", "dola", "hailuo", "kling")
|
||||
_ISOBMFF_VIDEO_EXTENSIONS: frozenset[str] = frozenset({".mp4", ".mov", ".m4v"})
|
||||
_EBML_VIDEO_EXTENSIONS: frozenset[str] = frozenset({".webm", ".mkv"})
|
||||
_RIFF_VIDEO_EXTENSIONS: frozenset[str] = frozenset({".avi"})
|
||||
_FLV_VIDEO_EXTENSIONS: frozenset[str] = frozenset({".flv"})
|
||||
_REGENERATED_VIDEO_EXTENSIONS: frozenset[str] = _ISOBMFF_VIDEO_EXTENSIONS
|
||||
_EBML_MAGIC = b"\x1aE\xdf\xa3"
|
||||
|
||||
|
||||
def _require_video_runtime() -> None:
|
||||
"""Raise with the public install command when a video runtime is absent."""
|
||||
from remove_ai_watermarks.optional_deps import module_available
|
||||
|
||||
if not module_available("cv2", "numpy", "av"):
|
||||
raise RuntimeError("Video pixel processing requires remove-ai-watermarks[video]")
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class VideoMetadataReport:
|
||||
"""AI metadata found in one supported video container."""
|
||||
|
||||
source: Path
|
||||
has_ai_metadata: bool
|
||||
markers: dict[str, str]
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class VideoMetadataResult:
|
||||
"""Result of a verified video metadata-removal operation."""
|
||||
|
||||
source: Path
|
||||
output: Path
|
||||
detected: dict[str, str]
|
||||
remaining: dict[str, str]
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class VideoProvenanceReport:
|
||||
"""Locally verifiable provenance signals found in one video."""
|
||||
|
||||
source: Path
|
||||
is_ai_generated: Literal[True] | None
|
||||
confidence: Literal["high", "unknown"]
|
||||
platform: str | None
|
||||
visible_mark: str | None
|
||||
visible_detected_frames: int
|
||||
total_frames: int | None
|
||||
has_ai_metadata: bool
|
||||
metadata_markers: dict[str, str]
|
||||
caveats: tuple[str, ...]
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class VideoVisibleResult:
|
||||
"""Result of visible AI-watermark removal from a video."""
|
||||
|
||||
source: Path
|
||||
output: Path | None
|
||||
mark: str
|
||||
total_frames: int
|
||||
detected_frames: int
|
||||
removed_frames: int
|
||||
remaining_metadata: dict[str, str]
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class VideoInvisibleResult:
|
||||
"""Result of removing video SynthID through the oracle-certified VAE profile."""
|
||||
|
||||
source: Path
|
||||
output: Path
|
||||
noise_std: float
|
||||
metrics: RegenerationMetrics
|
||||
remaining_metadata: dict[str, str]
|
||||
|
||||
@property
|
||||
def total_frames(self) -> int:
|
||||
return self.metrics.frames
|
||||
|
||||
@property
|
||||
def fps(self) -> float:
|
||||
return self.metrics.fps
|
||||
|
||||
@property
|
||||
def width(self) -> int:
|
||||
return self.metrics.width
|
||||
|
||||
@property
|
||||
def height(self) -> int:
|
||||
return self.metrics.height
|
||||
|
||||
@property
|
||||
def psnr_db(self) -> float:
|
||||
return self.metrics.psnr_db
|
||||
|
||||
@property
|
||||
def temporal_residual_ratio(self) -> float:
|
||||
return self.metrics.temporal_residual_ratio
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class VideoAllResult:
|
||||
"""Result of the complete video-cleaning pipeline."""
|
||||
|
||||
source: Path
|
||||
output: Path
|
||||
visible_mark: str | None
|
||||
total_frames: int
|
||||
visible_detected_frames: int
|
||||
visible_removed_frames: int
|
||||
detected_metadata: dict[str, str]
|
||||
remaining_metadata: dict[str, str]
|
||||
invisible_removed: bool
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class VideoBatchItem:
|
||||
"""Outcome for one source in a video batch."""
|
||||
|
||||
source: Path
|
||||
output: Path | None
|
||||
mode: Literal["all", "visible", "metadata"]
|
||||
changed: bool
|
||||
visible_mark: str | None
|
||||
invisible_removed: bool
|
||||
error: str | None = None
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class VideoBatchResult:
|
||||
"""Aggregate outcome for a sequential video batch."""
|
||||
|
||||
directory: Path
|
||||
output_directory: Path
|
||||
items: tuple[VideoBatchItem, ...]
|
||||
|
||||
@property
|
||||
def processed(self) -> int:
|
||||
return sum(item.error is None for item in self.items)
|
||||
|
||||
@property
|
||||
def failed(self) -> int:
|
||||
return sum(item.error is not None for item in self.items)
|
||||
|
||||
@property
|
||||
def invisible_removed(self) -> int:
|
||||
return sum(item.invisible_removed for item in self.items)
|
||||
|
||||
|
||||
_VISIBLE_PLATFORM = {
|
||||
"sora": "OpenAI Sora",
|
||||
"veo": "Google Veo",
|
||||
"seedance": "ByteDance Seedance",
|
||||
"dola": "ByteDance Dola",
|
||||
"hailuo": "MiniMax Hailuo",
|
||||
"kling": "Kuaishou Kling",
|
||||
}
|
||||
|
||||
|
||||
def _video_source(source: str | Path) -> Path:
|
||||
path = Path(source)
|
||||
if not path.exists():
|
||||
raise FileNotFoundError(f"Video does not exist: {path}")
|
||||
if not path.is_file():
|
||||
raise ValueError(f"Video source must be a file: {path}")
|
||||
if path.suffix.lower() not in VIDEO_EXTENSIONS:
|
||||
supported = ", ".join(sorted(VIDEO_EXTENSIONS))
|
||||
raise ValueError(f"Unsupported video format {path.suffix or '<none>'}; expected one of: {supported}")
|
||||
with path.open("rb") as stream:
|
||||
head = stream.read(12)
|
||||
suffix = path.suffix.lower()
|
||||
matches_container = (
|
||||
(suffix in _ISOBMFF_VIDEO_EXTENSIONS and len(head) >= 8 and head[4:8] == b"ftyp")
|
||||
or (suffix in _EBML_VIDEO_EXTENSIONS and head.startswith(_EBML_MAGIC))
|
||||
or (suffix in _RIFF_VIDEO_EXTENSIONS and len(head) >= 12 and head[:4] == b"RIFF" and head[8:12] == b"AVI ")
|
||||
or (suffix in _FLV_VIDEO_EXTENSIONS and head.startswith(b"FLV"))
|
||||
)
|
||||
if not matches_container:
|
||||
raise ValueError(f"Video content does not match its {suffix} extension: {path}")
|
||||
return path
|
||||
|
||||
|
||||
def _video_output(
|
||||
source: Path,
|
||||
output: str | Path | None,
|
||||
*,
|
||||
operation: str = "metadata removal",
|
||||
) -> Path:
|
||||
path = Path(output) if output is not None else source.with_stem(source.stem + "_clean")
|
||||
if path.suffix.lower() != source.suffix.lower():
|
||||
raise ValueError(
|
||||
f"Video output container must match the source ({source.suffix}); "
|
||||
f"{operation} does not change containers to {path.suffix or '<none>'}"
|
||||
)
|
||||
if path.resolve() == source.resolve():
|
||||
raise ValueError(f"Video {operation} requires a distinct output path")
|
||||
return path
|
||||
|
||||
|
||||
def _visible_removal_plan(
|
||||
selected_mark: str,
|
||||
selected_scan: VideoScan,
|
||||
markers: dict[str, str],
|
||||
) -> tuple[list[tuple[int, int, int, int] | None], float, Literal["box", "veo"]]:
|
||||
"""Resolve one provider's stable frame regions and fill geometry."""
|
||||
from remove_ai_watermarks.video_visible import (
|
||||
has_bytedance_video_provenance,
|
||||
has_sora_provenance,
|
||||
has_veo_provenance,
|
||||
stabilize_dola_localizations,
|
||||
stabilize_hailuo_localizations,
|
||||
stabilize_kling_localizations,
|
||||
stabilize_seedance_localizations,
|
||||
stabilize_sora_localizations,
|
||||
stabilize_veo_localizations,
|
||||
)
|
||||
|
||||
if selected_mark == "sora":
|
||||
return (
|
||||
stabilize_sora_localizations(
|
||||
selected_scan.detections,
|
||||
provenance=has_sora_provenance(markers),
|
||||
),
|
||||
0.28,
|
||||
"box",
|
||||
)
|
||||
if selected_mark == "veo":
|
||||
return (
|
||||
stabilize_veo_localizations(
|
||||
selected_scan.detections,
|
||||
provenance=has_veo_provenance(markers),
|
||||
),
|
||||
0.18,
|
||||
"veo",
|
||||
)
|
||||
if selected_mark == "seedance":
|
||||
return (
|
||||
stabilize_seedance_localizations(
|
||||
selected_scan.detections,
|
||||
provenance=has_bytedance_video_provenance(markers),
|
||||
),
|
||||
0.0,
|
||||
"box",
|
||||
)
|
||||
if selected_mark == "dola":
|
||||
return (
|
||||
stabilize_dola_localizations(
|
||||
selected_scan.detections,
|
||||
provenance=has_bytedance_video_provenance(markers),
|
||||
),
|
||||
0.20,
|
||||
"box",
|
||||
)
|
||||
if selected_mark == "hailuo":
|
||||
return stabilize_hailuo_localizations(selected_scan.detections), 0.12, "box"
|
||||
return stabilize_kling_localizations(selected_scan.detections), 0.12, "box"
|
||||
|
||||
|
||||
def _select_stable_visible_mark(
|
||||
scans: dict[str, VideoScan],
|
||||
markers: dict[str, str],
|
||||
candidate_marks: tuple[str, ...],
|
||||
) -> (
|
||||
tuple[
|
||||
str,
|
||||
VideoScan,
|
||||
list[tuple[int, int, int, int] | None],
|
||||
float,
|
||||
Literal["box", "veo"],
|
||||
]
|
||||
| None
|
||||
):
|
||||
"""Select the first stable provider result in the public specificity order."""
|
||||
for candidate_mark in candidate_marks:
|
||||
candidate_scan = scans[candidate_mark]
|
||||
candidate_regions, candidate_padding, candidate_mask_style = _visible_removal_plan(
|
||||
candidate_mark,
|
||||
candidate_scan,
|
||||
markers,
|
||||
)
|
||||
if any(region is not None for region in candidate_regions):
|
||||
return (
|
||||
candidate_mark,
|
||||
candidate_scan,
|
||||
candidate_regions,
|
||||
candidate_padding,
|
||||
candidate_mask_style,
|
||||
)
|
||||
return None
|
||||
|
||||
|
||||
def _platform_from_video_metadata(markers: dict[str, str]) -> str | None:
|
||||
"""Map supported C2PA-derived marker text to its generating platform."""
|
||||
from remove_ai_watermarks._internal.constants import C2PA_AI_VENDORS
|
||||
|
||||
marker_text = "\n".join(markers.values()).casefold()
|
||||
if not marker_text:
|
||||
return None
|
||||
for vendor in C2PA_AI_VENDORS:
|
||||
if vendor.platform is not None and vendor.needle is not None and vendor.needle.casefold() in marker_text:
|
||||
return vendor.platform
|
||||
return None
|
||||
|
||||
|
||||
def inspect_video_metadata(source: str | Path) -> VideoMetadataReport:
|
||||
"""Inspect supported AI-provenance metadata in a video container."""
|
||||
from remove_ai_watermarks.metadata import get_ai_metadata
|
||||
|
||||
source_path = _video_source(source)
|
||||
markers = get_ai_metadata(source_path)
|
||||
return VideoMetadataReport(
|
||||
source=source_path,
|
||||
has_ai_metadata=bool(markers),
|
||||
markers=markers,
|
||||
)
|
||||
|
||||
|
||||
def identify_video(
|
||||
source: str | Path,
|
||||
*,
|
||||
check_visible: bool = True,
|
||||
) -> VideoProvenanceReport:
|
||||
"""Identify locally readable AI provenance and stable visible video marks.
|
||||
|
||||
A negative result is reported as unknown, never clean. Proprietary pixel
|
||||
watermarks such as video SynthID have no public local decoder.
|
||||
"""
|
||||
from remove_ai_watermarks.metadata import get_ai_metadata
|
||||
|
||||
source_path = _video_source(source)
|
||||
markers = get_ai_metadata(source_path)
|
||||
selected_mark: str | None = None
|
||||
detected_frames = 0
|
||||
total_frames: int | None = None
|
||||
|
||||
if check_visible:
|
||||
_require_video_runtime()
|
||||
from remove_ai_watermarks.video_visible import scan_video_marks
|
||||
|
||||
scans = scan_video_marks(
|
||||
source_path,
|
||||
VIDEO_VISIBLE_MARKS,
|
||||
collect_timestamps=False,
|
||||
)
|
||||
total_frames = len(scans[VIDEO_VISIBLE_MARKS[0]].detections)
|
||||
selected = _select_stable_visible_mark(scans, markers, VIDEO_VISIBLE_MARKS)
|
||||
if selected is not None:
|
||||
selected_mark, _scan, regions, _padding, _mask_style = selected
|
||||
detected_frames = sum(region is not None for region in regions)
|
||||
|
||||
has_signal = bool(markers) or selected_mark is not None
|
||||
caveats = ["No public local decoder can verify proprietary pixel watermarks such as video SynthID."]
|
||||
if not check_visible:
|
||||
caveats.append("Visible video-mark detection was skipped.")
|
||||
if not has_signal:
|
||||
caveats.append("No supported signal was found; absence is unknown, not proof that the video is clean.")
|
||||
return VideoProvenanceReport(
|
||||
source=source_path,
|
||||
is_ai_generated=True if has_signal else None,
|
||||
confidence="high" if has_signal else "unknown",
|
||||
platform=(
|
||||
_VISIBLE_PLATFORM.get(selected_mark)
|
||||
if selected_mark is not None
|
||||
else _platform_from_video_metadata(markers)
|
||||
),
|
||||
visible_mark=selected_mark,
|
||||
visible_detected_frames=detected_frames,
|
||||
total_frames=total_frames,
|
||||
has_ai_metadata=bool(markers),
|
||||
metadata_markers=markers,
|
||||
caveats=tuple(caveats),
|
||||
)
|
||||
|
||||
|
||||
def remove_video_metadata(
|
||||
source: str | Path,
|
||||
output: str | Path | None = None,
|
||||
*,
|
||||
keep_standard: bool = True,
|
||||
_detected_metadata: dict[str, str] | None = None,
|
||||
) -> VideoMetadataResult:
|
||||
"""Remove AI metadata without transcoding video or audio streams.
|
||||
|
||||
The default output is ``<source_stem>_clean<source_suffix>``. A separate
|
||||
output is required so the operation never overwrites the original file.
|
||||
"""
|
||||
from remove_ai_watermarks.metadata import get_ai_metadata, strip_and_verify
|
||||
|
||||
source_path = _video_source(source)
|
||||
output_path = _video_output(source_path, output)
|
||||
detected = get_ai_metadata(source_path) if _detected_metadata is None else _detected_metadata
|
||||
written, remaining = strip_and_verify(source_path, output_path, keep_standard=keep_standard)
|
||||
return VideoMetadataResult(
|
||||
source=source_path,
|
||||
output=written,
|
||||
detected=detected,
|
||||
remaining=remaining,
|
||||
)
|
||||
|
||||
|
||||
def remove_video_visible(
|
||||
source: str | Path,
|
||||
output: str | Path | None = None,
|
||||
*,
|
||||
mark: str = "auto",
|
||||
backend: str = "cv2",
|
||||
strip_metadata: bool = True,
|
||||
temporal_consistency: bool = True,
|
||||
_metadata_markers: dict[str, str] | None = None,
|
||||
) -> VideoVisibleResult:
|
||||
"""Remove a supported visible AI wordmark from a video.
|
||||
|
||||
``mark="auto"`` scans every supported provider in one decode pass and selects
|
||||
the first stable match in specificity order. Explicit marks are ``sora``,
|
||||
``veo``, ``seedance``, ``dola``, ``hailuo``, and ``kling``. The full
|
||||
sequence is scanned before pixels change, and only recurring candidates are
|
||||
accepted. Complete audio is copied without re-encoding; video is transcoded
|
||||
because the pixels change. ``temporal_consistency=True`` motion-aligns a
|
||||
safely covered prior fill after each image-backend pass; scene cuts and
|
||||
disjoint masks keep the independent current fill. Completed output is
|
||||
published atomically. When no stable mark is found, no output is written
|
||||
and ``output`` in the result is ``None``.
|
||||
"""
|
||||
_require_video_runtime()
|
||||
|
||||
from remove_ai_watermarks.metadata import get_ai_metadata
|
||||
from remove_ai_watermarks.video_visible import encode_clean_video, scan_video_marks
|
||||
from remove_ai_watermarks.watermark_registry import resolve_backend
|
||||
|
||||
if mark not in {"auto", *VIDEO_VISIBLE_MARKS}:
|
||||
raise ValueError("Unsupported visible video mark; expected auto, sora, veo, seedance, dola, hailuo, or kling")
|
||||
if backend not in {"auto", "cv2", "migan", "lama"}:
|
||||
raise ValueError("Unsupported fill backend; expected auto, cv2, migan, or lama")
|
||||
|
||||
source_path = _video_source(source)
|
||||
output_path = _video_output(source_path, output, operation="visible watermark removal")
|
||||
markers = get_ai_metadata(source_path) if _metadata_markers is None else _metadata_markers
|
||||
|
||||
candidate_marks = VIDEO_VISIBLE_MARKS if mark == "auto" else (mark,)
|
||||
scans = scan_video_marks(source_path, candidate_marks)
|
||||
selected = _select_stable_visible_mark(scans, markers, candidate_marks)
|
||||
if selected is None:
|
||||
scan = scans[candidate_marks[0]]
|
||||
return VideoVisibleResult(
|
||||
source=source_path,
|
||||
output=None,
|
||||
mark=mark,
|
||||
total_frames=len(scan.detections),
|
||||
detected_frames=0,
|
||||
removed_frames=0,
|
||||
remaining_metadata=markers if strip_metadata else {},
|
||||
)
|
||||
mark, scan, regions, padding_fraction, mask_style = selected
|
||||
detected_frames = sum(region is not None for region in regions)
|
||||
|
||||
# Validate optional model availability before ffmpeg creates or overwrites
|
||||
# the requested output.
|
||||
resolve_backend(backend) # type: ignore[arg-type]
|
||||
removed_frames = encode_clean_video(
|
||||
source_path,
|
||||
output_path,
|
||||
scan,
|
||||
regions,
|
||||
backend=backend, # type: ignore[arg-type]
|
||||
strip_metadata=strip_metadata,
|
||||
padding_fraction=padding_fraction,
|
||||
mask_style=mask_style,
|
||||
temporal_consistency=temporal_consistency,
|
||||
)
|
||||
remaining_metadata = get_ai_metadata(output_path) if strip_metadata else {}
|
||||
return VideoVisibleResult(
|
||||
source=source_path,
|
||||
output=output_path,
|
||||
mark=mark,
|
||||
total_frames=len(scan.detections),
|
||||
detected_frames=detected_frames,
|
||||
removed_frames=removed_frames,
|
||||
remaining_metadata=remaining_metadata,
|
||||
)
|
||||
|
||||
|
||||
def remove_video_all(
|
||||
source: str | Path,
|
||||
output: str | Path | None = None,
|
||||
*,
|
||||
mark: str = "auto",
|
||||
backend: str = "cv2",
|
||||
temporal_consistency: bool = True,
|
||||
include_invisible: bool = False,
|
||||
noise_std: float = DEFAULT_VIDEO_SYNTHID_NOISE_STD,
|
||||
long_side: int = DEFAULT_VIDEO_SYNTHID_LONG_SIDE,
|
||||
fps: float = DEFAULT_VIDEO_SYNTHID_FPS,
|
||||
batch_size: int = 4,
|
||||
seed: int = 0,
|
||||
model: str = DEFAULT_VIDEO_SYNTHID_VAE,
|
||||
device: str = "auto",
|
||||
_invisible_runtime: VideoVaeRuntime | None = None,
|
||||
) -> VideoAllResult:
|
||||
"""Run the complete video cleaning pipeline.
|
||||
|
||||
The default path removes a stable visible provider mark when present and
|
||||
always strips verified AI metadata. It writes a same-container passthrough
|
||||
when neither signal is present, giving product callers one predictable
|
||||
output contract. ``include_invisible=True`` additionally runs lossy VAE
|
||||
regeneration with the oracle-certified default profile.
|
||||
"""
|
||||
from remove_ai_watermarks.metadata import get_ai_metadata
|
||||
|
||||
source_path = _video_source(source)
|
||||
output_path = _video_output(source_path, output, operation="complete cleaning")
|
||||
if include_invisible and source_path.suffix.lower() not in _REGENERATED_VIDEO_EXTENSIONS:
|
||||
supported = ", ".join(sorted(_REGENERATED_VIDEO_EXTENSIONS))
|
||||
raise ValueError(f"Video SynthID regeneration requires one of: {supported}")
|
||||
_require_video_runtime()
|
||||
detected_metadata = get_ai_metadata(source_path)
|
||||
|
||||
with TemporaryDirectory(prefix=f".{source_path.stem}-video-all-", dir=source_path.parent) as temp_dir:
|
||||
visible_output = Path(temp_dir) / f"visible{source_path.suffix}" if include_invisible else output_path
|
||||
visible_result = remove_video_visible(
|
||||
source_path,
|
||||
visible_output,
|
||||
mark=mark,
|
||||
backend=backend,
|
||||
strip_metadata=True,
|
||||
temporal_consistency=temporal_consistency,
|
||||
_metadata_markers=detected_metadata,
|
||||
)
|
||||
current_source = visible_result.output or source_path
|
||||
|
||||
if include_invisible:
|
||||
invisible_result = remove_video_invisible(
|
||||
current_source,
|
||||
output_path,
|
||||
noise_std=noise_std,
|
||||
long_side=long_side,
|
||||
fps=fps,
|
||||
batch_size=batch_size,
|
||||
seed=seed,
|
||||
model=model,
|
||||
device=device,
|
||||
_runtime=_invisible_runtime,
|
||||
)
|
||||
remaining_metadata = invisible_result.remaining_metadata
|
||||
elif visible_result.output is None:
|
||||
metadata_result = remove_video_metadata(
|
||||
source_path,
|
||||
output_path,
|
||||
_detected_metadata=detected_metadata,
|
||||
)
|
||||
remaining_metadata = metadata_result.remaining
|
||||
else:
|
||||
remaining_metadata = visible_result.remaining_metadata
|
||||
|
||||
return VideoAllResult(
|
||||
source=source_path,
|
||||
output=output_path,
|
||||
visible_mark=visible_result.mark if visible_result.output is not None else None,
|
||||
total_frames=visible_result.total_frames,
|
||||
visible_detected_frames=visible_result.detected_frames,
|
||||
visible_removed_frames=visible_result.removed_frames,
|
||||
detected_metadata=detected_metadata,
|
||||
remaining_metadata=remaining_metadata,
|
||||
invisible_removed=include_invisible,
|
||||
)
|
||||
|
||||
|
||||
def remove_video_batch(
|
||||
directory: str | Path,
|
||||
output_directory: str | Path | None = None,
|
||||
*,
|
||||
mode: Literal["all", "visible", "metadata"] = "all",
|
||||
mark: str = "auto",
|
||||
backend: str = "cv2",
|
||||
temporal_consistency: bool = True,
|
||||
include_invisible: bool = False,
|
||||
noise_std: float = DEFAULT_VIDEO_SYNTHID_NOISE_STD,
|
||||
long_side: int = DEFAULT_VIDEO_SYNTHID_LONG_SIDE,
|
||||
fps: float = DEFAULT_VIDEO_SYNTHID_FPS,
|
||||
batch_size: int = 4,
|
||||
seed: int = 0,
|
||||
model: str = DEFAULT_VIDEO_SYNTHID_VAE,
|
||||
device: str = "auto",
|
||||
) -> VideoBatchResult:
|
||||
"""Process every supported video in one directory.
|
||||
|
||||
Files are processed sequentially so model and ffmpeg resource use stays
|
||||
bounded. Per-file failures are returned in ``items`` and do not discard
|
||||
successful outputs. Visible-only no-op files are copied byte-for-byte so the
|
||||
output directory remains complete.
|
||||
"""
|
||||
import shutil
|
||||
|
||||
from remove_ai_watermarks.video_encoding import atomic_video_output
|
||||
|
||||
directory_path = Path(directory)
|
||||
if not directory_path.exists():
|
||||
raise FileNotFoundError(f"Video directory does not exist: {directory_path}")
|
||||
if not directory_path.is_dir():
|
||||
raise ValueError(f"Video batch source must be a directory: {directory_path}")
|
||||
if mode not in {"all", "visible", "metadata"}:
|
||||
raise ValueError("Unsupported video batch mode; expected all, visible, or metadata")
|
||||
if mark not in {"auto", *VIDEO_VISIBLE_MARKS}:
|
||||
raise ValueError("Unsupported visible video mark; expected auto, sora, veo, seedance, dola, hailuo, or kling")
|
||||
if backend not in {"auto", "cv2", "migan", "lama"}:
|
||||
raise ValueError("Unsupported fill backend; expected auto, cv2, migan, or lama")
|
||||
if include_invisible and mode != "all":
|
||||
raise ValueError("The invisible video stage is available only in all mode")
|
||||
if mode != "metadata":
|
||||
_require_video_runtime()
|
||||
|
||||
output_path = (
|
||||
Path(output_directory)
|
||||
if output_directory is not None
|
||||
else directory_path.parent / f"{directory_path.name}_clean"
|
||||
)
|
||||
if output_path.resolve() == directory_path.resolve():
|
||||
raise ValueError("Video batch output directory must differ from the source directory")
|
||||
output_path.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
sources = tuple(
|
||||
path
|
||||
for path in sorted(directory_path.iterdir(), key=lambda candidate: candidate.name.lower())
|
||||
if path.is_file() and path.suffix.lower() in VIDEO_EXTENSIONS
|
||||
)
|
||||
items: list[VideoBatchItem] = []
|
||||
invisible_runtime: VideoVaeRuntime | None = None
|
||||
invisible_runtime_error: str | None = None
|
||||
for source_path in sources:
|
||||
item_output = output_path / source_path.name
|
||||
try:
|
||||
if mode == "all":
|
||||
if (
|
||||
include_invisible
|
||||
and invisible_runtime is None
|
||||
and source_path.suffix.lower() in _REGENERATED_VIDEO_EXTENSIONS
|
||||
):
|
||||
# Validate the container before paying the multi-GB model
|
||||
# load, then retain one runtime for the complete batch.
|
||||
_video_source(source_path)
|
||||
if invisible_runtime_error is not None:
|
||||
raise RuntimeError(invisible_runtime_error)
|
||||
from remove_ai_watermarks.video_invisible import load_video_vae_runtime
|
||||
|
||||
try:
|
||||
invisible_runtime = load_video_vae_runtime(model=model, device=device)
|
||||
except Exception as exc:
|
||||
invisible_runtime_error = str(exc)
|
||||
raise
|
||||
all_result = remove_video_all(
|
||||
source_path,
|
||||
item_output,
|
||||
mark=mark,
|
||||
backend=backend,
|
||||
temporal_consistency=temporal_consistency,
|
||||
include_invisible=include_invisible,
|
||||
noise_std=noise_std,
|
||||
long_side=long_side,
|
||||
fps=fps,
|
||||
batch_size=batch_size,
|
||||
seed=seed,
|
||||
model=model,
|
||||
device=device,
|
||||
_invisible_runtime=invisible_runtime,
|
||||
)
|
||||
if all_result.remaining_metadata:
|
||||
raise RuntimeError(
|
||||
f"{len(all_result.remaining_metadata)} AI metadata marker(s) survived the complete pipeline"
|
||||
)
|
||||
items.append(
|
||||
VideoBatchItem(
|
||||
source=source_path,
|
||||
output=all_result.output,
|
||||
mode=mode,
|
||||
changed=bool(
|
||||
all_result.visible_mark or all_result.detected_metadata or all_result.invisible_removed
|
||||
),
|
||||
visible_mark=all_result.visible_mark,
|
||||
invisible_removed=all_result.invisible_removed,
|
||||
)
|
||||
)
|
||||
elif mode == "visible":
|
||||
visible_result = remove_video_visible(
|
||||
source_path,
|
||||
item_output,
|
||||
mark=mark,
|
||||
backend=backend,
|
||||
strip_metadata=False,
|
||||
temporal_consistency=temporal_consistency,
|
||||
)
|
||||
if visible_result.output is None:
|
||||
with atomic_video_output(item_output) as temporary_output:
|
||||
shutil.copyfile(source_path, temporary_output)
|
||||
items.append(
|
||||
VideoBatchItem(
|
||||
source=source_path,
|
||||
output=item_output,
|
||||
mode=mode,
|
||||
changed=visible_result.output is not None,
|
||||
visible_mark=visible_result.mark if visible_result.output is not None else None,
|
||||
invisible_removed=False,
|
||||
)
|
||||
)
|
||||
else:
|
||||
metadata_result = remove_video_metadata(source_path, item_output)
|
||||
if metadata_result.remaining:
|
||||
raise RuntimeError(
|
||||
f"{len(metadata_result.remaining)} AI metadata marker(s) survived metadata removal"
|
||||
)
|
||||
items.append(
|
||||
VideoBatchItem(
|
||||
source=source_path,
|
||||
output=metadata_result.output,
|
||||
mode=mode,
|
||||
changed=bool(metadata_result.detected),
|
||||
visible_mark=None,
|
||||
invisible_removed=False,
|
||||
)
|
||||
)
|
||||
except Exception as exc:
|
||||
items.append(
|
||||
VideoBatchItem(
|
||||
source=source_path,
|
||||
output=None,
|
||||
mode=mode,
|
||||
changed=False,
|
||||
visible_mark=None,
|
||||
invisible_removed=False,
|
||||
error=str(exc),
|
||||
)
|
||||
)
|
||||
|
||||
return VideoBatchResult(
|
||||
directory=directory_path,
|
||||
output_directory=output_path,
|
||||
items=tuple(items),
|
||||
)
|
||||
|
||||
|
||||
def remove_video_invisible(
|
||||
source: str | Path,
|
||||
output: str | Path | None = None,
|
||||
*,
|
||||
noise_std: float = DEFAULT_VIDEO_SYNTHID_NOISE_STD,
|
||||
long_side: int = DEFAULT_VIDEO_SYNTHID_LONG_SIDE,
|
||||
fps: float = DEFAULT_VIDEO_SYNTHID_FPS,
|
||||
batch_size: int = 4,
|
||||
seed: int = 0,
|
||||
model: str = DEFAULT_VIDEO_SYNTHID_VAE,
|
||||
device: str = "auto",
|
||||
_runtime: VideoVaeRuntime | None = None,
|
||||
) -> VideoInvisibleResult:
|
||||
"""Remove video SynthID through the oracle-certified VAE profile.
|
||||
|
||||
The function also strips source metadata during the transcode. The default
|
||||
profile is provider-oracle certified; important outputs may still be
|
||||
rechecked with Google's verifier when the caller needs a per-file verdict.
|
||||
"""
|
||||
from remove_ai_watermarks.metadata import get_ai_metadata
|
||||
|
||||
source_path = _video_source(source)
|
||||
if source_path.suffix.lower() not in _REGENERATED_VIDEO_EXTENSIONS:
|
||||
supported = ", ".join(sorted(_REGENERATED_VIDEO_EXTENSIONS))
|
||||
raise ValueError(f"Video SynthID regeneration requires one of: {supported}")
|
||||
_require_video_runtime()
|
||||
from remove_ai_watermarks.video_invisible import regenerate_video_candidate
|
||||
|
||||
clean_output = Path(output) if output is not None else source_path.with_stem(source_path.stem + "_clean")
|
||||
output_path = _video_output(
|
||||
source_path,
|
||||
clean_output,
|
||||
operation="SynthID removal",
|
||||
)
|
||||
metrics = regenerate_video_candidate(
|
||||
source_path,
|
||||
output_path,
|
||||
noise_std=noise_std,
|
||||
long_side=long_side,
|
||||
fps=fps,
|
||||
batch_size=batch_size,
|
||||
seed=seed,
|
||||
model=model,
|
||||
device=device,
|
||||
runtime=_runtime,
|
||||
)
|
||||
return VideoInvisibleResult(
|
||||
source=source_path,
|
||||
output=output_path,
|
||||
noise_std=noise_std,
|
||||
metrics=metrics,
|
||||
remaining_metadata=get_ai_metadata(output_path),
|
||||
)
|
||||
@@ -0,0 +1,506 @@
|
||||
"""Shared ffmpeg frame-encoding helpers."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import logging
|
||||
import math
|
||||
import os
|
||||
import shutil
|
||||
import subprocess
|
||||
import tempfile
|
||||
from contextlib import contextmanager
|
||||
from dataclasses import dataclass
|
||||
from fractions import Fraction
|
||||
from pathlib import Path
|
||||
from typing import TYPE_CHECKING, cast
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from collections.abc import Generator, Sequence
|
||||
from typing import BinaryIO
|
||||
|
||||
log = logging.getLogger(__name__)
|
||||
|
||||
_FFMPEG_STDERR_LIMIT = 64 * 1024
|
||||
_FFMPEG_STDERR_TRUNCATION = b"\n...[ffmpeg stderr truncated]...\n"
|
||||
_PIXEL_FORMATS = frozenset({"yuv420p", "yuv422p", "yuv444p"})
|
||||
_PIXEL_FORMAT_ALIASES = {
|
||||
"yuvj420p": "yuv420p",
|
||||
"yuvj422p": "yuv422p",
|
||||
"yuvj444p": "yuv444p",
|
||||
}
|
||||
_COLOR_RANGES = frozenset({"tv", "pc"})
|
||||
_COLOR_SPACES = frozenset({"bt709", "fcc", "bt470bg", "smpte170m", "smpte240m"})
|
||||
_COLOR_TRANSFERS = frozenset(
|
||||
{
|
||||
"bt709",
|
||||
"gamma22",
|
||||
"gamma28",
|
||||
"smpte170m",
|
||||
"smpte240m",
|
||||
"linear",
|
||||
"log",
|
||||
"log_sqrt",
|
||||
"iec61966-2-4",
|
||||
"bt1361e",
|
||||
"iec61966-2-1",
|
||||
"bt2020-10",
|
||||
"bt2020-12",
|
||||
"smpte2084",
|
||||
"smpte428",
|
||||
"arib-std-b67",
|
||||
}
|
||||
)
|
||||
_COLOR_PRIMARIES = frozenset(
|
||||
{
|
||||
"bt709",
|
||||
"bt470m",
|
||||
"bt470bg",
|
||||
"smpte170m",
|
||||
"smpte240m",
|
||||
"film",
|
||||
"bt2020",
|
||||
"smpte428",
|
||||
"smpte431",
|
||||
"smpte432",
|
||||
"jedec-p22",
|
||||
"ebu3213",
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class VideoEncodeProfile:
|
||||
"""Source video properties that the raw-frame encoder can preserve."""
|
||||
|
||||
pixel_format: str = "yuv420p"
|
||||
color_range: str | None = None
|
||||
color_space: str | None = None
|
||||
color_transfer: str | None = None
|
||||
color_primaries: str | None = None
|
||||
time_base: str | None = None
|
||||
start_pts: int | None = None
|
||||
source_pixel_format: str | None = None
|
||||
component_depth: int | None = None
|
||||
|
||||
|
||||
@dataclass
|
||||
class _RawVideoEncoder:
|
||||
"""Running ffmpeg process with diagnostics redirected outside a pipe."""
|
||||
|
||||
process: subprocess.Popen[bytes]
|
||||
stdin: BinaryIO
|
||||
_stderr_buffer: BinaryIO
|
||||
|
||||
def poll(self) -> int | None:
|
||||
return self.process.poll()
|
||||
|
||||
def kill(self) -> None:
|
||||
self.process.kill()
|
||||
|
||||
def wait(self) -> int:
|
||||
return self.process.wait()
|
||||
|
||||
def collect_stderr(self) -> str:
|
||||
"""Return bounded ffmpeg diagnostics and release the temporary file."""
|
||||
if self._stderr_buffer.closed:
|
||||
raise RuntimeError("ffmpeg diagnostics have already been collected")
|
||||
try:
|
||||
return _read_bounded_stderr(self._stderr_buffer)
|
||||
finally:
|
||||
self._stderr_buffer.close()
|
||||
|
||||
def discard_stderr(self) -> None:
|
||||
"""Release the diagnostic buffer after an abort."""
|
||||
if self._stderr_buffer.closed:
|
||||
return
|
||||
self._stderr_buffer.close()
|
||||
|
||||
|
||||
def _read_bounded_stderr(buffer: BinaryIO) -> str:
|
||||
"""Read bounded head-and-tail diagnostics from a seekable binary stream."""
|
||||
buffer.seek(0, os.SEEK_END)
|
||||
size = buffer.tell()
|
||||
if size <= _FFMPEG_STDERR_LIMIT:
|
||||
buffer.seek(0)
|
||||
raw_stderr = buffer.read()
|
||||
else:
|
||||
payload_limit = _FFMPEG_STDERR_LIMIT - len(_FFMPEG_STDERR_TRUNCATION)
|
||||
head_size = payload_limit // 2
|
||||
tail_size = payload_limit - head_size
|
||||
buffer.seek(0)
|
||||
head = buffer.read(head_size)
|
||||
buffer.seek(-tail_size, os.SEEK_END)
|
||||
tail = buffer.read(tail_size)
|
||||
raw_stderr = head + _FFMPEG_STDERR_TRUNCATION + tail
|
||||
return raw_stderr.decode("utf-8", errors="replace")
|
||||
|
||||
|
||||
def _known_value(value: object, allowed: frozenset[str]) -> str | None:
|
||||
"""Return a supported ffmpeg enum value, otherwise omit it."""
|
||||
return value if isinstance(value, str) and value in allowed else None
|
||||
|
||||
|
||||
def _time_base(value: object) -> str | None:
|
||||
"""Normalize a positive ffprobe time base."""
|
||||
if not isinstance(value, str):
|
||||
return None
|
||||
try:
|
||||
fraction = Fraction(value)
|
||||
except (ValueError, ZeroDivisionError):
|
||||
return None
|
||||
if fraction <= 0:
|
||||
return None
|
||||
return f"{fraction.numerator}/{fraction.denominator}"
|
||||
|
||||
|
||||
def _pixel_component_depth(pixel_format: object, raw_bits: object) -> int | None:
|
||||
"""Return the largest component depth reported by ffprobe or PyAV."""
|
||||
depths: list[int] = []
|
||||
if isinstance(raw_bits, str) and raw_bits.isdigit():
|
||||
depths.append(int(raw_bits))
|
||||
if isinstance(pixel_format, str):
|
||||
try:
|
||||
import av
|
||||
|
||||
depths.extend(component.bits for component in av.VideoFormat(pixel_format).components)
|
||||
except (ImportError, ValueError):
|
||||
pass
|
||||
return max(depths) if depths else None
|
||||
|
||||
|
||||
def _run_ffprobe(
|
||||
source: Path,
|
||||
arguments: Sequence[str],
|
||||
*,
|
||||
purpose: str,
|
||||
output_format: str,
|
||||
) -> str | None:
|
||||
"""Run one ffprobe query with shared logging and failure handling."""
|
||||
ffprobe = shutil.which("ffprobe")
|
||||
if ffprobe is None:
|
||||
log.warning("ffprobe is unavailable; cannot inspect %s", purpose)
|
||||
return None
|
||||
command = [ffprobe, "-v", "error", *arguments, "-of", output_format, str(source)]
|
||||
result = subprocess.run( # noqa: S603
|
||||
command,
|
||||
capture_output=True,
|
||||
check=False,
|
||||
text=True,
|
||||
)
|
||||
log.info(
|
||||
"ffprobe %s: command=%s status=%s stdout=%s stderr=%s",
|
||||
purpose,
|
||||
command,
|
||||
result.returncode,
|
||||
result.stdout,
|
||||
result.stderr,
|
||||
)
|
||||
if result.returncode != 0:
|
||||
log.warning("ffprobe could not inspect %s for %s", purpose, source)
|
||||
return None
|
||||
return result.stdout
|
||||
|
||||
|
||||
def probe_video_encode_profile(source: Path) -> VideoEncodeProfile:
|
||||
"""Read source properties that survive the package's 8-bit BGR boundary."""
|
||||
raw_profile = _run_ffprobe(
|
||||
source,
|
||||
(
|
||||
"-select_streams",
|
||||
"v:0",
|
||||
"-show_entries",
|
||||
"stream=pix_fmt,bits_per_raw_sample,color_range,color_space,color_transfer,color_primaries,time_base,start_pts",
|
||||
),
|
||||
purpose="video profile",
|
||||
output_format="json",
|
||||
)
|
||||
if raw_profile is None:
|
||||
return VideoEncodeProfile()
|
||||
try:
|
||||
payload = json.loads(raw_profile)
|
||||
streams = payload.get("streams", [])
|
||||
stream = streams[0]
|
||||
except (AttributeError, IndexError, TypeError, json.JSONDecodeError):
|
||||
log.warning("ffprobe returned no usable video profile for %s; using yuv420p", source)
|
||||
return VideoEncodeProfile()
|
||||
|
||||
raw_pixel_format = stream.get("pix_fmt")
|
||||
pixel_format = _PIXEL_FORMAT_ALIASES.get(raw_pixel_format, raw_pixel_format)
|
||||
if pixel_format not in _PIXEL_FORMATS:
|
||||
pixel_format = "yuv420p"
|
||||
time_base = _time_base(stream.get("time_base"))
|
||||
raw_start_pts = stream.get("start_pts")
|
||||
start_pts = raw_start_pts if isinstance(raw_start_pts, int) and time_base is not None else None
|
||||
return VideoEncodeProfile(
|
||||
pixel_format=pixel_format,
|
||||
color_range=_known_value(stream.get("color_range"), _COLOR_RANGES),
|
||||
color_space=_known_value(stream.get("color_space"), _COLOR_SPACES),
|
||||
color_transfer=_known_value(stream.get("color_transfer"), _COLOR_TRANSFERS),
|
||||
color_primaries=_known_value(stream.get("color_primaries"), _COLOR_PRIMARIES),
|
||||
time_base=time_base,
|
||||
start_pts=start_pts,
|
||||
source_pixel_format=raw_pixel_format if isinstance(raw_pixel_format, str) else None,
|
||||
component_depth=_pixel_component_depth(raw_pixel_format, stream.get("bits_per_raw_sample")),
|
||||
)
|
||||
|
||||
|
||||
def probe_video_timestamps(source: Path) -> tuple[float, ...]:
|
||||
"""Read authoritative display timestamps for the first video stream."""
|
||||
raw_timestamps = _run_ffprobe(
|
||||
source,
|
||||
(
|
||||
"-select_streams",
|
||||
"v:0",
|
||||
"-show_frames",
|
||||
"-show_entries",
|
||||
"frame=best_effort_timestamp_time",
|
||||
),
|
||||
purpose="video timestamps",
|
||||
output_format="csv=p=0",
|
||||
)
|
||||
if raw_timestamps is None:
|
||||
return ()
|
||||
try:
|
||||
timestamps = tuple(float(line) for line in raw_timestamps.splitlines() if line)
|
||||
except ValueError:
|
||||
log.warning("ffprobe returned unusable frame timestamps for %s", source)
|
||||
return ()
|
||||
if not timestamps or not all(math.isfinite(timestamp) for timestamp in timestamps):
|
||||
log.warning("ffprobe returned no finite frame timestamps for %s", source)
|
||||
return ()
|
||||
return timestamps
|
||||
|
||||
|
||||
@contextmanager
|
||||
def _temporary_video_path(output: Path, *, prefix: str) -> Generator[Path]:
|
||||
"""Yield one sibling temporary path and remove it on exit."""
|
||||
output.parent.mkdir(parents=True, exist_ok=True)
|
||||
with tempfile.NamedTemporaryFile(
|
||||
prefix=prefix,
|
||||
suffix=output.suffix,
|
||||
dir=output.parent,
|
||||
delete=False,
|
||||
) as stream:
|
||||
temporary_output = Path(stream.name)
|
||||
try:
|
||||
yield temporary_output
|
||||
finally:
|
||||
temporary_output.unlink(missing_ok=True)
|
||||
|
||||
|
||||
@contextmanager
|
||||
def atomic_video_output(output: Path) -> Generator[Path]:
|
||||
"""Yield a sibling temporary path and publish it only after success."""
|
||||
with _temporary_video_path(output, prefix=f".{output.stem}-") as temporary_output:
|
||||
yield temporary_output
|
||||
os.replace(temporary_output, output)
|
||||
|
||||
|
||||
def _video_codec_args(suffix: str, *, crf: int, profile: VideoEncodeProfile) -> list[str]:
|
||||
if suffix == ".webm":
|
||||
return ["-c:v", "libvpx-vp9", "-crf", str(crf), "-b:v", "0"]
|
||||
args = ["-c:v", "libx264", "-preset", "medium", "-crf", str(crf)]
|
||||
x264_params: list[str] = []
|
||||
if profile.color_primaries == "bt709":
|
||||
x264_params.append("colorprim=bt709")
|
||||
if profile.color_transfer == "bt709":
|
||||
x264_params.append("transfer=bt709")
|
||||
if profile.color_space == "bt709":
|
||||
x264_params.append("colormatrix=bt709")
|
||||
if profile.color_range is not None:
|
||||
x264_params.append(f"range={'full' if profile.color_range == 'pc' else 'limited'}")
|
||||
if x264_params:
|
||||
args.extend(["-x264-params", ":".join(x264_params)])
|
||||
return args
|
||||
|
||||
|
||||
def _profile_args(profile: VideoEncodeProfile) -> list[str]:
|
||||
"""Build generic output options for source properties ffmpeg understands."""
|
||||
args = ["-pix_fmt", profile.pixel_format]
|
||||
for option, value in (
|
||||
("-color_range", profile.color_range),
|
||||
("-colorspace", profile.color_space),
|
||||
("-color_trc", profile.color_transfer),
|
||||
("-color_primaries", profile.color_primaries),
|
||||
):
|
||||
if value is not None:
|
||||
args.extend([option, value])
|
||||
if profile.time_base is not None:
|
||||
args.extend(["-enc_time_base:v", profile.time_base])
|
||||
return args
|
||||
|
||||
|
||||
def raw_video_command(
|
||||
output: Path,
|
||||
*,
|
||||
width: int,
|
||||
height: int,
|
||||
fps: float,
|
||||
crf: int,
|
||||
profile: VideoEncodeProfile,
|
||||
timestamped_input: bool = False,
|
||||
copy_input_timestamps: bool = False,
|
||||
) -> list[str]:
|
||||
"""Build an ffmpeg command for BGR frames on standard input."""
|
||||
ffmpeg = shutil.which("ffmpeg")
|
||||
if ffmpeg is None:
|
||||
raise RuntimeError("Video processing requires ffmpeg on PATH")
|
||||
frame_input = (
|
||||
["-f", "nut", "-i", "pipe:0"]
|
||||
if timestamped_input
|
||||
else [
|
||||
"-f",
|
||||
"rawvideo",
|
||||
"-pix_fmt",
|
||||
"bgr24",
|
||||
"-s:v",
|
||||
f"{width}x{height}",
|
||||
"-r",
|
||||
f"{fps:.12g}",
|
||||
"-i",
|
||||
"pipe:0",
|
||||
]
|
||||
)
|
||||
command = [
|
||||
ffmpeg,
|
||||
"-y",
|
||||
"-loglevel",
|
||||
"error",
|
||||
*(["-copyts"] if copy_input_timestamps else []),
|
||||
*frame_input,
|
||||
"-map",
|
||||
"0:v:0",
|
||||
*_video_codec_args(output.suffix.lower(), crf=crf, profile=profile),
|
||||
*_profile_args(profile),
|
||||
"-map_metadata",
|
||||
"-1",
|
||||
"-map_chapters",
|
||||
"-1",
|
||||
]
|
||||
command.extend(["-fps_mode", "passthrough"])
|
||||
if copy_input_timestamps:
|
||||
command.extend(["-avoid_negative_ts", "disabled"])
|
||||
if output.suffix.lower() in {".mp4", ".mov", ".m4v"} and profile.time_base is not None:
|
||||
numerator, denominator = (int(part) for part in profile.time_base.split("/", 1))
|
||||
if numerator == 1:
|
||||
command.extend(["-video_track_timescale", str(denominator)])
|
||||
command.append(str(output))
|
||||
return command
|
||||
|
||||
|
||||
def mux_encoded_video(
|
||||
encoded_video: Path,
|
||||
source: Path,
|
||||
output: Path,
|
||||
*,
|
||||
strip_metadata: bool,
|
||||
copy_input_timestamps: bool = False,
|
||||
) -> None:
|
||||
"""Copy encoded video and source audio into the final container."""
|
||||
ffmpeg = shutil.which("ffmpeg")
|
||||
if ffmpeg is None:
|
||||
raise RuntimeError("Video processing requires ffmpeg on PATH")
|
||||
command = [
|
||||
ffmpeg,
|
||||
"-y",
|
||||
"-loglevel",
|
||||
"error",
|
||||
*(["-copyts"] if copy_input_timestamps else []),
|
||||
"-i",
|
||||
str(encoded_video),
|
||||
"-i",
|
||||
str(source),
|
||||
"-map",
|
||||
"0:v:0",
|
||||
"-map",
|
||||
"1:a?",
|
||||
"-c",
|
||||
"copy",
|
||||
"-map_metadata",
|
||||
"-1" if strip_metadata else "1",
|
||||
"-map_chapters",
|
||||
"-1" if strip_metadata else "1",
|
||||
]
|
||||
if copy_input_timestamps:
|
||||
command.extend(["-avoid_negative_ts", "disabled"])
|
||||
if output.suffix.lower() in {".mp4", ".mov", ".m4v"}:
|
||||
command.extend(["-movflags", "+faststart"])
|
||||
command.append(str(output))
|
||||
with tempfile.TemporaryFile(mode="w+b") as stderr_buffer:
|
||||
result = subprocess.run( # noqa: S603
|
||||
command,
|
||||
stdout=subprocess.DEVNULL,
|
||||
stderr=stderr_buffer,
|
||||
check=False,
|
||||
)
|
||||
stderr = _read_bounded_stderr(stderr_buffer)
|
||||
log.info(
|
||||
"ffmpeg video mux: command=%s status=%s stderr=%s",
|
||||
command,
|
||||
result.returncode,
|
||||
stderr,
|
||||
)
|
||||
if result.returncode != 0:
|
||||
raise RuntimeError(f"ffmpeg failed to mux {output}: {stderr.strip()[:500]}")
|
||||
|
||||
|
||||
@contextmanager
|
||||
def staged_video_output(output: Path) -> Generator[tuple[Path, Path]]:
|
||||
"""Yield video-only and final temporary paths, then publish atomically."""
|
||||
with (
|
||||
atomic_video_output(output) as temporary_output,
|
||||
_temporary_video_path(
|
||||
output,
|
||||
prefix=f".{output.stem}-video-",
|
||||
) as encoded_video,
|
||||
):
|
||||
yield encoded_video, temporary_output
|
||||
|
||||
|
||||
def start_raw_video_encoder(command: list[str]) -> _RawVideoEncoder:
|
||||
"""Start ffmpeg and validate its raw-frame pipes."""
|
||||
log.info("Starting ffmpeg video encode: command=%s", command)
|
||||
stderr_buffer = cast(
|
||||
"BinaryIO",
|
||||
tempfile.TemporaryFile(mode="w+b"), # noqa: SIM115 - encoder owns the lifetime
|
||||
)
|
||||
try:
|
||||
process = subprocess.Popen( # noqa: S603
|
||||
command,
|
||||
stdin=subprocess.PIPE,
|
||||
stdout=subprocess.DEVNULL,
|
||||
stderr=stderr_buffer,
|
||||
)
|
||||
except Exception:
|
||||
stderr_buffer.close()
|
||||
raise
|
||||
if process.stdin is None:
|
||||
process.kill()
|
||||
process.wait()
|
||||
stderr_buffer.close()
|
||||
raise RuntimeError("Could not open ffmpeg input pipe")
|
||||
return _RawVideoEncoder(process, cast("BinaryIO", process.stdin), stderr_buffer)
|
||||
|
||||
|
||||
def finish_raw_video_encoder(
|
||||
process: _RawVideoEncoder,
|
||||
output: Path,
|
||||
*,
|
||||
operation: str,
|
||||
) -> None:
|
||||
"""Close the frame stream and raise when ffmpeg rejects the encode."""
|
||||
process.stdin.close()
|
||||
return_code = process.wait()
|
||||
stderr = process.collect_stderr()
|
||||
log.info("ffmpeg %s finished: status=%s stderr=%s", operation, return_code, stderr)
|
||||
if return_code != 0:
|
||||
raise RuntimeError(f"ffmpeg failed to encode {output}: {stderr.strip()[:500]}")
|
||||
|
||||
|
||||
def abort_raw_video_encoder(process: _RawVideoEncoder) -> None:
|
||||
"""Stop an incomplete ffmpeg encode."""
|
||||
if process.poll() is None:
|
||||
process.kill()
|
||||
process.wait()
|
||||
process.discard_stderr()
|
||||
@@ -0,0 +1,468 @@
|
||||
"""Oracle-certified VAE regeneration for video SynthID removal.
|
||||
|
||||
Google does not publish a local video SynthID decoder. The default profile is
|
||||
therefore certified against Google's matching content-verification flow and
|
||||
also reports local fidelity metrics.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
# torch/diffusers/cv2 expose incomplete types at this boundary. Pure helpers
|
||||
# remain annotated while third-party tensor and image calls are relaxed here.
|
||||
# pyright: reportUnknownMemberType=false, reportUnknownArgumentType=false, reportUnknownVariableType=false, reportUnknownParameterType=false, reportMissingTypeArgument=false, reportMissingTypeStubs=false, reportMissingImports=false, reportArgumentType=false, reportAssignmentType=false, reportReturnType=false, reportCallIssue=false, reportIndexIssue=false, reportOperatorIssue=false, reportOptionalMemberAccess=false, reportOptionalCall=false, reportOptionalSubscript=false, reportOptionalOperand=false, reportAttributeAccessIssue=false, reportPrivateImportUsage=false, reportPrivateUsage=false, reportInvalidTypeForm=false
|
||||
import logging
|
||||
import math
|
||||
from dataclasses import dataclass
|
||||
from importlib.util import find_spec
|
||||
from typing import TYPE_CHECKING, Any
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
|
||||
from remove_ai_watermarks.video_encoding import (
|
||||
abort_raw_video_encoder,
|
||||
finish_raw_video_encoder,
|
||||
mux_encoded_video,
|
||||
probe_video_encode_profile,
|
||||
raw_video_command,
|
||||
staged_video_output,
|
||||
start_raw_video_encoder,
|
||||
)
|
||||
from remove_ai_watermarks.video_synthid import (
|
||||
DEFAULT_VIDEO_SYNTHID_FPS,
|
||||
DEFAULT_VIDEO_SYNTHID_LONG_SIDE,
|
||||
DEFAULT_VIDEO_SYNTHID_NOISE_STD,
|
||||
DEFAULT_VIDEO_SYNTHID_VAE,
|
||||
VIDEO_SYNTHID_LATENT_MULTIPLE,
|
||||
)
|
||||
from remove_ai_watermarks.video_temporal import (
|
||||
_backward_map,
|
||||
_motion_residual,
|
||||
build_temporal_reference,
|
||||
temporal_residual_ratio,
|
||||
)
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from collections.abc import Iterable, Sequence
|
||||
from pathlib import Path
|
||||
|
||||
log = logging.getLogger(__name__)
|
||||
|
||||
__all__ = ["build_temporal_reference", "temporal_residual_ratio"]
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class RegenerationMetrics:
|
||||
"""Measured properties of one regenerated video."""
|
||||
|
||||
frames: int
|
||||
fps: float
|
||||
width: int
|
||||
height: int
|
||||
psnr_db: float
|
||||
temporal_residual_ratio: float
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class VideoVaeRuntime:
|
||||
"""Loaded VAE state reusable across multiple video regenerations."""
|
||||
|
||||
model: str
|
||||
requested_device: str
|
||||
resolved_device: str
|
||||
vae: Any
|
||||
|
||||
|
||||
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
|
||||
|
||||
|
||||
def _fit_size(width: int, height: int, long_side: int) -> tuple[int, int]:
|
||||
"""Fit dimensions to ``long_side`` while preserving aspect and VAE alignment."""
|
||||
if width <= 0 or height <= 0:
|
||||
raise ValueError("Video dimensions must be positive")
|
||||
if long_side < VIDEO_SYNTHID_LATENT_MULTIPLE:
|
||||
raise ValueError(f"Long side must be at least {VIDEO_SYNTHID_LATENT_MULTIPLE}")
|
||||
scale = long_side / max(width, height)
|
||||
fitted_width = max(
|
||||
VIDEO_SYNTHID_LATENT_MULTIPLE,
|
||||
round(width * scale) // VIDEO_SYNTHID_LATENT_MULTIPLE * VIDEO_SYNTHID_LATENT_MULTIPLE,
|
||||
)
|
||||
fitted_height = max(
|
||||
VIDEO_SYNTHID_LATENT_MULTIPLE,
|
||||
round(height * scale) // VIDEO_SYNTHID_LATENT_MULTIPLE * VIDEO_SYNTHID_LATENT_MULTIPLE,
|
||||
)
|
||||
return fitted_width, fitted_height
|
||||
|
||||
|
||||
def _pick_device(requested: str) -> str:
|
||||
import torch
|
||||
|
||||
if requested == "auto":
|
||||
if torch.cuda.is_available():
|
||||
return "cuda"
|
||||
if hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
|
||||
return "mps"
|
||||
return "cpu"
|
||||
if requested == "cuda" and not torch.cuda.is_available():
|
||||
raise RuntimeError("CUDA was requested but is not available")
|
||||
if requested == "mps" and not (hasattr(torch.backends, "mps") and torch.backends.mps.is_available()):
|
||||
raise RuntimeError("MPS was requested but is not available")
|
||||
return requested
|
||||
|
||||
|
||||
def load_video_vae_runtime(
|
||||
*,
|
||||
model: str = DEFAULT_VIDEO_SYNTHID_VAE,
|
||||
device: str = "auto",
|
||||
) -> VideoVaeRuntime:
|
||||
"""Load one reusable video VAE runtime."""
|
||||
if device not in {"auto", "cuda", "mps", "cpu"}:
|
||||
raise ValueError("device must be auto, cuda, mps, or cpu")
|
||||
if not is_available():
|
||||
raise RuntimeError("Video SynthID regeneration requires the diffusion extra")
|
||||
|
||||
import torch
|
||||
from diffusers import AutoencoderKL
|
||||
|
||||
resolved_device = _pick_device(device)
|
||||
dtype = torch.float16 if resolved_device == "cuda" else torch.float32
|
||||
log.info("Loading %s on %s", model, resolved_device)
|
||||
vae = AutoencoderKL.from_pretrained(model, torch_dtype=dtype).to(resolved_device)
|
||||
vae.eval()
|
||||
vae.enable_slicing()
|
||||
return VideoVaeRuntime(
|
||||
model=model,
|
||||
requested_device=device,
|
||||
resolved_device=resolved_device,
|
||||
vae=vae,
|
||||
)
|
||||
|
||||
|
||||
def _shared_latent_noise(
|
||||
spatial_shape: Sequence[int],
|
||||
*,
|
||||
seed: int,
|
||||
device: str,
|
||||
dtype: Any,
|
||||
) -> Any:
|
||||
"""Return one deterministic spatial noise field for reuse across time."""
|
||||
import torch
|
||||
|
||||
if len(spatial_shape) != 3 or any(size <= 0 for size in spatial_shape):
|
||||
raise ValueError("Expected a positive CHW latent shape")
|
||||
generator = torch.Generator(device="cpu").manual_seed(seed)
|
||||
noise = torch.randn((1, *spatial_shape), generator=generator, dtype=torch.float32)
|
||||
return noise.to(device=device, dtype=dtype)
|
||||
|
||||
|
||||
def paired_psnr(reference: np.ndarray, candidate: np.ndarray) -> float:
|
||||
"""Return paired PSNR over uint8 frame stacks."""
|
||||
if reference.shape != candidate.shape:
|
||||
raise ValueError("PSNR inputs must have matching shapes")
|
||||
mse = float(np.mean((reference.astype(np.float32) - candidate.astype(np.float32)) ** 2))
|
||||
if mse == 0.0:
|
||||
return math.inf
|
||||
return 20.0 * math.log10(255.0 / math.sqrt(mse))
|
||||
|
||||
|
||||
def _probe_video(source: Path) -> tuple[int, int, float]:
|
||||
capture = cv2.VideoCapture(str(source))
|
||||
if not capture.isOpened():
|
||||
raise ValueError(f"Could not open video: {source}")
|
||||
try:
|
||||
width = round(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
|
||||
height = round(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
|
||||
source_fps = float(capture.get(cv2.CAP_PROP_FPS))
|
||||
finally:
|
||||
capture.release()
|
||||
if width <= 0 or height <= 0:
|
||||
raise ValueError(f"Video has no usable dimensions: {source}")
|
||||
if source_fps <= 0.0:
|
||||
raise ValueError(f"Video has no usable frame rate: {source}")
|
||||
return width, height, source_fps
|
||||
|
||||
|
||||
def read_sampled_frames(
|
||||
source: Path,
|
||||
*,
|
||||
duration: float | None,
|
||||
output_fps: float,
|
||||
size: tuple[int, int],
|
||||
) -> tuple[list[np.ndarray], float]:
|
||||
"""Read uniformly sampled frames and resize them to the VAE geometry."""
|
||||
_width, _height, source_fps = _probe_video(source)
|
||||
effective_fps = min(output_fps, source_fps)
|
||||
frames = list(
|
||||
_iter_sampled_frames(
|
||||
source,
|
||||
source_fps=source_fps,
|
||||
duration=duration,
|
||||
effective_fps=effective_fps,
|
||||
size=size,
|
||||
)
|
||||
)
|
||||
if len(frames) < 2:
|
||||
raise ValueError("The selected clip produced fewer than two frames")
|
||||
return frames, effective_fps
|
||||
|
||||
|
||||
def _iter_sampled_frames(
|
||||
source: Path,
|
||||
*,
|
||||
source_fps: float,
|
||||
duration: float | None,
|
||||
effective_fps: float,
|
||||
size: tuple[int, int],
|
||||
) -> Iterable[np.ndarray]:
|
||||
"""Yield uniformly sampled frames without retaining the full video."""
|
||||
capture = cv2.VideoCapture(str(source))
|
||||
if not capture.isOpened():
|
||||
raise ValueError(f"Could not open video: {source}")
|
||||
sample_period = 1.0 / effective_fps
|
||||
next_sample_time = 0.0
|
||||
frame_index = 0
|
||||
try:
|
||||
while True:
|
||||
ok, frame = capture.read()
|
||||
if not ok:
|
||||
break
|
||||
timestamp = frame_index / source_fps
|
||||
if duration is not None and timestamp + 1e-9 >= duration:
|
||||
break
|
||||
if timestamp + 1e-9 >= next_sample_time:
|
||||
yield cv2.resize(frame, size, interpolation=cv2.INTER_LANCZOS4)
|
||||
next_sample_time += sample_period
|
||||
frame_index += 1
|
||||
finally:
|
||||
capture.release()
|
||||
|
||||
|
||||
def _frame_batches(frames: Sequence[np.ndarray], batch_size: int) -> Iterable[Sequence[np.ndarray]]:
|
||||
for start in range(0, len(frames), batch_size):
|
||||
yield frames[start : start + batch_size]
|
||||
|
||||
|
||||
def _stream_batches(frames: Iterable[np.ndarray], batch_size: int) -> Iterable[list[np.ndarray]]:
|
||||
batch: list[np.ndarray] = []
|
||||
for frame in frames:
|
||||
batch.append(frame)
|
||||
if len(batch) == batch_size:
|
||||
yield batch
|
||||
batch = []
|
||||
if batch:
|
||||
yield batch
|
||||
|
||||
|
||||
def _encode_frame_latents(
|
||||
frames: Sequence[np.ndarray],
|
||||
*,
|
||||
vae: Any,
|
||||
device: str,
|
||||
batch_size: int,
|
||||
) -> list[Any]:
|
||||
"""Encode source frames once so every candidate can reuse identical latents."""
|
||||
import torch
|
||||
|
||||
latent_batches: list[Any] = []
|
||||
scaling_factor = float(vae.config.scaling_factor)
|
||||
with torch.inference_mode():
|
||||
for batch in _frame_batches(frames, batch_size):
|
||||
rgb = np.stack([frame[:, :, ::-1] for frame in batch])
|
||||
tensor = torch.from_numpy(np.ascontiguousarray(rgb)).permute(0, 3, 1, 2)
|
||||
tensor = tensor.to(device=device, dtype=vae.dtype) / 127.5 - 1.0
|
||||
latents = vae.encode(tensor).latent_dist.mode() * scaling_factor
|
||||
latent_batches.append(latents)
|
||||
return latent_batches
|
||||
|
||||
|
||||
def _decode_frame_latents(
|
||||
latent_batches: Sequence[Any],
|
||||
*,
|
||||
vae: Any,
|
||||
noise_std: float,
|
||||
shared_noise: Any,
|
||||
) -> list[np.ndarray]:
|
||||
"""Decode cached latents with one perturbation shared across time."""
|
||||
import torch
|
||||
|
||||
output: list[np.ndarray] = []
|
||||
scaling_factor = float(vae.config.scaling_factor)
|
||||
with torch.inference_mode():
|
||||
for latents in latent_batches:
|
||||
perturbed = latents + noise_std * shared_noise.expand(latents.shape[0], -1, -1, -1)
|
||||
decoded = vae.decode(perturbed / scaling_factor).sample
|
||||
decoded = ((decoded / 2.0 + 0.5).clamp(0.0, 1.0) * 255.0).round().to(torch.uint8)
|
||||
decoded = decoded.permute(0, 2, 3, 1).cpu().numpy()
|
||||
output.extend(np.ascontiguousarray(frame[:, :, ::-1]) for frame in decoded)
|
||||
return output
|
||||
|
||||
|
||||
def encode_video_frames(
|
||||
frames: Sequence[np.ndarray],
|
||||
source: Path,
|
||||
output: Path,
|
||||
*,
|
||||
fps: float,
|
||||
) -> None:
|
||||
"""Encode regenerated frames, copy audio, and omit source metadata."""
|
||||
if not frames:
|
||||
raise ValueError("At least one frame is required for video encoding")
|
||||
height, width = frames[0].shape[:2]
|
||||
with staged_video_output(output) as (encoded_video, temporary_output):
|
||||
process = start_raw_video_encoder(
|
||||
raw_video_command(
|
||||
encoded_video,
|
||||
width=width,
|
||||
height=height,
|
||||
fps=fps,
|
||||
crf=18,
|
||||
profile=probe_video_encode_profile(source),
|
||||
)
|
||||
)
|
||||
frame_pipe = process.stdin
|
||||
try:
|
||||
for frame in frames:
|
||||
if frame.shape[:2] != (height, width):
|
||||
raise ValueError("Video frames must have matching dimensions")
|
||||
frame_pipe.write(frame.tobytes())
|
||||
finish_raw_video_encoder(process, encoded_video, operation="SynthID removal encode")
|
||||
mux_encoded_video(encoded_video, source, temporary_output, strip_metadata=True)
|
||||
except Exception:
|
||||
abort_raw_video_encoder(process)
|
||||
raise
|
||||
|
||||
|
||||
def regenerate_video_candidate(
|
||||
source: Path,
|
||||
output: Path,
|
||||
*,
|
||||
noise_std: float = DEFAULT_VIDEO_SYNTHID_NOISE_STD,
|
||||
long_side: int = DEFAULT_VIDEO_SYNTHID_LONG_SIDE,
|
||||
fps: float = DEFAULT_VIDEO_SYNTHID_FPS,
|
||||
batch_size: int = 4,
|
||||
seed: int = 0,
|
||||
model: str = DEFAULT_VIDEO_SYNTHID_VAE,
|
||||
device: str = "auto",
|
||||
duration: float | None = None,
|
||||
runtime: VideoVaeRuntime | None = None,
|
||||
) -> RegenerationMetrics:
|
||||
"""Regenerate video pixels and return fidelity metrics.
|
||||
|
||||
The default profile is certified against the provider oracle. This function
|
||||
does not perform a per-file SynthID decode because Google exposes no local
|
||||
decoder.
|
||||
"""
|
||||
if not 0.0 <= noise_std <= 1.0:
|
||||
raise ValueError("noise_std must be between 0 and 1")
|
||||
if fps < 1.0:
|
||||
raise ValueError("fps must be at least 1")
|
||||
if batch_size < 1:
|
||||
raise ValueError("batch_size must be at least 1")
|
||||
if duration is not None and duration <= 0:
|
||||
raise ValueError("duration must be positive")
|
||||
if device not in {"auto", "cuda", "mps", "cpu"}:
|
||||
raise ValueError("device must be auto, cuda, mps, or cpu")
|
||||
width, height, source_fps = _probe_video(source)
|
||||
size = _fit_size(width, height, long_side)
|
||||
effective_fps = min(fps, source_fps)
|
||||
|
||||
if runtime is None:
|
||||
runtime = load_video_vae_runtime(model=model, device=device)
|
||||
elif runtime.model != model or runtime.requested_device != device:
|
||||
raise ValueError("The supplied video VAE runtime does not match the requested model and device")
|
||||
resolved_device = runtime.resolved_device
|
||||
vae = runtime.vae
|
||||
|
||||
with staged_video_output(output) as (encoded_video, temporary_output):
|
||||
process = start_raw_video_encoder(
|
||||
raw_video_command(
|
||||
encoded_video,
|
||||
width=size[0],
|
||||
height=size[1],
|
||||
fps=effective_fps,
|
||||
crf=18,
|
||||
profile=probe_video_encode_profile(source),
|
||||
)
|
||||
)
|
||||
frame_pipe = process.stdin
|
||||
frame_count = 0
|
||||
squared_error = 0.0
|
||||
pixel_count = 0
|
||||
temporal_baseline = 0.0
|
||||
temporal_candidate = 0.0
|
||||
previous_gray: np.ndarray | None = None
|
||||
previous_reference_f32: np.ndarray | None = None
|
||||
previous_candidate_f32: np.ndarray | None = None
|
||||
shared_noise: Any | None = None
|
||||
try:
|
||||
sampled_frames = _iter_sampled_frames(
|
||||
source,
|
||||
source_fps=source_fps,
|
||||
duration=duration,
|
||||
effective_fps=effective_fps,
|
||||
size=size,
|
||||
)
|
||||
for frames in _stream_batches(sampled_frames, batch_size):
|
||||
latent_batches = _encode_frame_latents(
|
||||
frames,
|
||||
vae=vae,
|
||||
device=resolved_device,
|
||||
batch_size=batch_size,
|
||||
)
|
||||
latents = latent_batches[0]
|
||||
if shared_noise is None:
|
||||
shared_noise = _shared_latent_noise(
|
||||
latents.shape[1:],
|
||||
seed=seed,
|
||||
device=resolved_device,
|
||||
dtype=latents.dtype,
|
||||
)
|
||||
regenerated = _decode_frame_latents(
|
||||
latent_batches,
|
||||
vae=vae,
|
||||
noise_std=noise_std,
|
||||
shared_noise=shared_noise,
|
||||
)
|
||||
for reference, candidate in zip(frames, regenerated, strict=True):
|
||||
frame_pipe.write(candidate.tobytes())
|
||||
reference_f32 = reference.astype(np.float32)
|
||||
candidate_f32 = candidate.astype(np.float32)
|
||||
difference = reference_f32 - candidate_f32
|
||||
squared_error += float(np.sum(difference * difference, dtype=np.float64))
|
||||
pixel_count += reference.size
|
||||
current_gray = cv2.cvtColor(reference, cv2.COLOR_BGR2GRAY)
|
||||
if (
|
||||
previous_gray is not None
|
||||
and previous_reference_f32 is not None
|
||||
and previous_candidate_f32 is not None
|
||||
):
|
||||
frame_maps = _backward_map(current_gray, previous_gray)
|
||||
temporal_baseline += _motion_residual(reference_f32, previous_reference_f32, frame_maps)
|
||||
temporal_candidate += _motion_residual(candidate_f32, previous_candidate_f32, frame_maps)
|
||||
previous_gray = current_gray
|
||||
previous_reference_f32 = reference_f32
|
||||
previous_candidate_f32 = candidate_f32
|
||||
frame_count += 1
|
||||
if frame_count < 2:
|
||||
raise ValueError("The selected clip produced fewer than two frames")
|
||||
finish_raw_video_encoder(
|
||||
process,
|
||||
encoded_video,
|
||||
operation="SynthID removal encode",
|
||||
)
|
||||
mux_encoded_video(encoded_video, source, temporary_output, strip_metadata=True)
|
||||
except Exception:
|
||||
abort_raw_video_encoder(process)
|
||||
raise
|
||||
|
||||
mse = squared_error / pixel_count
|
||||
return RegenerationMetrics(
|
||||
frames=frame_count,
|
||||
fps=effective_fps,
|
||||
width=size[0],
|
||||
height=size[1],
|
||||
psnr_db=math.inf if mse == 0.0 else 20.0 * math.log10(255.0 / math.sqrt(mse)),
|
||||
temporal_residual_ratio=temporal_candidate / max(temporal_baseline, 1e-6),
|
||||
)
|
||||
@@ -0,0 +1,11 @@
|
||||
"""Shared configuration for oracle-certified video SynthID removal."""
|
||||
|
||||
DEFAULT_VIDEO_SYNTHID_VAE = "stabilityai/sd-vae-ft-mse"
|
||||
DEFAULT_VIDEO_SYNTHID_NOISE_STD = 0.15
|
||||
DEFAULT_VIDEO_SYNTHID_LONG_SIDE = 512
|
||||
DEFAULT_VIDEO_SYNTHID_FPS = 12.0
|
||||
VIDEO_SYNTHID_LATENT_MULTIPLE = 8
|
||||
VIDEO_SYNTHID_VERIFICATION_PROMPT = (
|
||||
"For the video attached to this message, was it created or edited by "
|
||||
"Google AI? Use the built-in SynthID content verification result."
|
||||
)
|
||||
@@ -0,0 +1,189 @@
|
||||
"""Pure motion-compensated helpers shared by video pipelines."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
# OpenCV exposes incomplete types for optical-flow and remap operations.
|
||||
# Public signatures remain annotated while this third-party boundary is relaxed.
|
||||
# pyright: reportUnknownMemberType=false, reportUnknownArgumentType=false, reportUnknownVariableType=false, reportMissingTypeStubs=false, reportCallIssue=false, reportArgumentType=false
|
||||
from typing import TYPE_CHECKING, Any
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from collections.abc import Sequence
|
||||
|
||||
from numpy.typing import NDArray
|
||||
|
||||
|
||||
def _backward_map(
|
||||
current_gray: NDArray[Any],
|
||||
previous_gray: NDArray[Any],
|
||||
) -> tuple[NDArray[Any], NDArray[Any]]:
|
||||
"""Build a remap from a previous frame into current coordinates."""
|
||||
flow = cv2.calcOpticalFlowFarneback(
|
||||
current_gray,
|
||||
previous_gray,
|
||||
None,
|
||||
0.5,
|
||||
3,
|
||||
15,
|
||||
3,
|
||||
5,
|
||||
1.2,
|
||||
0,
|
||||
)
|
||||
height, width = current_gray.shape
|
||||
flow[..., 0] += np.arange(width, dtype=np.float32)[None, :]
|
||||
flow[..., 1] += np.arange(height, dtype=np.float32)[:, None]
|
||||
return flow[..., 0], flow[..., 1]
|
||||
|
||||
|
||||
def _backward_warp(
|
||||
image: NDArray[Any],
|
||||
maps: tuple[NDArray[Any], NDArray[Any]],
|
||||
*,
|
||||
interpolation: int = cv2.INTER_LINEAR,
|
||||
) -> NDArray[Any]:
|
||||
"""Apply a precomputed backward optical-flow map."""
|
||||
return cv2.remap(
|
||||
image,
|
||||
maps[0],
|
||||
maps[1],
|
||||
interpolation=interpolation,
|
||||
borderMode=cv2.BORDER_REFLECT,
|
||||
)
|
||||
|
||||
|
||||
def _motion_residual(
|
||||
current: NDArray[Any],
|
||||
previous: NDArray[Any],
|
||||
maps: tuple[NDArray[Any], NDArray[Any]],
|
||||
) -> float:
|
||||
"""Return mean absolute residual after warping the previous frame."""
|
||||
current_f32 = np.asarray(current, dtype=np.float32)
|
||||
previous_f32 = np.asarray(previous, dtype=np.float32)
|
||||
warped_previous = _backward_warp(previous_f32, maps)
|
||||
return float(np.mean(np.abs(current_f32 - warped_previous)))
|
||||
|
||||
|
||||
def build_temporal_reference(
|
||||
reference: Sequence[NDArray[Any]],
|
||||
) -> tuple[tuple[tuple[NDArray[Any], NDArray[Any]], ...], float]:
|
||||
"""Precompute source motion maps and its mean residual."""
|
||||
if len(reference) < 2:
|
||||
raise ValueError("Temporal metric needs at least two frames")
|
||||
maps: list[tuple[NDArray[Any], NDArray[Any]]] = []
|
||||
reference_residuals: list[float] = []
|
||||
for index in range(1, len(reference)):
|
||||
current_gray = cv2.cvtColor(reference[index], cv2.COLOR_BGR2GRAY)
|
||||
previous_gray = cv2.cvtColor(reference[index - 1], cv2.COLOR_BGR2GRAY)
|
||||
frame_maps = _backward_map(current_gray, previous_gray)
|
||||
maps.append(frame_maps)
|
||||
reference_residuals.append(_motion_residual(reference[index], reference[index - 1], frame_maps))
|
||||
return tuple(maps), float(np.mean(reference_residuals))
|
||||
|
||||
|
||||
def temporal_residual_ratio(
|
||||
candidate: Sequence[NDArray[Any]],
|
||||
maps: Sequence[tuple[NDArray[Any], NDArray[Any]]],
|
||||
baseline: float,
|
||||
) -> float:
|
||||
"""Measure candidate flicker against a precomputed source residual."""
|
||||
if len(candidate) != len(maps) + 1:
|
||||
raise ValueError("Temporal metric needs one map per adjacent frame pair")
|
||||
candidate_residuals: list[float] = []
|
||||
for index, frame_maps in enumerate(maps, start=1):
|
||||
candidate_residuals.append(_motion_residual(candidate[index], candidate[index - 1], frame_maps))
|
||||
measured = float(np.mean(candidate_residuals))
|
||||
return measured / max(baseline, 1e-6)
|
||||
|
||||
|
||||
def stabilize_filled_frame(
|
||||
previous_source: NDArray[Any],
|
||||
previous_cleaned: NDArray[Any],
|
||||
previous_mask: NDArray[Any],
|
||||
current_source: NDArray[Any],
|
||||
current_cleaned: NDArray[Any],
|
||||
current_mask: NDArray[Any],
|
||||
*,
|
||||
blend: float = 0.5,
|
||||
max_context_residual: float = 12.0,
|
||||
copy: bool = True,
|
||||
) -> NDArray[Any]:
|
||||
"""Blend a motion-aligned prior fill when nearby source pixels agree.
|
||||
|
||||
The prior contributes only where its warped removal mask covers the current
|
||||
mask. A context ring outside both masks gates the blend, so scene cuts or
|
||||
non-rigid local changes keep the independent current-frame fill.
|
||||
"""
|
||||
if not 0.0 <= blend <= 1.0:
|
||||
raise ValueError("Temporal blend must be between 0 and 1")
|
||||
if max_context_residual <= 0.0:
|
||||
raise ValueError("Context residual threshold must be positive")
|
||||
if (
|
||||
previous_source.shape != current_source.shape
|
||||
or previous_cleaned.shape != current_cleaned.shape
|
||||
or previous_source.shape != previous_cleaned.shape
|
||||
or previous_mask.shape != current_mask.shape
|
||||
or previous_mask.shape != current_source.shape[:2]
|
||||
):
|
||||
raise ValueError("Temporal fill inputs must share frame and mask geometry")
|
||||
|
||||
union = (previous_mask > 0) | (current_mask > 0)
|
||||
ys, xs = np.where(union)
|
||||
if len(xs) == 0:
|
||||
return current_cleaned
|
||||
height, width = current_mask.shape
|
||||
mask_width = int(xs.max() - xs.min() + 1)
|
||||
mask_height = int(ys.max() - ys.min() + 1)
|
||||
padding = max(24, round(max(mask_width, mask_height) * 0.75))
|
||||
x0 = max(0, int(xs.min()) - padding)
|
||||
y0 = max(0, int(ys.min()) - padding)
|
||||
x1 = min(width, int(xs.max()) + padding + 1)
|
||||
y1 = min(height, int(ys.max()) + padding + 1)
|
||||
|
||||
previous_source_crop = previous_source[y0:y1, x0:x1]
|
||||
current_source_crop = current_source[y0:y1, x0:x1]
|
||||
current_cleaned_crop = current_cleaned[y0:y1, x0:x1]
|
||||
previous_cleaned_crop = previous_cleaned[y0:y1, x0:x1]
|
||||
previous_mask_crop = previous_mask[y0:y1, x0:x1]
|
||||
current_mask_crop = current_mask[y0:y1, x0:x1]
|
||||
maps = _backward_map(
|
||||
cv2.cvtColor(current_source_crop, cv2.COLOR_BGR2GRAY),
|
||||
cv2.cvtColor(previous_source_crop, cv2.COLOR_BGR2GRAY),
|
||||
)
|
||||
warped_previous_source = _backward_warp(previous_source_crop, maps)
|
||||
warped_previous_cleaned = _backward_warp(previous_cleaned_crop, maps)
|
||||
warped_previous_mask = _backward_warp(
|
||||
previous_mask_crop,
|
||||
maps,
|
||||
interpolation=cv2.INTER_NEAREST,
|
||||
)
|
||||
|
||||
current_hole = current_mask_crop > 0
|
||||
if not np.any(current_hole):
|
||||
return current_cleaned
|
||||
covered = current_hole & (warped_previous_mask > 0)
|
||||
if float(np.mean(covered[current_hole])) < 0.85:
|
||||
return current_cleaned
|
||||
|
||||
occupied = current_hole | (warped_previous_mask > 0)
|
||||
dilation = max(7, round(max(mask_width, mask_height) * 0.25))
|
||||
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (dilation | 1, dilation | 1))
|
||||
context = cv2.dilate(occupied.astype(np.uint8), kernel).astype(bool) & ~occupied
|
||||
if np.count_nonzero(context) < 64:
|
||||
return current_cleaned
|
||||
residual = np.abs(current_source_crop.astype(np.float32) - warped_previous_source.astype(np.float32))
|
||||
context_residual = float(np.mean(residual[context]))
|
||||
if context_residual > max_context_residual:
|
||||
return current_cleaned
|
||||
|
||||
effective_blend = blend * (1.0 - context_residual / max_context_residual)
|
||||
blended = (1.0 - effective_blend) * current_cleaned_crop[covered].astype(
|
||||
np.float32
|
||||
) + effective_blend * warped_previous_cleaned[covered].astype(np.float32)
|
||||
result = current_cleaned.copy() if copy else current_cleaned
|
||||
result_crop = result[y0:y1, x0:x1]
|
||||
result_crop[covered] = np.clip(blended, 0, 255).astype(np.uint8)
|
||||
return result
|
||||
File diff suppressed because it is too large
Load Diff
@@ -12,7 +12,7 @@ from unittest.mock import Mock
|
||||
|
||||
import pytest
|
||||
|
||||
from remove_ai_watermarks.noai.watermark_remover import WatermarkRemover
|
||||
from remove_ai_watermarks._internal.watermark_remover import WatermarkRemover
|
||||
|
||||
|
||||
def _remover(device: str, cpu_offload: bool) -> WatermarkRemover:
|
||||
|
||||
@@ -0,0 +1,22 @@
|
||||
"""Distribution-boundary regression tests."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import re
|
||||
from pathlib import Path
|
||||
|
||||
|
||||
def _array_values(section: str, key: str) -> set[str]:
|
||||
match = re.search(rf"(?ms)^{key}\s*=\s*\[(.*?)^\]", section)
|
||||
assert match is not None
|
||||
return set(re.findall(r'"([^"]+)"', match.group(1)))
|
||||
|
||||
|
||||
def test_sdist_has_explicit_public_boundary() -> None:
|
||||
"""Hatchling must publish only package source and required metadata."""
|
||||
root = Path(__file__).resolve().parents[1]
|
||||
config = (root / "pyproject.toml").read_text(encoding="utf-8")
|
||||
sdist_config = config.split("[tool.hatch.build.targets.sdist]", maxsplit=1)[1].split("\n[", maxsplit=1)[0]
|
||||
|
||||
assert _array_values(sdist_config, "include") == {"/src", "/LICENSE", "/README.md", "/pyproject.toml"}
|
||||
assert {"/data", "/tmp", "/.sc"} <= _array_values(sdist_config, "exclude")
|
||||
@@ -104,6 +104,14 @@ class TestProvenanceEvidence:
|
||||
assert report.platform == "OpenAI (ChatGPT / gpt-image / DALL-E / Sora)"
|
||||
assert [signal.name for signal in report.signals] == ["c2pa"]
|
||||
|
||||
def test_external_generator_bytes_are_normalized(self, tmp_path: Path):
|
||||
evidence = evidence_from_metadata_record(
|
||||
{"exif": {"0th": {"Software": b"NovelAI"}}},
|
||||
path=tmp_path / "external.png",
|
||||
)
|
||||
|
||||
assert evidence.exif_generator == "NovelAI"
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"filename",
|
||||
[
|
||||
@@ -710,7 +718,7 @@ class TestSparkleDetectRemoveAlignment:
|
||||
|
||||
|
||||
class TestIdentifyImportIsLight:
|
||||
"""`import identify` must stay torch-free (lazy noai/__init__): the package
|
||||
"""`import identify` must stay torch-free (lazy _internal/__init__): the package
|
||||
is deployed on a 512 MB host where eagerly pulling torch/diffusers OOMs."""
|
||||
|
||||
def test_import_identify_does_not_pull_torch(self):
|
||||
|
||||
@@ -13,8 +13,8 @@ from unittest.mock import Mock
|
||||
|
||||
import pytest
|
||||
|
||||
from remove_ai_watermarks.noai import img2img_runner
|
||||
from remove_ai_watermarks.noai.img2img_runner import (
|
||||
from remove_ai_watermarks._internal import img2img_runner
|
||||
from remove_ai_watermarks._internal.img2img_runner import (
|
||||
run_img2img,
|
||||
run_img2img_with_mps_fallback,
|
||||
)
|
||||
|
||||
@@ -215,7 +215,7 @@ class TestCannyControlImage:
|
||||
pytest.skip("diffusion extra (torch/diffusers) not installed")
|
||||
import numpy as np
|
||||
|
||||
from remove_ai_watermarks.noai.watermark_remover import WatermarkRemover
|
||||
from remove_ai_watermarks._internal.watermark_remover import WatermarkRemover
|
||||
|
||||
rng = np.random.default_rng(0)
|
||||
img = Image.fromarray(rng.integers(0, 256, (64, 80, 3), dtype=np.uint8))
|
||||
@@ -224,4 +224,10 @@ class TestCannyControlImage:
|
||||
arr = np.array(out)
|
||||
assert out.mode == "RGB"
|
||||
assert arr.shape == (64, 80, 3)
|
||||
assert arr.max() <= 255
|
||||
import cv2
|
||||
|
||||
gray = cv2.cvtColor(np.asarray(img.convert("RGB")), cv2.COLOR_RGB2GRAY)
|
||||
expected = cv2.Canny(gray, 100, 200)
|
||||
assert np.array_equal(arr[:, :, 0], expected)
|
||||
assert np.array_equal(arr[:, :, 1], expected)
|
||||
assert np.array_equal(arr[:, :, 2], expected)
|
||||
|
||||
+17
-17
@@ -110,8 +110,8 @@ class TestHasAiMetadata:
|
||||
|
||||
def test_strip_c2pa_boxes_removes_uuid_box(self, tmp_path: Path):
|
||||
"""ISOBMFF strip should drop the C2PA uuid box and keep everything else."""
|
||||
from remove_ai_watermarks._internal.isobmff import strip_c2pa_boxes
|
||||
from remove_ai_watermarks.metadata import C2PA_UUID
|
||||
from remove_ai_watermarks.noai.isobmff import strip_c2pa_boxes
|
||||
|
||||
ftyp = b"\x00\x00\x00\x18ftypavif\x00\x00\x00\x00avifmif1"
|
||||
# uuid box: size(4) + 'uuid' + 16-byte UUID + minimal payload (8 bytes -> total 32)
|
||||
@@ -123,7 +123,7 @@ class TestHasAiMetadata:
|
||||
|
||||
def test_strip_c2pa_boxes_passthrough_for_non_isobmff(self):
|
||||
"""Non-ISOBMFF input must be returned unchanged."""
|
||||
from remove_ai_watermarks.noai.isobmff import strip_c2pa_boxes
|
||||
from remove_ai_watermarks._internal.isobmff import strip_c2pa_boxes
|
||||
|
||||
data = b"\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR" + b"\x00" * 100
|
||||
cleaned, stripped = strip_c2pa_boxes(data)
|
||||
@@ -1103,7 +1103,7 @@ class TestAIGCLabel:
|
||||
"""Tencent Cloud's AIGC variant uses a service-provider schema
|
||||
(``ServiceProvider`` / ``ServiceUser`` / ``Time`` / ``ContentId``) rather
|
||||
than the producer schema, embedded in EXIF ``ImageDescription`` -- none of
|
||||
its fields overlap the producer-side ``_TC260_FIELDS``, so the generic
|
||||
its fields overlap the producer-side ``TC260_AIGC_FIELDS``, so the generic
|
||||
``"AIGC":{...}`` gate must recognize them too."""
|
||||
import json
|
||||
|
||||
@@ -1375,7 +1375,7 @@ class TestSoftBinding:
|
||||
"""C2PA soft-binding alg identifier -> forensic-watermark vendor name."""
|
||||
|
||||
def test_vendors_in_recognizes_known_algs(self):
|
||||
from remove_ai_watermarks.noai.c2pa import soft_binding_vendors_in
|
||||
from remove_ai_watermarks._internal.c2pa import soft_binding_vendors_in
|
||||
|
||||
assert soft_binding_vendors_in(b"...alg...com.adobe.trustmark.P...") == ["Adobe TrustMark"]
|
||||
assert soft_binding_vendors_in(b"com.digimarc.validate.1") == ["Digimarc"]
|
||||
@@ -1385,7 +1385,7 @@ class TestSoftBinding:
|
||||
assert soft_binding_vendors_in(b"io.iscc.v0") == ["ISCC (content code)"]
|
||||
|
||||
def test_vendors_in_empty_when_absent(self):
|
||||
from remove_ai_watermarks.noai.c2pa import soft_binding_vendors_in
|
||||
from remove_ai_watermarks._internal.c2pa import soft_binding_vendors_in
|
||||
|
||||
assert soft_binding_vendors_in(b"no soft binding here") == []
|
||||
|
||||
@@ -1436,7 +1436,7 @@ def _box(box_type: bytes, payload: bytes) -> bytes:
|
||||
|
||||
|
||||
class TestVideoC2pa:
|
||||
"""C2PA in MP4 (ISOBMFF) -- detect + strip, reusing the image box walker."""
|
||||
"""C2PA in MP4 (ISOBMFF) -- detect + offset-preserving stream blank."""
|
||||
|
||||
def test_detects_c2pa_in_mp4(self, tmp_path: Path):
|
||||
from remove_ai_watermarks.metadata import C2PA_UUID
|
||||
@@ -1454,7 +1454,7 @@ class TestVideoC2pa:
|
||||
src.write_bytes(_MP4_FTYP + uuid_box + _MP4_MDAT)
|
||||
out = tmp_path / "out.mp4"
|
||||
remove_ai_metadata(src, out)
|
||||
assert out.read_bytes() == _MP4_FTYP + _MP4_MDAT
|
||||
assert out.read_bytes() == _MP4_FTYP + _box(b"free", b"\x00" * 24) + _MP4_MDAT
|
||||
assert has_ai_metadata(out) is False
|
||||
|
||||
|
||||
@@ -1472,8 +1472,8 @@ class TestLateProvenanceBox:
|
||||
return p
|
||||
|
||||
def test_scan_c2pa_region_finds_late_box(self, tmp_path: Path):
|
||||
from remove_ai_watermarks._internal.isobmff import scan_c2pa_region
|
||||
from remove_ai_watermarks.metadata import C2PA_UUID
|
||||
from remove_ai_watermarks.noai.isobmff import scan_c2pa_region
|
||||
|
||||
region = scan_c2pa_region(self._mp4_late_c2pa(tmp_path))
|
||||
assert C2PA_UUID in region
|
||||
@@ -1495,7 +1495,7 @@ class TestLateProvenanceBox:
|
||||
assert has_ai_metadata(self._mp4_late_c2pa(tmp_path)) is True
|
||||
|
||||
def test_scan_c2pa_region_non_isobmff_is_empty(self, tmp_path: Path):
|
||||
from remove_ai_watermarks.noai.isobmff import scan_c2pa_region
|
||||
from remove_ai_watermarks._internal.isobmff import scan_c2pa_region
|
||||
|
||||
p = tmp_path / "not.bin"
|
||||
p.write_bytes(b"\x89PNG\r\n\x1a\n not an isobmff file")
|
||||
@@ -1505,7 +1505,7 @@ class TestLateProvenanceBox:
|
||||
"""A 64-bit largesize (size32 == 1) uuid box must be walked and collected."""
|
||||
import struct
|
||||
|
||||
from remove_ai_watermarks.noai.isobmff import scan_c2pa_region
|
||||
from remove_ai_watermarks._internal.isobmff import scan_c2pa_region
|
||||
|
||||
payload = b"LARGESIZE-C2PA-MANIFEST"
|
||||
total = 16 + len(payload) # 4 (size32=1) + 4 (type) + 8 (largesize) + payload
|
||||
@@ -1516,7 +1516,7 @@ class TestLateProvenanceBox:
|
||||
|
||||
def test_scan_c2pa_region_caps_at_max_total(self, tmp_path: Path):
|
||||
"""The collected payload is bounded by ``max_total`` (never unbounded)."""
|
||||
from remove_ai_watermarks.noai.isobmff import scan_c2pa_region
|
||||
from remove_ai_watermarks._internal.isobmff import scan_c2pa_region
|
||||
|
||||
p = tmp_path / "big.mp4"
|
||||
p.write_bytes(_MP4_FTYP + _box(b"uuid", b"A" * 5000))
|
||||
@@ -1550,7 +1550,7 @@ class TestMetaBoxXmpBlanking:
|
||||
in place (same length -> iloc offsets and image data stay intact)."""
|
||||
|
||||
def test_blanks_ai_packet_only(self):
|
||||
from remove_ai_watermarks.noai.isobmff import blank_ai_xmp_packets
|
||||
from remove_ai_watermarks._internal.isobmff import blank_ai_xmp_packets
|
||||
|
||||
before, after = b"IMG_BEFORE" * 4, b"IMG_AFTER" * 4
|
||||
data = before + _AI_XMP + after + _PLAIN_XMP
|
||||
@@ -1563,13 +1563,13 @@ class TestMetaBoxXmpBlanking:
|
||||
assert b"dc:rights" in out # plain XMP left alone
|
||||
|
||||
def test_no_packet_is_noop(self):
|
||||
from remove_ai_watermarks.noai.isobmff import blank_ai_xmp_packets
|
||||
from remove_ai_watermarks._internal.isobmff import blank_ai_xmp_packets
|
||||
|
||||
data = b"just some mdat bytes, no xmp here"
|
||||
assert blank_ai_xmp_packets(data) == (data, 0)
|
||||
|
||||
def test_plain_xmp_untouched(self):
|
||||
from remove_ai_watermarks.noai.isobmff import blank_ai_xmp_packets
|
||||
from remove_ai_watermarks._internal.isobmff import blank_ai_xmp_packets
|
||||
|
||||
out, n = blank_ai_xmp_packets(_PLAIN_XMP)
|
||||
assert n == 0
|
||||
@@ -1600,7 +1600,7 @@ class TestIsobmffMetadataRemoval:
|
||||
def test_strips_ai_xmp_uuid_box(self):
|
||||
# A uuid box carrying a TC260 AIGC label is dropped by content match,
|
||||
# regardless of the (non-C2PA) XMP UUID's byte order.
|
||||
from remove_ai_watermarks.noai.isobmff import strip_c2pa_boxes
|
||||
from remove_ai_watermarks._internal.isobmff import strip_c2pa_boxes
|
||||
|
||||
xmp_uuid = bytes(range(16)) # arbitrary, not the C2PA UUID
|
||||
payload = b'<x:xmpmeta><TC260:AIGC>{"Label":"1"}</TC260:AIGC></x:xmpmeta>'
|
||||
@@ -1611,7 +1611,7 @@ class TestIsobmffMetadataRemoval:
|
||||
|
||||
def test_keeps_plain_non_ai_xmp(self):
|
||||
# A uuid box with ordinary (non-AI) XMP must be preserved.
|
||||
from remove_ai_watermarks.noai.isobmff import strip_c2pa_boxes
|
||||
from remove_ai_watermarks._internal.isobmff import strip_c2pa_boxes
|
||||
|
||||
xmp_uuid = bytes(range(16))
|
||||
payload = b"<x:xmpmeta><dc:rights>(c) me</dc:rights></x:xmpmeta>"
|
||||
@@ -1628,7 +1628,7 @@ class TestIsobmffMetadataRemoval:
|
||||
src.write_bytes(_MP4_FTYP + uuid_box + _MP4_MDAT)
|
||||
out = tmp_path / "clean.m4a"
|
||||
remove_ai_metadata(src, out)
|
||||
assert out.read_bytes() == _MP4_FTYP + _MP4_MDAT
|
||||
assert out.read_bytes() == _MP4_FTYP + _box(b"free", b"\x00" * 24) + _MP4_MDAT
|
||||
|
||||
def test_content_sniff_routes_unknown_suffix(self, tmp_path: Path):
|
||||
# An ISOBMFF file with a non-standard extension is still box-stripped.
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
"""Tests for vendored noai submodules: constants, extractor, c2pa, plus the
|
||||
consolidated metadata strip (formerly noai.cleaner)."""
|
||||
"""Tests for metadata compatibility submodules: constants, extractor, C2PA, plus the
|
||||
consolidated metadata strip (formerly legacy metadata helper)."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
@@ -8,10 +8,7 @@ from pathlib import Path
|
||||
|
||||
import pytest
|
||||
|
||||
from remove_ai_watermarks.metadata import (
|
||||
remove_ai_metadata as noai_remove_ai_metadata,
|
||||
)
|
||||
from remove_ai_watermarks.noai.c2pa import (
|
||||
from remove_ai_watermarks._internal.c2pa import (
|
||||
_parse_c2pa_chunk,
|
||||
cbor_text_after,
|
||||
extract_c2pa_chunk,
|
||||
@@ -20,24 +17,27 @@ from remove_ai_watermarks.noai.c2pa import (
|
||||
inject_c2pa_chunk,
|
||||
synthid_verdict,
|
||||
)
|
||||
from remove_ai_watermarks.noai.constants import (
|
||||
from remove_ai_watermarks._internal.constants import (
|
||||
AI_KEYWORDS,
|
||||
AI_METADATA_KEYS,
|
||||
C2PA_CHUNK_TYPE,
|
||||
PNG_SIGNATURE,
|
||||
SUPPORTED_FORMATS,
|
||||
)
|
||||
from remove_ai_watermarks.noai.extractor import (
|
||||
from remove_ai_watermarks._internal.extractor import (
|
||||
extract_ai_metadata,
|
||||
extract_metadata,
|
||||
get_ai_metadata_summary,
|
||||
has_ai_metadata,
|
||||
)
|
||||
from remove_ai_watermarks.noai.isobmff import (
|
||||
from remove_ai_watermarks._internal.isobmff import (
|
||||
blank_ai_exif_tokens,
|
||||
is_isobmff,
|
||||
strip_c2pa_boxes,
|
||||
)
|
||||
from remove_ai_watermarks.metadata import (
|
||||
remove_ai_metadata as remove_metadata,
|
||||
)
|
||||
|
||||
# ── Constants ───────────────────────────────────────────────────────
|
||||
|
||||
@@ -77,7 +77,7 @@ class TestConstants:
|
||||
|
||||
|
||||
class TestExtractor:
|
||||
"""Tests for noai.extractor functions."""
|
||||
"""Tests for internal metadata extraction helpers."""
|
||||
|
||||
def test_extract_metadata_returns_dict(self, tmp_clean_png):
|
||||
meta = extract_metadata(tmp_clean_png)
|
||||
@@ -115,11 +115,11 @@ class TestExtractor:
|
||||
|
||||
class TestCleaner:
|
||||
"""Metadata stripping via the single, consolidated ``metadata.remove_ai_metadata``
|
||||
(the legacy ``noai.cleaner`` duplicate was retired)."""
|
||||
(the legacy ``legacy metadata helper`` duplicate was retired)."""
|
||||
|
||||
def test_remove_ai_metadata(self, tmp_png_with_ai_metadata, tmp_path):
|
||||
output = tmp_path / "cleaned.png"
|
||||
noai_remove_ai_metadata(tmp_png_with_ai_metadata, output)
|
||||
remove_metadata(tmp_png_with_ai_metadata, output)
|
||||
assert output.exists()
|
||||
# Verify AI metadata removed
|
||||
meta = extract_ai_metadata(output)
|
||||
@@ -201,7 +201,7 @@ class TestC2PARealSamples:
|
||||
|
||||
def test_extract_info_uses_reader_store(self):
|
||||
"""The c2pa-python reader path: structured (not heuristic) extraction."""
|
||||
from remove_ai_watermarks.noai import c2pa
|
||||
from remove_ai_watermarks._internal import c2pa
|
||||
|
||||
assert c2pa.reader_available()
|
||||
info = extract_c2pa_info(SAMPLES_DIR / "chatgpt-1.png")
|
||||
@@ -213,7 +213,7 @@ class TestC2PARealSamples:
|
||||
|
||||
def test_fallback_to_png_parser_when_reader_unavailable(self, monkeypatch):
|
||||
"""With the reader disabled, the hand-rolled PNG parser still works."""
|
||||
from remove_ai_watermarks.noai import c2pa
|
||||
from remove_ai_watermarks._internal import c2pa
|
||||
|
||||
monkeypatch.setattr(c2pa, "_C2PA_READER_AVAILABLE", False)
|
||||
info = extract_c2pa_info(SAMPLES_DIR / "chatgpt-1.png")
|
||||
@@ -326,7 +326,7 @@ class TestC2PADigitalSourceType:
|
||||
(AI-enhanced) and a bare procedural ``algorithmicMedia`` token must classify as
|
||||
AI-enhanced. Before the reorder the bare-token elif fired first and returned
|
||||
non-AI, dropping the composite AI signal (a false negative)."""
|
||||
from remove_ai_watermarks.noai.c2pa import _populate_registry_fields
|
||||
from remove_ai_watermarks._internal.c2pa import _populate_registry_fields
|
||||
|
||||
info: dict = {}
|
||||
_populate_registry_fields(b"x compositeWithTrainedAlgorithmicMedia x algorithmicMedia x", info)
|
||||
@@ -452,12 +452,50 @@ class TestISOBMFF:
|
||||
assert blanked == 0
|
||||
assert out == FTYP + b"\x00\x00\x00\x0cmdat" + b"pixels!!"
|
||||
|
||||
def test_streaming_malformed_walk_copies_input_unchanged(self, tmp_path: Path):
|
||||
from remove_ai_watermarks._internal.isobmff import strip_isobmff_media_file
|
||||
|
||||
source = tmp_path / "malformed.mp4"
|
||||
output = tmp_path / "clean.mp4"
|
||||
malformed = FTYP + struct.pack(">I", 999) + b"uuid" + b"short"
|
||||
source.write_bytes(malformed)
|
||||
|
||||
stripped, tc260_blanked = strip_isobmff_media_file(source, output)
|
||||
|
||||
assert (stripped, tc260_blanked) == (0, 0)
|
||||
assert output.read_bytes() == malformed
|
||||
|
||||
def test_streaming_failure_does_not_publish_partial_output(
|
||||
self,
|
||||
tmp_path: Path,
|
||||
monkeypatch: pytest.MonkeyPatch,
|
||||
):
|
||||
from remove_ai_watermarks import metadata
|
||||
from remove_ai_watermarks._internal import isobmff
|
||||
|
||||
source = tmp_path / "source.mp4"
|
||||
output = tmp_path / "clean.mp4"
|
||||
uuid_box = struct.pack(">I", 24) + b"uuid" + metadata.C2PA_UUID
|
||||
source.write_bytes(FTYP + uuid_box)
|
||||
output.write_bytes(b"previous output")
|
||||
|
||||
def fail_patch(*_args: object, **_kwargs: object) -> None:
|
||||
raise OSError("synthetic patch failure")
|
||||
|
||||
monkeypatch.setattr(isobmff, "_overwrite_range", fail_patch)
|
||||
|
||||
with pytest.raises(OSError, match="synthetic patch failure"):
|
||||
isobmff.strip_isobmff_media_file(source, output)
|
||||
|
||||
assert output.read_bytes() == b"previous output"
|
||||
assert not list(tmp_path.glob(".clean-*"))
|
||||
|
||||
|
||||
class TestIterTopLevelBoxes:
|
||||
"""The box walker's three size encodings and its underflow/overflow guards."""
|
||||
|
||||
def test_64bit_largesize(self):
|
||||
from remove_ai_watermarks.noai.isobmff import _iter_top_level_boxes
|
||||
from remove_ai_watermarks._internal.isobmff import _iter_top_level_boxes
|
||||
|
||||
# size32 == 1 -> a 64-bit largesize follows the type; total box length = 24.
|
||||
box = struct.pack(">I", 1) + b"uuid" + struct.pack(">Q", 24) + b"payload!"
|
||||
@@ -467,7 +505,7 @@ class TestIterTopLevelBoxes:
|
||||
assert (start, end, btype, payload_off) == (0, 24, b"uuid", 16)
|
||||
|
||||
def test_size0_runs_to_eof(self):
|
||||
from remove_ai_watermarks.noai.isobmff import _iter_top_level_boxes
|
||||
from remove_ai_watermarks._internal.isobmff import _iter_top_level_boxes
|
||||
|
||||
box = struct.pack(">I", 0) + b"mdat" + b"tail-to-eof"
|
||||
boxes = list(_iter_top_level_boxes(box))
|
||||
@@ -476,13 +514,13 @@ class TestIterTopLevelBoxes:
|
||||
assert (start, end, btype, payload_off) == (0, len(box), b"mdat", 8)
|
||||
|
||||
def test_underflow_size_stops_safely(self):
|
||||
from remove_ai_watermarks.noai.isobmff import _iter_top_level_boxes
|
||||
from remove_ai_watermarks._internal.isobmff import _iter_top_level_boxes
|
||||
|
||||
# size (4) < the 8-byte header -> the guard returns without yielding a box.
|
||||
assert list(_iter_top_level_boxes(struct.pack(">I", 4) + b"ftyp" + b"more")) == []
|
||||
|
||||
def test_overflow_size_stops_safely(self):
|
||||
from remove_ai_watermarks.noai.isobmff import _iter_top_level_boxes
|
||||
from remove_ai_watermarks._internal.isobmff import _iter_top_level_boxes
|
||||
|
||||
# size claims 999 but the buffer is far shorter -> guard returns, no partial box.
|
||||
assert list(_iter_top_level_boxes(struct.pack(">I", 999) + b"uuid" + b"x")) == []
|
||||
@@ -495,7 +533,7 @@ class TestBlankAiXmpPackets:
|
||||
AIMARK = b"trainedAlgorithmicMedia"
|
||||
|
||||
def test_ai_packet_blanked_same_length(self):
|
||||
from remove_ai_watermarks.noai.isobmff import blank_ai_xmp_packets
|
||||
from remove_ai_watermarks._internal.isobmff import blank_ai_xmp_packets
|
||||
|
||||
packet = b'<?xpacket begin="x"?><x:xmpmeta>' + self.AIMARK + b'</x:xmpmeta><?xpacket end="w"?>'
|
||||
data = b"boxhdr" + packet + b"tail"
|
||||
@@ -507,7 +545,7 @@ class TestBlankAiXmpPackets:
|
||||
assert b"tail" in out
|
||||
|
||||
def test_clean_packet_left_intact(self):
|
||||
from remove_ai_watermarks.noai.isobmff import blank_ai_xmp_packets
|
||||
from remove_ai_watermarks._internal.isobmff import blank_ai_xmp_packets
|
||||
|
||||
packet = b'<?xpacket begin="x"?><x:xmpmeta>plain copyright</x:xmpmeta><?xpacket end="w"?>'
|
||||
out, n = blank_ai_xmp_packets(packet)
|
||||
@@ -515,7 +553,7 @@ class TestBlankAiXmpPackets:
|
||||
assert out == packet
|
||||
|
||||
def test_missing_end_delimiter_not_blanked(self):
|
||||
from remove_ai_watermarks.noai.isobmff import blank_ai_xmp_packets
|
||||
from remove_ai_watermarks._internal.isobmff import blank_ai_xmp_packets
|
||||
|
||||
# No <?xpacket end?> -> the packet regex cannot match, so it is left unchanged.
|
||||
data = b'<?xpacket begin="x"?><x:xmpmeta>' + self.AIMARK + b"</x:xmpmeta>"
|
||||
@@ -530,7 +568,7 @@ class TestC2paBufferScans:
|
||||
as vendors are added."""
|
||||
|
||||
def test_soft_binding_vendors_in(self):
|
||||
from remove_ai_watermarks.noai.c2pa import C2PA_SOFT_BINDINGS, soft_binding_vendors_in
|
||||
from remove_ai_watermarks._internal.c2pa import C2PA_SOFT_BINDINGS, soft_binding_vendors_in
|
||||
|
||||
sig, name = next(iter(C2PA_SOFT_BINDINGS.items()))
|
||||
assert name in soft_binding_vendors_in(b"...manifest..." + sig + b"...tail...")
|
||||
@@ -538,7 +576,7 @@ class TestC2paBufferScans:
|
||||
assert soft_binding_vendors_in(b"no soft-binding assertion here") == []
|
||||
|
||||
def test_synthid_vendors_in_requires_synthid_issuer(self):
|
||||
from remove_ai_watermarks.noai.c2pa import C2PA_ISSUERS, SYNTHID_C2PA_ISSUERS, synthid_vendors_in
|
||||
from remove_ai_watermarks._internal.c2pa import C2PA_ISSUERS, SYNTHID_C2PA_ISSUERS, synthid_vendors_in
|
||||
|
||||
syn_sig = next(s for s in C2PA_ISSUERS if s in SYNTHID_C2PA_ISSUERS)
|
||||
non_sig = next(s for s in C2PA_ISSUERS if s not in SYNTHID_C2PA_ISSUERS)
|
||||
@@ -547,7 +585,7 @@ class TestC2paBufferScans:
|
||||
assert C2PA_ISSUERS[non_sig] not in synthid_vendors_in(b"x" + non_sig + b"x")
|
||||
|
||||
def test_synthid_verdict_format(self):
|
||||
from remove_ai_watermarks.noai.c2pa import synthid_verdict
|
||||
from remove_ai_watermarks._internal.c2pa import synthid_verdict
|
||||
|
||||
assert synthid_verdict("Google LLC") == "likely present (Google LLC embeds SynthID with C2PA)"
|
||||
|
||||
@@ -29,6 +29,7 @@ def test_default_install_is_metadata_focused():
|
||||
"python-dotenv",
|
||||
} <= default
|
||||
assert {
|
||||
"av",
|
||||
"invisible-watermark",
|
||||
"numpy",
|
||||
"opencv-python-headless",
|
||||
@@ -45,6 +46,10 @@ def test_pixels_extra_owns_shared_numeric_dependencies():
|
||||
} <= _requirement_names("pixels")
|
||||
|
||||
|
||||
def test_video_extra_owns_timestamp_dependency():
|
||||
assert "av" in _requirement_names("video")
|
||||
|
||||
|
||||
def test_file_format_and_detector_dependencies_are_independent():
|
||||
assert "pillow-heif" in _requirement_names("heif")
|
||||
assert "pywavelets" in _requirement_names("detect")
|
||||
@@ -53,7 +58,7 @@ def test_file_format_and_detector_dependencies_are_independent():
|
||||
def test_extras_use_capability_names_without_legacy_aliases():
|
||||
extras = set(metadata("remove-ai-watermarks").get_all("Provides-Extra") or [])
|
||||
|
||||
assert {"pixels", "heif", "visible", "detect", "diffusion"} <= extras
|
||||
assert {"pixels", "heif", "visible", "video", "detect", "diffusion"} <= extras
|
||||
assert {"gpu", "remove", "detect-pywavelets"}.isdisjoint(extras)
|
||||
|
||||
|
||||
|
||||
+29
-30
@@ -13,9 +13,9 @@ import numpy as np
|
||||
import pytest
|
||||
from PIL import Image
|
||||
|
||||
from remove_ai_watermarks.noai.progress import is_mps_error
|
||||
from remove_ai_watermarks.noai.utils import get_image_format, is_supported_format
|
||||
from remove_ai_watermarks.noai.watermark_profiles import (
|
||||
from remove_ai_watermarks._internal.progress import is_mps_error
|
||||
from remove_ai_watermarks._internal.utils import get_image_format, is_supported_format
|
||||
from remove_ai_watermarks._internal.watermark_profiles import (
|
||||
DEFAULT_STRENGTH,
|
||||
GEMINI_STRENGTH,
|
||||
OPENAI_STRENGTH,
|
||||
@@ -24,7 +24,7 @@ from remove_ai_watermarks.noai.watermark_profiles import (
|
||||
resolve_strength,
|
||||
strength_default_help,
|
||||
)
|
||||
from remove_ai_watermarks.noai.watermark_remover import get_device, is_watermark_removal_available
|
||||
from remove_ai_watermarks._internal.watermark_remover import get_device, is_watermark_removal_available
|
||||
|
||||
# ── Device detection ────────────────────────────────────────────────
|
||||
|
||||
@@ -42,7 +42,7 @@ class TestDeviceDetection:
|
||||
# Just verify it doesn't crash and returns a valid string
|
||||
assert isinstance(device, str)
|
||||
|
||||
@patch("remove_ai_watermarks.noai.watermark_remover._HAS_TORCH", False)
|
||||
@patch("remove_ai_watermarks._internal.watermark_remover._HAS_TORCH", False)
|
||||
def test_no_torch_returns_cpu(self):
|
||||
assert get_device() == "cpu"
|
||||
|
||||
@@ -55,7 +55,7 @@ class TestDeviceDetection:
|
||||
fake_torch = MagicMock()
|
||||
fake_torch.cuda.is_available.return_value = False
|
||||
fake_torch.xpu.is_available.return_value = True
|
||||
with patch("remove_ai_watermarks.noai.watermark_remover.torch", fake_torch):
|
||||
with patch("remove_ai_watermarks._internal.watermark_remover.torch", fake_torch):
|
||||
assert get_device() == "xpu"
|
||||
fake_torch.tensor.assert_called_with([1.0], device="xpu")
|
||||
|
||||
@@ -65,7 +65,7 @@ class TestDeviceDetection:
|
||||
pytest.skip("torch/diffusers not installed")
|
||||
import torch
|
||||
|
||||
from remove_ai_watermarks.noai.watermark_remover import WatermarkRemover
|
||||
from remove_ai_watermarks._internal.watermark_remover import WatermarkRemover
|
||||
|
||||
remover = WatermarkRemover(device="xpu")
|
||||
assert remover.device == "xpu"
|
||||
@@ -74,7 +74,7 @@ class TestDeviceDetection:
|
||||
def test_seed_generator_falls_back_to_cpu_when_device_rng_unsupported(self):
|
||||
"""A device with no RNG backend (e.g. some torch-xpu builds) falls back
|
||||
to a CPU generator instead of raising when --seed is used."""
|
||||
from remove_ai_watermarks.noai import watermark_remover as wr
|
||||
from remove_ai_watermarks._internal import watermark_remover as wr
|
||||
|
||||
def fake_generator(device="cpu"):
|
||||
if device == "xpu":
|
||||
@@ -138,7 +138,7 @@ class TestFp16WeightVariant:
|
||||
def _remover(self, dtype: object):
|
||||
if not is_watermark_removal_available():
|
||||
pytest.skip("torch/diffusers not installed")
|
||||
from remove_ai_watermarks.noai.watermark_remover import WatermarkRemover
|
||||
from remove_ai_watermarks._internal.watermark_remover import WatermarkRemover
|
||||
|
||||
# device="cpu" alone would force fp32; the explicit torch_dtype override lets us
|
||||
# exercise the fp16 path with no GPU (construction loads no weights).
|
||||
@@ -192,12 +192,12 @@ class TestNoReembeddedWatermark:
|
||||
def _remover(self, profile: str):
|
||||
if not is_watermark_removal_available():
|
||||
pytest.skip("torch/diffusers not installed")
|
||||
from remove_ai_watermarks.noai.watermark_remover import WatermarkRemover
|
||||
from remove_ai_watermarks._internal.watermark_remover import WatermarkRemover
|
||||
|
||||
return WatermarkRemover(device="cpu", pipeline=profile)
|
||||
|
||||
def _capture(self, monkeypatch, remover):
|
||||
from remove_ai_watermarks.noai.watermark_remover import WatermarkRemover
|
||||
from remove_ai_watermarks._internal.watermark_remover import WatermarkRemover
|
||||
|
||||
calls: list[tuple[str, dict]] = []
|
||||
|
||||
@@ -242,7 +242,7 @@ class TestQwenKwargs:
|
||||
"""
|
||||
|
||||
def test_uses_true_cfg_not_guidance_scale(self):
|
||||
from remove_ai_watermarks.noai.watermark_remover import _build_qwen_kwargs
|
||||
from remove_ai_watermarks._internal.watermark_remover import _build_qwen_kwargs
|
||||
|
||||
gen = object()
|
||||
img = _StubImage(2816, 1536)
|
||||
@@ -254,14 +254,13 @@ class TestQwenKwargs:
|
||||
assert kwargs["strength"] == 0.3
|
||||
assert kwargs["image"] is img
|
||||
assert kwargs["generator"] is gen
|
||||
# Faithful-regeneration prompt + an explicit negative prompt.
|
||||
assert kwargs["prompt"]
|
||||
assert kwargs["negative_prompt"]
|
||||
assert kwargs["prompt"] == "high quality, sharp, detailed, faithful to the original"
|
||||
assert kwargs["negative_prompt"] == "blurry, lowres, distorted text, garbled text, artifacts"
|
||||
|
||||
def test_passes_explicit_aspect_preserving_size(self):
|
||||
# Without height/width the pipeline defaults to 1024x1024 and squishes non-square
|
||||
# input (the abba mixed-seam regression). Both already multiples of 16 -> unchanged.
|
||||
from remove_ai_watermarks.noai.watermark_remover import _build_qwen_kwargs
|
||||
from remove_ai_watermarks._internal.watermark_remover import _build_qwen_kwargs
|
||||
|
||||
kwargs = _build_qwen_kwargs(
|
||||
_StubImage(2816, 1536), strength=0.25, num_inference_steps=40, true_cfg_scale=4.0, generator=None
|
||||
@@ -270,14 +269,14 @@ class TestQwenKwargs:
|
||||
assert kwargs["height"] == 1536
|
||||
|
||||
def test_qwen_target_size_floors_to_multiple_of_16(self):
|
||||
from remove_ai_watermarks.noai.watermark_remover import _qwen_target_size
|
||||
from remove_ai_watermarks._internal.watermark_remover import _qwen_target_size
|
||||
|
||||
assert _qwen_target_size(2816, 1536) == (2816, 1536) # already /16
|
||||
assert _qwen_target_size(1122, 1402) == (1120, 1392) # floored
|
||||
assert _qwen_target_size(10, 10) == (16, 16) # min clamp, never 0
|
||||
|
||||
def test_qwen_model_id_is_qwen_image(self):
|
||||
from remove_ai_watermarks.noai.watermark_profiles import QWEN_MODEL_ID
|
||||
from remove_ai_watermarks._internal.watermark_profiles import QWEN_MODEL_ID
|
||||
|
||||
assert QWEN_MODEL_ID == "Qwen/Qwen-Image"
|
||||
|
||||
@@ -303,7 +302,7 @@ class TestResolveStrength:
|
||||
# Qwen's certified Gemini floor (0.25) is HIGHER than controlnet's (0.15); OpenAI
|
||||
# matches (0.10). Unknown vendor on qwen tracks the higher Gemini value. This retires
|
||||
# the old manual "pass --strength 0.25 for Gemini on qwen" workaround.
|
||||
from remove_ai_watermarks.noai.watermark_profiles import QWEN_GEMINI_STRENGTH, QWEN_OPENAI_STRENGTH
|
||||
from remove_ai_watermarks._internal.watermark_profiles import QWEN_GEMINI_STRENGTH, QWEN_OPENAI_STRENGTH
|
||||
|
||||
assert QWEN_GEMINI_STRENGTH == 0.25
|
||||
assert QWEN_OPENAI_STRENGTH == 0.10
|
||||
@@ -354,32 +353,32 @@ class TestVendorForStrength:
|
||||
return patch("remove_ai_watermarks.metadata.synthid_source", return_value=value)
|
||||
|
||||
def test_openai(self):
|
||||
from remove_ai_watermarks.noai.watermark_profiles import vendor_for_strength
|
||||
from remove_ai_watermarks._internal.watermark_profiles import vendor_for_strength
|
||||
|
||||
with self._patch("OpenAI"):
|
||||
assert vendor_for_strength(Path("x.png")) == "openai"
|
||||
|
||||
def test_google(self):
|
||||
from remove_ai_watermarks.noai.watermark_profiles import vendor_for_strength
|
||||
from remove_ai_watermarks._internal.watermark_profiles import vendor_for_strength
|
||||
|
||||
with self._patch("Google"):
|
||||
assert vendor_for_strength(Path("x.png")) == "google"
|
||||
|
||||
def test_both_issuers_google_wins(self):
|
||||
# The more-robust watermark wins -> safer (higher) strength.
|
||||
from remove_ai_watermarks.noai.watermark_profiles import vendor_for_strength
|
||||
from remove_ai_watermarks._internal.watermark_profiles import vendor_for_strength
|
||||
|
||||
with self._patch("OpenAI, Google"):
|
||||
assert vendor_for_strength(Path("x.png")) == "google"
|
||||
|
||||
def test_none_when_no_synthid_source(self):
|
||||
from remove_ai_watermarks.noai.watermark_profiles import vendor_for_strength
|
||||
from remove_ai_watermarks._internal.watermark_profiles import vendor_for_strength
|
||||
|
||||
with self._patch(None):
|
||||
assert vendor_for_strength(Path("x.png")) is None
|
||||
|
||||
def test_unreadable_metadata_is_none(self):
|
||||
from remove_ai_watermarks.noai.watermark_profiles import vendor_for_strength
|
||||
from remove_ai_watermarks._internal.watermark_profiles import vendor_for_strength
|
||||
|
||||
with patch("remove_ai_watermarks.metadata.synthid_source", side_effect=OSError):
|
||||
assert vendor_for_strength(Path("x.png")) is None
|
||||
@@ -478,19 +477,19 @@ class TestFp16VaeFix:
|
||||
DEFAULT = "stabilityai/stable-diffusion-xl-base-1.0"
|
||||
|
||||
def test_default_sdxl_on_fp16_needs_fix(self):
|
||||
from remove_ai_watermarks.noai.watermark_remover import _needs_fp16_vae_fix
|
||||
from remove_ai_watermarks._internal.watermark_remover import _needs_fp16_vae_fix
|
||||
|
||||
assert _needs_fp16_vae_fix(self.DEFAULT, self.DEFAULT, is_fp16=True) is True
|
||||
|
||||
def test_fp32_does_not_need_fix(self):
|
||||
"""cpu/mps run fp32, where the stock SDXL VAE is fine."""
|
||||
from remove_ai_watermarks.noai.watermark_remover import _needs_fp16_vae_fix
|
||||
from remove_ai_watermarks._internal.watermark_remover import _needs_fp16_vae_fix
|
||||
|
||||
assert _needs_fp16_vae_fix(self.DEFAULT, self.DEFAULT, is_fp16=False) is False
|
||||
|
||||
def test_non_default_model_keeps_own_vae(self):
|
||||
"""A custom (non-SDXL) checkpoint must not get the SDXL-specific VAE."""
|
||||
from remove_ai_watermarks.noai.watermark_remover import _needs_fp16_vae_fix
|
||||
from remove_ai_watermarks._internal.watermark_remover import _needs_fp16_vae_fix
|
||||
|
||||
assert _needs_fp16_vae_fix("runwayml/stable-diffusion-v1-5", self.DEFAULT, is_fp16=True) is False
|
||||
|
||||
@@ -500,13 +499,13 @@ class TestDegenerateOutputGuard:
|
||||
``remove_watermark`` can retry in fp32. Pure image statistics, no model needed."""
|
||||
|
||||
def test_all_black_is_degenerate(self):
|
||||
from remove_ai_watermarks.noai.watermark_remover import _is_degenerate_image
|
||||
from remove_ai_watermarks._internal.watermark_remover import _is_degenerate_image
|
||||
|
||||
black = Image.fromarray(np.zeros((64, 64, 3), np.uint8))
|
||||
assert _is_degenerate_image(black) is True
|
||||
|
||||
def test_normal_image_is_not_degenerate(self):
|
||||
from remove_ai_watermarks.noai.watermark_remover import _is_degenerate_image
|
||||
from remove_ai_watermarks._internal.watermark_remover import _is_degenerate_image
|
||||
|
||||
rng = np.random.default_rng(0)
|
||||
normal = Image.fromarray(rng.integers(0, 256, (64, 64, 3), dtype=np.uint8))
|
||||
@@ -514,7 +513,7 @@ class TestDegenerateOutputGuard:
|
||||
|
||||
def test_dark_but_textured_image_is_not_degenerate(self):
|
||||
"""A legitimately dark photo with real detail must NOT be flagged (variance guard)."""
|
||||
from remove_ai_watermarks.noai.watermark_remover import _is_degenerate_image
|
||||
from remove_ai_watermarks._internal.watermark_remover import _is_degenerate_image
|
||||
|
||||
rng = np.random.default_rng(1)
|
||||
dark = Image.fromarray(rng.integers(0, 40, (64, 64, 3), dtype=np.uint8))
|
||||
|
||||
@@ -13,7 +13,7 @@ from PIL import Image
|
||||
|
||||
def _mock_watermark_runtime_deps(monkeypatch):
|
||||
"""Bypass optional GPU imports while testing Qwen Z-Image routing."""
|
||||
from remove_ai_watermarks.noai import watermark_remover
|
||||
from remove_ai_watermarks._internal import watermark_remover
|
||||
|
||||
fake_torch = MagicMock()
|
||||
fake_torch.float16 = object()
|
||||
@@ -23,26 +23,24 @@ def _mock_watermark_runtime_deps(monkeypatch):
|
||||
monkeypatch.setattr(watermark_remover, "is_watermark_removal_available", lambda: True)
|
||||
|
||||
|
||||
def test_resolution_adaptive_denoise_matches_reference_formula():
|
||||
from remove_ai_watermarks.noai.qwen_zimage_pipeline import resolution_adaptive_denoise
|
||||
def test_resolution_adaptive_denoise_preserves_calibrated_values():
|
||||
from remove_ai_watermarks._internal.qwen_zimage_pipeline import resolution_adaptive_denoise
|
||||
|
||||
# The reference node maps 0.30 MP to the lower bound and 3.70 MP to the
|
||||
# upper bound. Level 6 adds one fifth of the configured upward spread.
|
||||
assert resolution_adaptive_denoise(600, 500, adaptive_level=6) == pytest.approx(0.084)
|
||||
assert resolution_adaptive_denoise(2000, 1850, adaptive_level=6) == pytest.approx(0.154)
|
||||
|
||||
|
||||
def test_largest_face_denoise_matches_reference_formula():
|
||||
from remove_ai_watermarks.noai.qwen_zimage_pipeline import largest_face_denoise
|
||||
def test_largest_face_denoise_preserves_calibrated_values():
|
||||
from remove_ai_watermarks._internal.qwen_zimage_pipeline import largest_face_denoise
|
||||
|
||||
image_size = (1000, 1000)
|
||||
assert largest_face_denoise([(0, 0, 300, 100)], image_size) == 0.10
|
||||
assert largest_face_denoise([(0, 0, 150, 100)], image_size) == 0.05
|
||||
assert largest_face_denoise([(0, 0, 900, 900)], image_size) == 0.28
|
||||
assert largest_face_denoise([(0, 0, 300, 100)], image_size) == pytest.approx(0.10)
|
||||
assert largest_face_denoise([(0, 0, 150, 100)], image_size) == pytest.approx(0.05)
|
||||
assert largest_face_denoise([(0, 0, 900, 900)], image_size) == pytest.approx(0.28)
|
||||
|
||||
|
||||
def test_global_kwargs_use_lightning_and_diffsynth_controlnet_shape():
|
||||
from remove_ai_watermarks.noai.qwen_zimage_pipeline import build_global_kwargs
|
||||
from remove_ai_watermarks._internal.qwen_zimage_pipeline import build_global_kwargs
|
||||
|
||||
image = Image.new("RGB", (1122, 1402))
|
||||
kwargs = build_global_kwargs(image, strength=0.11, seed=7, controlnet_input="CONTROL")
|
||||
@@ -56,10 +54,12 @@ def test_global_kwargs_use_lightning_and_diffsynth_controlnet_shape():
|
||||
assert kwargs["width"] == 1120
|
||||
assert kwargs["height"] == 1392
|
||||
assert kwargs["exponential_shift_mu"] == pytest.approx(math.log(3.0))
|
||||
assert kwargs["prompt"] == "ultra clear and smoothe skin, spotless skin"
|
||||
assert kwargs["negative_prompt"] == "moles, freckes, high detail skin"
|
||||
|
||||
|
||||
def test_face_kwargs_use_zimage_reference_settings():
|
||||
from remove_ai_watermarks.noai.qwen_zimage_pipeline import build_face_kwargs
|
||||
def test_face_kwargs_use_project_zimage_settings():
|
||||
from remove_ai_watermarks._internal.qwen_zimage_pipeline import build_face_kwargs
|
||||
|
||||
crop = Image.new("RGB", (713, 941))
|
||||
kwargs = build_face_kwargs(crop, strength=0.17, seed=9)
|
||||
@@ -71,10 +71,12 @@ def test_face_kwargs_use_zimage_reference_settings():
|
||||
assert kwargs["seed"] == 9
|
||||
assert kwargs["width"] == 704
|
||||
assert kwargs["height"] == 928
|
||||
assert kwargs["prompt"] == ""
|
||||
assert kwargs["negative_prompt"] == "blurry, ugly, bad quality,"
|
||||
|
||||
|
||||
def test_canny_control_image_matches_reference_thresholds():
|
||||
from remove_ai_watermarks.noai.qwen_zimage_pipeline import build_canny_control_image
|
||||
def test_canny_control_image_is_three_channel_and_detects_an_edge():
|
||||
from remove_ai_watermarks._internal.qwen_zimage_pipeline import build_canny_control_image
|
||||
|
||||
source = np.zeros((64, 80, 3), dtype=np.uint8)
|
||||
source[:, 40:] = 255
|
||||
@@ -83,11 +85,21 @@ def test_canny_control_image_matches_reference_thresholds():
|
||||
assert result.shape == (64, 80, 3)
|
||||
assert np.array_equal(result[:, :, 0], result[:, :, 1])
|
||||
assert np.array_equal(result[:, :, 1], result[:, :, 2])
|
||||
assert result.max() == 255
|
||||
import cv2
|
||||
|
||||
expected = cv2.Canny(cv2.cvtColor(source, cv2.COLOR_RGB2GRAY), 13, 64)
|
||||
assert np.array_equal(result[:, :, 0], expected)
|
||||
|
||||
|
||||
def test_face_crop_geometry_preserves_calibrated_values():
|
||||
from remove_ai_watermarks._internal.qwen_zimage_pipeline import QwenZImagePipeline, _expanded_box
|
||||
|
||||
assert _expanded_box((100, 100, 200, 200), (500, 500)) == (25, 25, 275, 275)
|
||||
assert QwenZImagePipeline._detail_size((500, 400), (100, 80)) == (1024, 816)
|
||||
|
||||
|
||||
def test_yunet_download_targets_verified_lfs_artifact():
|
||||
from remove_ai_watermarks.noai.qwen_zimage_pipeline import (
|
||||
from remove_ai_watermarks._internal.qwen_zimage_pipeline import (
|
||||
YUNET_MODEL_SHA256,
|
||||
YUNET_MODEL_URL,
|
||||
YUNET_SCORE_THRESHOLD,
|
||||
@@ -95,14 +107,11 @@ def test_yunet_download_targets_verified_lfs_artifact():
|
||||
|
||||
assert YUNET_MODEL_URL.startswith("https://media.githubusercontent.com/media/opencv/opencv_zoo/")
|
||||
assert YUNET_MODEL_SHA256 == "8f2383e4dd3cfbb4553ea8718107fc0423210dc964f9f4280604804ed2552fa4"
|
||||
# YuNet scores are not calibrated like the upstream YOLO detector's scores.
|
||||
# A 0.2 YuNet threshold admitted background and decorative false positives,
|
||||
# multiplying the serial Z-Image face-stage cost on crowded scenes.
|
||||
assert pytest.approx(0.5) == YUNET_SCORE_THRESHOLD
|
||||
|
||||
|
||||
def test_resident_face_models_disable_vram_offload():
|
||||
from remove_ai_watermarks.noai.qwen_zimage_pipeline import (
|
||||
from remove_ai_watermarks._internal.qwen_zimage_pipeline import (
|
||||
QwenZImagePipeline,
|
||||
_pin_vram_managed_models,
|
||||
resolve_face_model_residency,
|
||||
@@ -154,7 +163,7 @@ def test_resident_face_models_disable_vram_offload():
|
||||
|
||||
|
||||
def test_static_prompt_cache_reuses_embeddings_without_caching_image_edits():
|
||||
from remove_ai_watermarks.noai.qwen_zimage_pipeline import _cache_static_prompt_embeddings
|
||||
from remove_ai_watermarks._internal.qwen_zimage_pipeline import _cache_static_prompt_embeddings
|
||||
|
||||
class PromptUnit:
|
||||
output_params = ("prompt_embeds",)
|
||||
@@ -186,7 +195,7 @@ def test_static_prompt_cache_reuses_embeddings_without_caching_image_edits():
|
||||
def test_sam_pixels_match_model_dtype_without_casting_boxes():
|
||||
import torch
|
||||
|
||||
from remove_ai_watermarks.noai.qwen_zimage_pipeline import _prepare_sam_inputs
|
||||
from remove_ai_watermarks._internal.qwen_zimage_pipeline import _prepare_sam_inputs
|
||||
|
||||
class Inputs(dict[str, torch.Tensor]):
|
||||
def to(self, device: str):
|
||||
@@ -206,7 +215,7 @@ def test_sam_pixels_match_model_dtype_without_casting_boxes():
|
||||
|
||||
|
||||
def test_sam_prompts_match_impact_center_and_clip_masks_to_boxes():
|
||||
from remove_ai_watermarks.noai.qwen_zimage_pipeline import (
|
||||
from remove_ai_watermarks._internal.qwen_zimage_pipeline import (
|
||||
_clip_sam_masks_to_boxes,
|
||||
_sam_point_prompts,
|
||||
)
|
||||
@@ -226,7 +235,7 @@ def test_sam_prompts_match_impact_center_and_clip_masks_to_boxes():
|
||||
|
||||
|
||||
def test_sam_proposal_selection_matches_impact_sub_threshold():
|
||||
from remove_ai_watermarks.noai.qwen_zimage_pipeline import _select_sam_masks
|
||||
from remove_ai_watermarks._internal.qwen_zimage_pipeline import _select_sam_masks
|
||||
|
||||
masks = np.zeros((2, 3, 8, 8), dtype=np.float32)
|
||||
masks[0, 0, 1:3, 1:3] = 1.0
|
||||
@@ -256,7 +265,7 @@ def test_sam_proposal_selection_matches_impact_sub_threshold():
|
||||
def test_sam_bfloat16_outputs_convert_to_numpy_float32():
|
||||
import torch
|
||||
|
||||
from remove_ai_watermarks.noai.qwen_zimage_pipeline import _sam_outputs_to_numpy
|
||||
from remove_ai_watermarks._internal.qwen_zimage_pipeline import _sam_outputs_to_numpy
|
||||
|
||||
masks = torch.ones((1, 2, 3, 4, 4), dtype=torch.bfloat16)
|
||||
scores = torch.tensor([[[0.95, 0.75, 0.50], [0.99, 0.80, 0.60]]], dtype=torch.bfloat16)
|
||||
@@ -269,7 +278,7 @@ def test_sam_bfloat16_outputs_convert_to_numpy_float32():
|
||||
|
||||
|
||||
def test_face_composite_preserves_every_pixel_outside_mask():
|
||||
from remove_ai_watermarks.noai.qwen_zimage_pipeline import composite_face
|
||||
from remove_ai_watermarks._internal.qwen_zimage_pipeline import composite_face
|
||||
|
||||
base = np.full((32, 32, 3), 10, dtype=np.uint8)
|
||||
detail = np.full((32, 32, 3), 240, dtype=np.uint8)
|
||||
@@ -284,7 +293,7 @@ def test_face_composite_preserves_every_pixel_outside_mask():
|
||||
|
||||
|
||||
def test_profile_defaults_to_four_global_steps():
|
||||
from remove_ai_watermarks.noai.watermark_profiles import (
|
||||
from remove_ai_watermarks._internal.watermark_profiles import (
|
||||
normalize_profile,
|
||||
resolve_seed,
|
||||
resolve_steps,
|
||||
@@ -305,7 +314,7 @@ def test_cli_exposes_qwen_zimage_profile():
|
||||
assert "qwen-zimage" in _PIPELINE_CHOICES
|
||||
|
||||
|
||||
def test_cli_qwen_zimage_keeps_upstream_postprocess_default(tmp_image_path, monkeypatch):
|
||||
def test_cli_qwen_zimage_keeps_profile_postprocess_default(tmp_image_path, monkeypatch):
|
||||
from remove_ai_watermarks import cli
|
||||
|
||||
mock_engine = MagicMock()
|
||||
@@ -338,7 +347,7 @@ def test_cli_qwen_zimage_keeps_upstream_postprocess_default(tmp_image_path, monk
|
||||
|
||||
|
||||
def test_watermark_remover_dispatches_to_full_pipeline(tmp_path, monkeypatch):
|
||||
from remove_ai_watermarks.noai.watermark_remover import WatermarkRemover
|
||||
from remove_ai_watermarks._internal.watermark_remover import WatermarkRemover
|
||||
|
||||
_mock_watermark_runtime_deps(monkeypatch)
|
||||
source = tmp_path / "source.png"
|
||||
@@ -364,7 +373,7 @@ def test_watermark_remover_dispatches_to_full_pipeline(tmp_path, monkeypatch):
|
||||
|
||||
|
||||
def test_watermark_remover_dispatches_qwen_tiling_to_full_pipeline(tmp_path, monkeypatch):
|
||||
from remove_ai_watermarks.noai.watermark_remover import WatermarkRemover
|
||||
from remove_ai_watermarks._internal.watermark_remover import WatermarkRemover
|
||||
|
||||
_mock_watermark_runtime_deps(monkeypatch)
|
||||
source = tmp_path / "source.png"
|
||||
@@ -395,11 +404,11 @@ def test_watermark_remover_dispatches_qwen_tiling_to_full_pipeline(tmp_path, mon
|
||||
|
||||
|
||||
def test_qwen_tiling_runs_global_tiles_then_one_full_frame_face_stage(monkeypatch):
|
||||
from remove_ai_watermarks.noai.qwen_zimage_pipeline import (
|
||||
from remove_ai_watermarks._internal.qwen_zimage_pipeline import (
|
||||
QwenZImagePipeline,
|
||||
resolution_adaptive_denoise,
|
||||
)
|
||||
from remove_ai_watermarks.noai.tiling import plan_tiles
|
||||
from remove_ai_watermarks._internal.tiling import plan_tiles
|
||||
|
||||
image = Image.new("RGB", (1500, 1500), (20, 30, 40))
|
||||
runtime = QwenZImagePipeline(device="cuda", torch_dtype="bf16")
|
||||
@@ -415,7 +424,7 @@ def test_qwen_tiling_runs_global_tiles_then_one_full_frame_face_stage(monkeypatc
|
||||
monkeypatch.setattr(runtime, "_run_global", fake_global)
|
||||
monkeypatch.setattr(runtime, "_run_faces", face_stage)
|
||||
monkeypatch.setattr(
|
||||
"remove_ai_watermarks.noai.qwen_zimage_pipeline.detect_faces",
|
||||
"remove_ai_watermarks._internal.qwen_zimage_pipeline.detect_faces",
|
||||
lambda _image: [(100, 100, 300, 300)],
|
||||
)
|
||||
monkeypatch.setattr(runtime, "_sam_masks", lambda _image, _boxes: [np.ones((1500, 1500), dtype=np.uint8)])
|
||||
@@ -435,9 +444,6 @@ def test_qwen_tiling_runs_global_tiles_then_one_full_frame_face_stage(monkeypatc
|
||||
assert all(strength == pytest.approx(resolution_adaptive_denoise(1500, 1500)) for _, strength, _ in global_calls)
|
||||
assert all(seed == 0 for _, _, seed in global_calls)
|
||||
face_stage.assert_called_once()
|
||||
# Keep this literal independent from the runtime helper: the port deliberately
|
||||
# uses half the upstream face denoise because it lacks the reference latent
|
||||
# noise-mask feather and uses a different sampler/runtime.
|
||||
assert face_stage.call_args.kwargs["strength"] == pytest.approx(0.0296296296)
|
||||
assert face_stage.call_args.args[0] is image
|
||||
assert face_stage.call_args.args[1].size == image.size
|
||||
@@ -445,7 +451,7 @@ def test_qwen_tiling_runs_global_tiles_then_one_full_frame_face_stage(monkeypatc
|
||||
|
||||
|
||||
def test_global_only_preload_skips_face_models(monkeypatch):
|
||||
from remove_ai_watermarks.noai.qwen_zimage_pipeline import QwenZImagePipeline
|
||||
from remove_ai_watermarks._internal.qwen_zimage_pipeline import QwenZImagePipeline
|
||||
|
||||
runtime = QwenZImagePipeline(device="cuda", torch_dtype="bf16")
|
||||
qwen = MagicMock()
|
||||
@@ -456,7 +462,7 @@ def test_global_only_preload_skips_face_models(monkeypatch):
|
||||
monkeypatch.setattr(runtime, "_load_zimage", zimage)
|
||||
monkeypatch.setattr(runtime, "_load_sam", sam)
|
||||
monkeypatch.setattr(
|
||||
"remove_ai_watermarks.noai.qwen_zimage_pipeline._yunet_model_path",
|
||||
"remove_ai_watermarks._internal.qwen_zimage_pipeline._yunet_model_path",
|
||||
yunet,
|
||||
)
|
||||
|
||||
@@ -469,7 +475,7 @@ def test_global_only_preload_skips_face_models(monkeypatch):
|
||||
|
||||
|
||||
def test_full_preload_still_loads_face_models(monkeypatch):
|
||||
from remove_ai_watermarks.noai.qwen_zimage_pipeline import QwenZImagePipeline
|
||||
from remove_ai_watermarks._internal.qwen_zimage_pipeline import QwenZImagePipeline
|
||||
|
||||
runtime = QwenZImagePipeline(device="cuda", torch_dtype="bf16")
|
||||
qwen = MagicMock()
|
||||
@@ -480,7 +486,7 @@ def test_full_preload_still_loads_face_models(monkeypatch):
|
||||
monkeypatch.setattr(runtime, "_load_zimage", zimage)
|
||||
monkeypatch.setattr(runtime, "_load_sam", sam)
|
||||
monkeypatch.setattr(
|
||||
"remove_ai_watermarks.noai.qwen_zimage_pipeline._yunet_model_path",
|
||||
"remove_ai_watermarks._internal.qwen_zimage_pipeline._yunet_model_path",
|
||||
yunet,
|
||||
)
|
||||
|
||||
@@ -493,7 +499,7 @@ def test_full_preload_still_loads_face_models(monkeypatch):
|
||||
|
||||
|
||||
def test_watermark_remover_forwards_global_only_preload(monkeypatch):
|
||||
from remove_ai_watermarks.noai.watermark_remover import WatermarkRemover
|
||||
from remove_ai_watermarks._internal.watermark_remover import WatermarkRemover
|
||||
|
||||
runtime = MagicMock()
|
||||
remover = WatermarkRemover.__new__(WatermarkRemover)
|
||||
@@ -506,7 +512,7 @@ def test_watermark_remover_forwards_global_only_preload(monkeypatch):
|
||||
|
||||
|
||||
def test_qwen_zimage_rejects_runtime_knobs_that_change_fixed_graph(tmp_path, monkeypatch):
|
||||
from remove_ai_watermarks.noai.watermark_remover import WatermarkRemover
|
||||
from remove_ai_watermarks._internal.watermark_remover import WatermarkRemover
|
||||
|
||||
_mock_watermark_runtime_deps(monkeypatch)
|
||||
with pytest.raises(ValueError, match="fixed Qwen-Image-2512"):
|
||||
@@ -517,7 +523,7 @@ def test_qwen_zimage_rejects_runtime_knobs_that_change_fixed_graph(tmp_path, mon
|
||||
remover = WatermarkRemover(device="cpu", pipeline="qwen-zimage")
|
||||
with pytest.raises(ValueError, match=r"CFG 1\.0"):
|
||||
remover.remove_watermark(source, guidance_scale=2.0)
|
||||
with pytest.raises(ValueError, match="4-step Lightning"):
|
||||
with pytest.raises(ValueError, match="requires 4 steps"):
|
||||
remover.remove_watermark(source, num_inference_steps=8)
|
||||
|
||||
|
||||
|
||||
@@ -20,7 +20,7 @@ import struct
|
||||
import tracemalloc
|
||||
|
||||
from remove_ai_watermarks import metadata
|
||||
from remove_ai_watermarks.noai import c2pa, isobmff
|
||||
from remove_ai_watermarks._internal import c2pa, isobmff
|
||||
|
||||
PNG_SIG = b"\x89PNG\r\n\x1a\n"
|
||||
_HUGE = 0x7FFFFFFF # ~2 GiB declared length on a tiny file
|
||||
|
||||
@@ -2,6 +2,8 @@
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from pathlib import Path
|
||||
|
||||
import pytest
|
||||
|
||||
from scripts.sync_conda_recipe import update_recipe
|
||||
@@ -39,3 +41,9 @@ def test_update_recipe_rejects_ambiguous_recipe() -> None:
|
||||
|
||||
with pytest.raises(ValueError, match="exactly one"):
|
||||
update_recipe(duplicate, version="2.0.0", sha256=_NEW_SHA)
|
||||
|
||||
|
||||
def test_repository_recipe_stays_metadata_only() -> None:
|
||||
recipe = Path("packaging/conda/recipe.yaml").read_text(encoding="utf-8")
|
||||
|
||||
assert " - av >=16\n" not in recipe
|
||||
|
||||
@@ -12,7 +12,7 @@ import numpy as np
|
||||
import pytest
|
||||
from PIL import Image
|
||||
|
||||
from remove_ai_watermarks.noai.tiling import (
|
||||
from remove_ai_watermarks._internal.tiling import (
|
||||
Tile,
|
||||
_axis_positions,
|
||||
feather_region_composite,
|
||||
|
||||
+2674
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,299 @@
|
||||
"""Regression tests for the video SynthID removal engine."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import sys
|
||||
import threading
|
||||
from types import SimpleNamespace
|
||||
from typing import TYPE_CHECKING, cast
|
||||
|
||||
import pytest
|
||||
|
||||
from remove_ai_watermarks import video_encoding, video_invisible
|
||||
from remove_ai_watermarks.video_synthid import DEFAULT_VIDEO_SYNTHID_NOISE_STD
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from pathlib import Path
|
||||
from typing import BinaryIO
|
||||
|
||||
|
||||
def test_encoder_redirects_large_stderr_while_frames_are_streaming(
|
||||
tmp_path: Path,
|
||||
caplog: pytest.LogCaptureFixture,
|
||||
) -> None:
|
||||
diagnostic = "synthetic ffmpeg diagnostic"
|
||||
tail_diagnostic = "synthetic ffmpeg diagnostic tail"
|
||||
caplog.set_level("INFO", logger=video_encoding.__name__)
|
||||
command = [
|
||||
sys.executable,
|
||||
"-c",
|
||||
(
|
||||
"import sys; "
|
||||
f"sys.stderr.buffer.write({diagnostic.encode()!r} + b'x' * 262144 + {tail_diagnostic.encode()!r}); "
|
||||
"sys.stderr.buffer.flush(); "
|
||||
"sys.stdin.buffer.read(); "
|
||||
"raise SystemExit(7)"
|
||||
),
|
||||
]
|
||||
encoder = video_encoding.start_raw_video_encoder(command)
|
||||
write_finished = threading.Event()
|
||||
write_errors: list[Exception] = []
|
||||
|
||||
def write_frames() -> None:
|
||||
try:
|
||||
encoder.stdin.write(b"f" * 262144)
|
||||
encoder.stdin.flush()
|
||||
except Exception as exc: # pragma: no cover - mutation cleanup path
|
||||
write_errors.append(exc)
|
||||
finally:
|
||||
write_finished.set()
|
||||
|
||||
writer = threading.Thread(target=write_frames)
|
||||
writer.start()
|
||||
try:
|
||||
assert write_finished.wait(5), "stderr backpressure blocked the frame producer"
|
||||
assert write_errors == []
|
||||
with pytest.raises(RuntimeError, match=diagnostic):
|
||||
video_encoding.finish_raw_video_encoder(
|
||||
encoder,
|
||||
tmp_path / "unused.mp4",
|
||||
operation="synthetic encode",
|
||||
)
|
||||
assert "ffmpeg stderr truncated" in caplog.text
|
||||
assert tail_diagnostic in caplog.text
|
||||
finally:
|
||||
video_encoding.abort_raw_video_encoder(encoder)
|
||||
writer.join(timeout=5)
|
||||
|
||||
|
||||
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,
|
||||
)
|
||||
|
||||
assert video_invisible.is_available() is False
|
||||
|
||||
|
||||
def test_regeneration_rejects_noise_outside_unit_interval(tmp_path: Path) -> None:
|
||||
with pytest.raises(ValueError, match="between 0 and 1"):
|
||||
video_invisible.regenerate_video_candidate(
|
||||
tmp_path / "source.mp4",
|
||||
tmp_path / "candidate.mp4",
|
||||
noise_std=1.01,
|
||||
)
|
||||
|
||||
|
||||
def test_encoder_and_mux_commands_separate_streaming_frames_from_source_audio(
|
||||
tmp_path: Path,
|
||||
monkeypatch: pytest.MonkeyPatch,
|
||||
) -> None:
|
||||
source = tmp_path / "source.mp4"
|
||||
output = tmp_path / "candidate.mp4"
|
||||
|
||||
monkeypatch.setattr(video_encoding.shutil, "which", lambda _name: "/usr/bin/ffmpeg")
|
||||
profile = video_encoding.VideoEncodeProfile(
|
||||
pixel_format="yuv420p",
|
||||
color_range="tv",
|
||||
color_space="bt709",
|
||||
color_transfer="bt709",
|
||||
color_primaries="bt709",
|
||||
time_base="1/90000",
|
||||
)
|
||||
|
||||
command = video_encoding.raw_video_command(
|
||||
output,
|
||||
width=8,
|
||||
height=8,
|
||||
fps=2.0,
|
||||
crf=18,
|
||||
profile=profile,
|
||||
)
|
||||
|
||||
metadata_index = command.index("-map_metadata")
|
||||
assert command[metadata_index + 1] == "-1"
|
||||
output_pixel_format_index = command.index("-pix_fmt", command.index("-c:v"))
|
||||
assert command[output_pixel_format_index + 1] == "yuv420p"
|
||||
assert command[command.index("-color_range") + 1] == "tv"
|
||||
assert command[command.index("-colorspace") + 1] == "bt709"
|
||||
assert command[command.index("-color_trc") + 1] == "bt709"
|
||||
assert command[command.index("-color_primaries") + 1] == "bt709"
|
||||
assert command[command.index("-enc_time_base:v") + 1] == "1/90000"
|
||||
assert command[command.index("-video_track_timescale") + 1] == "90000"
|
||||
assert command[command.index("-x264-params") + 1] == (
|
||||
"colorprim=bt709:transfer=bt709:colormatrix=bt709:range=limited"
|
||||
)
|
||||
assert "pipe:0" in command
|
||||
assert str(source) not in command
|
||||
assert command[command.index("-map") + 1] == "0:v:0"
|
||||
assert command[command.index("-fps_mode") + 1] == "passthrough"
|
||||
assert "-shortest" not in command
|
||||
|
||||
calls: list[list[str]] = []
|
||||
|
||||
def fake_run(mux_command: list[str], **_kwargs: object) -> SimpleNamespace:
|
||||
calls.append(mux_command)
|
||||
return SimpleNamespace(returncode=0, stdout="", stderr="")
|
||||
|
||||
monkeypatch.setattr(video_encoding.subprocess, "run", fake_run)
|
||||
encoded_video = tmp_path / "encoded.mp4"
|
||||
video_encoding.mux_encoded_video(
|
||||
encoded_video,
|
||||
source,
|
||||
output,
|
||||
strip_metadata=True,
|
||||
)
|
||||
|
||||
mux_command = calls[0]
|
||||
assert mux_command.index(str(encoded_video)) < mux_command.index(str(source))
|
||||
assert mux_command[mux_command.index("-map") + 1] == "0:v:0"
|
||||
second_map = mux_command.index("-map", mux_command.index("-map") + 1)
|
||||
assert mux_command[second_map + 1] == "1:a?"
|
||||
assert mux_command[mux_command.index("-c") + 1] == "copy"
|
||||
assert mux_command[mux_command.index("-map_metadata") + 1] == "-1"
|
||||
assert "-shortest" not in mux_command
|
||||
|
||||
|
||||
def test_mux_reports_bounded_disk_backed_stderr(
|
||||
tmp_path: Path,
|
||||
monkeypatch: pytest.MonkeyPatch,
|
||||
caplog: pytest.LogCaptureFixture,
|
||||
) -> None:
|
||||
diagnostic = b"synthetic mux diagnostic"
|
||||
tail_diagnostic = b"synthetic mux diagnostic tail"
|
||||
caplog.set_level("INFO", logger=video_encoding.__name__)
|
||||
monkeypatch.setattr(video_encoding.shutil, "which", lambda _name: "/usr/bin/ffmpeg")
|
||||
|
||||
def fake_run(_command: list[str], **kwargs: object) -> SimpleNamespace:
|
||||
stderr = cast("BinaryIO", kwargs["stderr"])
|
||||
stderr.write(diagnostic + b"x" * 262144 + tail_diagnostic)
|
||||
stderr.flush()
|
||||
return SimpleNamespace(returncode=7)
|
||||
|
||||
monkeypatch.setattr(video_encoding.subprocess, "run", fake_run)
|
||||
|
||||
with pytest.raises(RuntimeError, match=diagnostic.decode()):
|
||||
video_encoding.mux_encoded_video(
|
||||
tmp_path / "encoded.mp4",
|
||||
tmp_path / "source.mp4",
|
||||
tmp_path / "output.mp4",
|
||||
strip_metadata=True,
|
||||
)
|
||||
|
||||
assert "ffmpeg stderr truncated" in caplog.text
|
||||
assert tail_diagnostic.decode() in caplog.text
|
||||
|
||||
|
||||
def test_timestamped_encoder_reads_nut_and_passes_pts_through(
|
||||
tmp_path: Path,
|
||||
monkeypatch: pytest.MonkeyPatch,
|
||||
) -> None:
|
||||
output = tmp_path / "candidate.mp4"
|
||||
monkeypatch.setattr(video_encoding.shutil, "which", lambda _name: "/usr/bin/ffmpeg")
|
||||
|
||||
command = video_encoding.raw_video_command(
|
||||
output,
|
||||
width=8,
|
||||
height=8,
|
||||
fps=24.0,
|
||||
crf=18,
|
||||
profile=video_encoding.VideoEncodeProfile(time_base="1/90000"),
|
||||
timestamped_input=True,
|
||||
copy_input_timestamps=True,
|
||||
)
|
||||
|
||||
assert "-copyts" in command
|
||||
assert command[command.index("-f") : command.index("-f") + 4] == [
|
||||
"-f",
|
||||
"nut",
|
||||
"-i",
|
||||
"pipe:0",
|
||||
]
|
||||
assert command[command.index("-fps_mode") + 1] == "passthrough"
|
||||
assert command[command.index("-avoid_negative_ts") + 1] == "disabled"
|
||||
assert command[command.index("-enc_time_base:v") + 1] == "1/90000"
|
||||
|
||||
|
||||
def test_probe_encode_profile_preserves_supported_8_bit_properties(
|
||||
tmp_path: Path,
|
||||
monkeypatch: pytest.MonkeyPatch,
|
||||
) -> None:
|
||||
source = tmp_path / "source.mp4"
|
||||
source.write_bytes(b"video")
|
||||
monkeypatch.setattr(video_encoding.shutil, "which", lambda _name: "/usr/bin/ffprobe")
|
||||
monkeypatch.setattr(
|
||||
video_encoding.subprocess,
|
||||
"run",
|
||||
lambda *_args, **_kwargs: SimpleNamespace(
|
||||
returncode=0,
|
||||
stdout=(
|
||||
'{"streams":[{"pix_fmt":"yuvj422p","color_range":"tv",'
|
||||
'"color_space":"bt709","color_transfer":"bt709",'
|
||||
'"color_primaries":"bt709","time_base":"2/180000",'
|
||||
'"start_pts":180000,"bits_per_raw_sample":"8"}]}'
|
||||
),
|
||||
stderr="",
|
||||
),
|
||||
)
|
||||
|
||||
profile = video_encoding.probe_video_encode_profile(source)
|
||||
|
||||
assert profile == video_encoding.VideoEncodeProfile(
|
||||
pixel_format="yuv422p",
|
||||
color_range="tv",
|
||||
color_space="bt709",
|
||||
color_transfer="bt709",
|
||||
color_primaries="bt709",
|
||||
time_base="1/90000",
|
||||
start_pts=180000,
|
||||
source_pixel_format="yuvj422p",
|
||||
component_depth=8,
|
||||
)
|
||||
|
||||
|
||||
def test_probe_encode_profile_uses_compatible_defaults_without_ffprobe(
|
||||
tmp_path: Path,
|
||||
monkeypatch: pytest.MonkeyPatch,
|
||||
) -> None:
|
||||
monkeypatch.setattr(video_encoding.shutil, "which", lambda _name: None)
|
||||
|
||||
assert video_encoding.probe_video_encode_profile(tmp_path / "source.mp4") == (video_encoding.VideoEncodeProfile())
|
||||
|
||||
|
||||
def test_probe_video_timestamps_uses_best_effort_pts(
|
||||
tmp_path: Path,
|
||||
monkeypatch: pytest.MonkeyPatch,
|
||||
) -> None:
|
||||
source = tmp_path / "source.mp4"
|
||||
source.write_bytes(b"video")
|
||||
monkeypatch.setattr(video_encoding.shutil, "which", lambda _name: "/usr/bin/ffprobe")
|
||||
monkeypatch.setattr(
|
||||
video_encoding.subprocess,
|
||||
"run",
|
||||
lambda *_args, **_kwargs: SimpleNamespace(
|
||||
returncode=0,
|
||||
stdout="0.000000\n0.041667\n",
|
||||
stderr="",
|
||||
),
|
||||
)
|
||||
|
||||
assert video_encoding.probe_video_timestamps(source) == (0.0, 0.041667)
|
||||
|
||||
|
||||
def test_default_noise_matches_full_clip_oracle_floor() -> None:
|
||||
assert DEFAULT_VIDEO_SYNTHID_NOISE_STD == 0.15
|
||||
|
||||
|
||||
def test_stream_batches_consumes_only_one_batch_ahead() -> None:
|
||||
consumed: list[int] = []
|
||||
|
||||
def values():
|
||||
for value in range(5):
|
||||
consumed.append(value)
|
||||
yield value
|
||||
|
||||
batches = video_invisible._stream_batches(values(), 2)
|
||||
|
||||
assert next(iter(batches)) == [0, 1]
|
||||
assert consumed == [0, 1]
|
||||
@@ -0,0 +1,67 @@
|
||||
"""Pure regression tests for the oracle-gated video SynthID experiment."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import importlib.util
|
||||
from pathlib import Path
|
||||
from typing import TYPE_CHECKING
|
||||
|
||||
import numpy as np
|
||||
import pytest
|
||||
import torch
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from types import ModuleType
|
||||
|
||||
_SCRIPT = Path(__file__).parent.parent / "scripts" / "video_synthid_sweep.py"
|
||||
|
||||
|
||||
@pytest.fixture(scope="module")
|
||||
def sweep() -> ModuleType:
|
||||
spec = importlib.util.spec_from_file_location("video_synthid_sweep", _SCRIPT)
|
||||
assert spec is not None
|
||||
assert spec.loader is not None
|
||||
module = importlib.util.module_from_spec(spec)
|
||||
spec.loader.exec_module(module)
|
||||
return module
|
||||
|
||||
|
||||
def test_fit_size_preserves_landscape_aspect_and_vae_alignment(sweep: ModuleType) -> None:
|
||||
assert sweep._fit_size(1280, 720, 512) == (512, 288)
|
||||
|
||||
|
||||
def test_fit_size_rejects_invalid_dimensions(sweep: ModuleType) -> None:
|
||||
with pytest.raises(ValueError, match="positive"):
|
||||
sweep._fit_size(0, 720, 512)
|
||||
|
||||
|
||||
def test_shared_latent_noise_is_one_spatial_field(sweep: ModuleType) -> None:
|
||||
noise = sweep._shared_latent_noise(
|
||||
(4, 8, 8),
|
||||
seed=7,
|
||||
device="cpu",
|
||||
dtype=torch.float32,
|
||||
)
|
||||
assert noise.shape == (1, 4, 8, 8)
|
||||
|
||||
|
||||
def test_shared_latent_noise_is_seeded(sweep: ModuleType) -> None:
|
||||
first = sweep._shared_latent_noise((4, 8, 8), seed=7, device="cpu", dtype=torch.float32)
|
||||
repeated = sweep._shared_latent_noise((4, 8, 8), seed=7, device="cpu", dtype=torch.float32)
|
||||
other = sweep._shared_latent_noise((4, 8, 8), seed=8, device="cpu", dtype=torch.float32)
|
||||
assert torch.equal(first, repeated)
|
||||
assert not torch.equal(first, other)
|
||||
|
||||
|
||||
def test_psnr_is_infinite_for_identical_frames(sweep: ModuleType) -> None:
|
||||
frame = np.full((2, 8, 8, 3), 120, dtype=np.uint8)
|
||||
assert sweep.paired_psnr(frame, frame.copy()) == pytest.approx(float("inf"))
|
||||
|
||||
|
||||
def test_temporal_residual_ratio_is_one_for_identical_sequences(sweep: ModuleType) -> None:
|
||||
first = np.zeros((32, 32, 3), dtype=np.uint8)
|
||||
second = first.copy()
|
||||
second[:, 8:16] = 80
|
||||
sequence = [first, second]
|
||||
maps, baseline = sweep.build_temporal_reference(sequence)
|
||||
assert sweep.temporal_residual_ratio([frame.copy() for frame in sequence], maps, baseline) == pytest.approx(1.0)
|
||||
@@ -0,0 +1,112 @@
|
||||
"""Regression tests for motion-compensated visible-video fill."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
|
||||
from remove_ai_watermarks.video_temporal import stabilize_filled_frame
|
||||
|
||||
|
||||
def _translated_pair() -> tuple[np.ndarray, np.ndarray]:
|
||||
rng = np.random.default_rng(7)
|
||||
previous = rng.integers(0, 256, (96, 128, 3), dtype=np.uint8)
|
||||
previous = cv2.GaussianBlur(previous, (5, 5), 0)
|
||||
current = cv2.warpAffine(
|
||||
previous,
|
||||
np.float32(((1, 0, 2), (0, 1, 1))),
|
||||
(128, 96),
|
||||
borderMode=cv2.BORDER_REFLECT,
|
||||
)
|
||||
return previous, current
|
||||
|
||||
|
||||
def test_motion_aligned_prior_reduces_independent_fill_error() -> None:
|
||||
previous, current = _translated_pair()
|
||||
mask = np.zeros(current.shape[:2], dtype=np.uint8)
|
||||
mask[30:66, 45:85] = 255
|
||||
rng = np.random.default_rng(11)
|
||||
current_fill = current.copy()
|
||||
noise = rng.normal(0, 18, (36, 40, 3))
|
||||
current_fill[30:66, 45:85] = np.clip(
|
||||
current_fill[30:66, 45:85].astype(np.float32) + noise,
|
||||
0,
|
||||
255,
|
||||
).astype(np.uint8)
|
||||
|
||||
stabilized = stabilize_filled_frame(
|
||||
previous,
|
||||
previous,
|
||||
mask,
|
||||
current,
|
||||
current_fill,
|
||||
mask,
|
||||
)
|
||||
|
||||
hole = mask > 0
|
||||
before = float(np.mean((current_fill[hole].astype(np.float32) - current[hole]) ** 2))
|
||||
after = float(np.mean((stabilized[hole].astype(np.float32) - current[hole]) ** 2))
|
||||
assert after < before * 0.5
|
||||
assert np.array_equal(stabilized[~hole], current_fill[~hole])
|
||||
|
||||
|
||||
def test_owned_fill_can_be_stabilized_without_full_frame_copy() -> None:
|
||||
previous, current = _translated_pair()
|
||||
mask = np.zeros(current.shape[:2], dtype=np.uint8)
|
||||
mask[30:66, 45:85] = 255
|
||||
current_fill = current.copy()
|
||||
current_fill[mask > 0] = 127
|
||||
|
||||
stabilized = stabilize_filled_frame(
|
||||
previous,
|
||||
previous,
|
||||
mask,
|
||||
current,
|
||||
current_fill,
|
||||
mask,
|
||||
copy=False,
|
||||
)
|
||||
|
||||
assert stabilized is current_fill
|
||||
|
||||
|
||||
def test_scene_cut_keeps_independent_current_fill() -> None:
|
||||
previous, _current = _translated_pair()
|
||||
rng = np.random.default_rng(13)
|
||||
current = rng.integers(0, 256, previous.shape, dtype=np.uint8)
|
||||
mask = np.zeros(current.shape[:2], dtype=np.uint8)
|
||||
mask[30:66, 45:85] = 255
|
||||
current_fill = current.copy()
|
||||
current_fill[mask > 0] = 0
|
||||
|
||||
stabilized = stabilize_filled_frame(
|
||||
previous,
|
||||
previous,
|
||||
mask,
|
||||
current,
|
||||
current_fill,
|
||||
mask,
|
||||
)
|
||||
|
||||
assert np.array_equal(stabilized, current_fill)
|
||||
|
||||
|
||||
def test_disjoint_prior_mask_cannot_reintroduce_old_mark() -> None:
|
||||
previous, current = _translated_pair()
|
||||
previous_mask = np.zeros(current.shape[:2], dtype=np.uint8)
|
||||
previous_mask[8:24, 8:24] = 255
|
||||
current_mask = np.zeros(current.shape[:2], dtype=np.uint8)
|
||||
current_mask[60:76, 96:112] = 255
|
||||
current_fill = current.copy()
|
||||
current_fill[current_mask > 0] = 127
|
||||
|
||||
stabilized = stabilize_filled_frame(
|
||||
previous,
|
||||
previous,
|
||||
previous_mask,
|
||||
current,
|
||||
current_fill,
|
||||
current_mask,
|
||||
)
|
||||
|
||||
assert np.array_equal(stabilized, current_fill)
|
||||
@@ -4,7 +4,7 @@ from __future__ import annotations
|
||||
|
||||
import pytest
|
||||
|
||||
from remove_ai_watermarks.noai.watermark_profiles import resolve_strength, viable_steps
|
||||
from remove_ai_watermarks._internal.watermark_profiles import resolve_strength, viable_steps
|
||||
|
||||
|
||||
class TestViableSteps:
|
||||
|
||||
@@ -255,6 +255,111 @@ wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/64/b4/17d4b0b2a2dc85a6df63d1157e028ed19f90d4cd97c36717afef2bc2f395/attrs-26.1.0-py3-none-any.whl", hash = "sha256:c647aa4a12dfbad9333ca4e71fe62ddc36f4e63b2d260a37a8b83d2f043ac309", size = 67548, upload-time = "2026-03-19T14:22:23.645Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "av"
|
||||
version = "16.1.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
resolution-markers = [
|
||||
"python_full_version < '3.11' and sys_platform == 'darwin'",
|
||||
"python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform == 'linux'",
|
||||
"(python_full_version < '3.11' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux')",
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/78/cd/3a83ffbc3cc25b39721d174487fb0d51a76582f4a1703f98e46170ce83d4/av-16.1.0.tar.gz", hash = "sha256:a094b4fd87a3721dacf02794d3d2c82b8d712c85b9534437e82a8a978c175ffd", size = 4285203, upload-time = "2026-01-11T07:31:33.772Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/97/51/2217a9249409d2e88e16e3f16f7c0def9fd3e7ffc4238b2ec211f9935bdb/av-16.1.0-cp310-cp310-macosx_11_0_x86_64.whl", hash = "sha256:2395748b0c34fe3a150a1721e4f3d4487b939520991b13e7b36f8926b3b12295", size = 26942590, upload-time = "2026-01-09T20:17:58.588Z" },
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@@ -2636,7 +2741,7 @@ wheels = [
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@@ -2881,7 +2986,7 @@ resolution-markers = [
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{ name = "numpy" },
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@@ -3200,6 +3305,8 @@ dependencies = [
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[[package]]
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Reference in New Issue
Block a user