mirror of
https://github.com/wiltodelta/remove-ai-watermarks.git
synced 2026-08-06 22:18:36 +02:00
Release 0.22.0 with composable feature extras
This commit is contained in:
Victor Kuznetsov
@@ -32,9 +32,24 @@ Remove AI provenance marks from images you generated yourself:
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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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## Installation modes
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| Need | Install |
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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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| 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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Lower-level and specialized extras include `pixels`, `heif`, `trustmark`,
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`migan`, `lama`, `esrgan`, and `qwen-zimage`. The
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[installation guide](docs/installation.md#feature-extras) documents their exact
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dependency composition and model requirements.
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## Quick start
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Install the core CLI:
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Install the metadata-focused default CLI:
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```bash
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uv tool install remove-ai-watermarks
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@@ -46,7 +61,13 @@ Inspect an image:
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remove-ai-watermarks identify image.png
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```
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Remove a known visible mark and AI metadata:
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For visible watermark removal, install the pixel dependencies:
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```bash
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uv tool install --force "remove-ai-watermarks[visible]"
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```
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Then remove a known visible mark and AI metadata:
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```bash
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remove-ai-watermarks visible image.png -o clean.png
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@@ -61,7 +82,7 @@ remove-ai-watermarks metadata image.png --remove -o clean.png
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For invisible watermark removal, install the diffusion dependencies:
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```bash
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uv tool install --force "remove-ai-watermarks[gpu]"
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uv tool install --force "remove-ai-watermarks[diffusion]"
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remove-ai-watermarks invisible image.png -o clean.png
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```
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@@ -129,8 +150,9 @@ remove-ai-watermarks erase image.png \
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### Use a learned fill backend
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The core install uses OpenCV inpainting when no learned backend is installed.
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For more difficult backgrounds:
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The `visible` extra uses OpenCV inpainting when no learned backend is installed.
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For more difficult backgrounds, the learned-backend extras include the same
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pixel dependencies automatically:
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```bash
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uv tool install --force "remove-ai-watermarks[migan]"
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@@ -197,6 +219,8 @@ See [supported signals](docs/supported-signals.md) and
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## Python API
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The visible-removal API requires `remove-ai-watermarks[visible]`.
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```python
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import remove_ai_watermarks as raiw
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+33
-5
@@ -9,15 +9,35 @@ remove-ai-watermarks [OPTIONS] COMMAND [ARGS]
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Run `remove-ai-watermarks COMMAND --help` for the complete option list and
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defaults. This page focuses on choosing the right command.
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## Command dependency map
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| Command or signal | Required installation |
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| --- | --- |
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| `metadata` and metadata-only `identify` | Default package |
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| Visible signals in `identify` | `remove-ai-watermarks[visible]` (`pixels` is the minimal runtime) |
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| Open DWT-DCT signals in `identify` | `remove-ai-watermarks[detect]` |
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| Adobe TrustMark signals in `identify` | `remove-ai-watermarks[trustmark]` |
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| `visible` and `erase` with OpenCV | `remove-ai-watermarks[visible]` (`pixels` is the minimal runtime) |
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| `visible` or `erase` with MI-GAN | `remove-ai-watermarks[migan]` |
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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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| 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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`batch` requires the same extra as its selected mode. Extras can be combined in
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one installation, for example `remove-ai-watermarks[visible,detect,heif]`.
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## Inspect an image
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```bash
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remove-ai-watermarks identify image.png
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```
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`identify` combines supported metadata and pixel signals into one provenance
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report. When no signal is found, it reports the origin as unknown. It does not
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claim the image is clean.
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`identify` always inspects supported metadata. When pixel extras are installed,
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it also evaluates supported visible and invisible pixel signals. When no signal
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is found, it reports the origin as unknown. It does not claim the image is
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clean.
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Machine readable output:
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@@ -36,6 +56,8 @@ invisible pixel detectors. Metadata inspection still runs.
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## Remove known visible marks
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Install `remove-ai-watermarks[visible]` before using `visible` or `erase`.
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```bash
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remove-ai-watermarks visible image.png -o clean.png
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```
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@@ -130,7 +152,7 @@ non-ISOBMFF audio and video path.
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Install the diffusion dependencies first:
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```bash
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uv tool install --force "remove-ai-watermarks[gpu]"
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uv tool install --force "remove-ai-watermarks[diffusion]"
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```
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Then run:
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@@ -194,6 +216,12 @@ It is a memory strategy, not a guarantee of better quality.
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## Run the full pipeline
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The `all` command and the `all` installation extra are separate concepts. The
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command runs every applicable stage. Installing `remove-ai-watermarks[all]`
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makes every production backend available; a smaller installation such as
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`remove-ai-watermarks[visible,diffusion]` can also run the command with fewer
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optional backends.
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```bash
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remove-ai-watermarks all image.png -o clean.png
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```
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@@ -206,7 +234,7 @@ The command runs:
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The visible options and diffusion options are also available on `all`.
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If diffusion is required but the `gpu` extra is unavailable, `all` still
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If diffusion is required but the `diffusion` extra is unavailable, `all` still
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writes the result of the visible and metadata stages, prints a prominent
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warning, and exits with code 1. This prevents a partial result from being
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reported as complete.
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+3
-3
@@ -6,15 +6,15 @@ Read this reference for environment setup, dependency recovery, CI behavior, and
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- Use `uv sync --frozen --extra dev` and add only the feature extras needed for the task.
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- Do not use `uv pip install` for development tools. It can re-resolve `uv.lock` outside the compatible ML dependency set.
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- A core-only sync removes GPU packages by design. Package imports remain light through lazy exports; only removal paths should require the heavy stack.
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- On an unreliable connection, sync the needed `dev` and `gpu` extras and run the lint, type, and test commands directly instead of downloading every optional learned backend.
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- A default-only sync removes every pixel and model package by design. Package imports remain light through lazy exports.
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- On an unreliable connection, sync `dev` plus only the required feature extras, such as `diffusion`, and run the checks directly instead of downloading every optional learned backend.
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- Run `uv` from the repository root or it may create a bare environment without the project dependencies.
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The optional TrustMark decoder downloads weights into its installed package directory. After pruning that extra, a leftover weights directory can make availability checks see an empty namespace package. If Pyright reports an unknown `TrustMark` import and `find_spec("trustmark")` returns a loader-less spec, remove that regenerable remnant from the active virtual environment and resync.
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## CI
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`.github/workflows/test.yml` runs Ruff and a cross-platform supported-Python test matrix with core plus development dependencies. GPU and model-running tests skip in that matrix; metadata, identification, visible removal, and the OpenCV eraser remain covered across operating systems.
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`.github/workflows/test.yml` runs Ruff and a cross-platform supported-Python test matrix with default plus development dependencies. Diffusion and model-running tests skip in that matrix; metadata, identification, visible removal, the DWT-DCT decoder, and the OpenCV eraser remain covered across operating systems.
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Keep `uv.lock` compatible with `uv sync --frozen`. Dependency pull-request checks use GitHub's merge result against current `main`; if `main` moves, merge it locally and rerun the full gate because a newer linter can expose stale directives in later code.
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+83
-21
@@ -2,15 +2,17 @@
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Python 3.10.1 or newer is required.
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## Core install
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## Default metadata mode
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The core package provides:
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The default package provides:
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- provenance inspection;
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- visible watermark removal with OpenCV;
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- manual region erasing with OpenCV;
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- AI metadata inspection and removal.
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It installs Pillow, piexif, and c2pa-python for reading metadata directly from
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files. It does not install NumPy, OpenCV, pillow-heif, Torch, diffusion models,
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or invisible-watermark decoders.
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Install it as an isolated command with uv:
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```bash
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@@ -29,12 +31,27 @@ You can also install the Homebrew package on macOS or Linux:
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brew install wiltodelta/tap/remove-ai-watermarks
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```
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## Invisible watermark removal
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## Visible watermark removal
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Diffusion based removal needs the `gpu` extra:
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Visible mark detection, OpenCV inpainting, and manual region erasing need the
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`visible` extra:
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```bash
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uv tool install --force "remove-ai-watermarks[gpu]"
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uv tool install --force "remove-ai-watermarks[visible]"
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```
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Add `heif` only when the pixel path must decode HEIC, HEIF, or AVIF:
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```bash
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uv tool install --force "remove-ai-watermarks[visible,heif]"
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```
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## Invisible watermark removal
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Diffusion based removal needs the `diffusion` extra:
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```bash
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uv tool install --force "remove-ai-watermarks[diffusion]"
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```
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The code supports CUDA, XPU, MPS, and CPU devices. A GPU is recommended because
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@@ -46,28 +63,73 @@ For the CUDA only Qwen Image plus Z-Image profile:
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uv tool install --force "remove-ai-watermarks[qwen-zimage]"
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```
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The `qwen-zimage` extra includes the normal `gpu` dependencies.
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The `qwen-zimage` extra includes the normal `diffusion` dependencies.
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## Optional features
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## Feature extras
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Install only what you need:
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Extras are composable. Install only the capabilities and file formats the
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application actually uses:
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| Extra | Adds |
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| --- | --- |
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| `migan` | MI-GAN ONNX fill backend |
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| `lama` | big-LaMa ONNX fill backend |
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| `detect` | Open DWT-DCT watermark decoder used by `identify` |
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| `trustmark` | Adobe TrustMark decoder |
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| `esrgan` | Real-ESRGAN upscaling before diffusion |
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| `qwen-zimage` | CUDA only Qwen Image plus Z-Image pipeline |
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| Extra | Capability | Automatically includes | Torch or model download |
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| --- | --- | --- | --- |
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| `pixels` | Shared BGR array and image-processing runtime | NumPy, headless OpenCV | No |
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| `heif` | HEIC, HEIF, and AVIF pixel decoding | pillow-heif | No |
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| `visible` | Visible mark detection, OpenCV inpainting, and manual erasing | `pixels` | No |
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| `detect` | Open DWT-DCT detection for Stable Diffusion, SDXL, and FLUX | `pixels`, PyWavelets | No |
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| `trustmark` | Adobe TrustMark detection | trustmark | Yes |
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| `diffusion` | Diffusion-based invisible watermark removal | `pixels`, Torch, Diffusers | Yes |
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| `migan` | MI-GAN ONNX fill backend | `visible`, ONNX Runtime | Model download, no Torch |
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| `lama` | big-LaMa ONNX fill backend | `visible`, ONNX Runtime | Model download, no Torch |
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| `esrgan` | Real-ESRGAN upscaling before diffusion | `pixels`, spandrel | Yes |
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| `qwen-zimage` | CUDA-only Qwen Image plus Z-Image pipeline | `diffusion`, DiffSynth | Yes |
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| `all` | Every production feature | All rows above | Yes |
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| `dev` | Tests, linting, typing, and upstream parity checks | `visible`, `detect`, upstream invisible-watermark | Yes, for parity tests |
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Example:
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Dependency composition:
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```mermaid
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flowchart LR
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visible --> pixels
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detect --> pixels
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diffusion --> pixels
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migan --> visible
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lama --> visible
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esrgan --> pixels
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qwen["qwen-zimage"] --> diffusion
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heif
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trustmark
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```
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`heif` and `trustmark` are independent branches. Combine them explicitly with
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another feature when required. The `all` bundle contains every production
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branch but never includes `dev`.
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Examples:
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```bash
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# Metadata plus torch-free DWT-DCT detection
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uv tool install --force "remove-ai-watermarks[detect]"
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# Visible removal with HEIC/AVIF support and MI-GAN
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uv tool install --force "remove-ai-watermarks[migan,heif]"
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# DWT-DCT and TrustMark detection without diffusion removal
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uv tool install --force "remove-ai-watermarks[detect,trustmark]"
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# Every production capability
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uv tool install --force "remove-ai-watermarks[all]"
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# An arbitrary minimal combination
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uv tool install --force "remove-ai-watermarks[migan,detect]"
|
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```
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Some optional models download their weights on first use.
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`heif` stays independent so applications that only process PNG, JPEG, or WebP
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do not install libheif. `detect` uses the in-tree torch-free decoder and does
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not install the upstream `invisible-watermark` package. Optional models download
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their weights on first use.
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The old `gpu` and `remove` aliases are intentionally not provided. Use
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`diffusion` and `visible` respectively.
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## Install from the repository
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@@ -81,7 +143,7 @@ Add the feature groups required for your work:
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```bash
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uv sync --frozen --extra dev
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uv sync --frozen --extra dev --extra gpu
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uv sync --frozen --extra dev --extra diffusion
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```
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Run commands from the repository root:
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@@ -11,7 +11,8 @@ superseded experiments live in the research archive listed in
|
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Visible removal changes only the selected mask, but the hidden pixels still
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have to be reconstructed.
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- OpenCV is fast and dependency free. It works well on flat backgrounds but
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- OpenCV is fast and requires no model download. It works well on flat
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backgrounds but
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can smear texture or repeated structure.
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- MI-GAN is a lighter learned backend. It can improve natural texture but may
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ghost or invent structure.
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@@ -162,11 +163,13 @@ The metadata path recognizes JPEG XL containers, but the visible and diffusion
|
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image paths do not list `.jxl` as a supported pixel format because the package
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does not include a JPEG XL pixel decoder.
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### HEIC, HEIF, and AVIF use a Pillow fallback
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### HEIC, HEIF, and AVIF pixel decoding uses an optional Pillow fallback
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OpenCV does not decode these formats in the project. `image_io.imread` falls
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back to Pillow with `pillow-heif`. A corrupt or truncated file may still fail to
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decode.
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back to Pillow with `pillow-heif` when the `heif` extra is installed alongside
|
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a pixel feature. The
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default metadata path scans these containers without that plugin. A corrupt or
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truncated file may still fail to decode.
|
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|
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### Some metadata removal requires ffmpeg
|
||||
|
||||
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@@ -144,6 +144,11 @@ metadata extraction from verdict logic:
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- `identify` preserves the path-based API and adds the optional registered
|
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visible-mark and open invisible-watermark decoders after extraction.
|
||||
|
||||
The `detect` extra composes the shared `pixels` runtime with PyWavelets. Its
|
||||
in-tree [`dwt_dct.py`](../src/remove_ai_watermarks/dwt_dct.py) decoder preserves
|
||||
the upstream matrix algorithm without installing Torch or non-headless OpenCV.
|
||||
The upstream MIT notice ships inside the wheel under `licenses/`.
|
||||
|
||||
`is_ai_generated` is `True` or `None`; absence of evidence is not reported as a
|
||||
human-made verdict. `ai_source_kind` distinguishes fully generated content from
|
||||
AI-enhanced composites when the source metadata provides that distinction.
|
||||
@@ -379,7 +384,8 @@ Contracts:
|
||||
- `to_bgr` normalizes grayscale and alpha-bearing arrays.
|
||||
- `read_bgr_and_alpha` and `write_bgr_with_alpha` preserve the alpha plane.
|
||||
- `imwrite` returns a success flag; every caller must check it.
|
||||
- HEIC, HEIF, and AVIF fall back to Pillow plus `pillow-heif`.
|
||||
- HEIC, HEIF, and AVIF pixel reads fall back to Pillow plus `pillow-heif` from
|
||||
the independent `heif` extra. Metadata scanning does not require that plugin.
|
||||
- A visible no-op can preserve the original file bytes.
|
||||
|
||||
Regression coverage:
|
||||
|
||||
@@ -3,8 +3,17 @@
|
||||
Use the high level API for normal application integration. Low level detector
|
||||
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).
|
||||
|
||||
## Remove visible marks
|
||||
|
||||
Install `remove-ai-watermarks[visible]` before using the visible-removal API.
|
||||
|
||||
```python
|
||||
import remove_ai_watermarks as raiw
|
||||
|
||||
@@ -68,6 +77,9 @@ result, removed = raiw.remove_visible(image, backend="cv2")
|
||||
|
||||
## Inspect provenance
|
||||
|
||||
The default installation evaluates file metadata. Add `visible`, `detect`, or
|
||||
`trustmark` to enable the corresponding optional pixel signals.
|
||||
|
||||
Get the vendor keys used by visible removal:
|
||||
|
||||
```python
|
||||
@@ -172,6 +184,9 @@ as proof that metadata was removed.
|
||||
|
||||
## Remove invisible watermarks
|
||||
|
||||
Install `remove-ai-watermarks[diffusion]` for the standard pipelines or
|
||||
`remove-ai-watermarks[qwen-zimage]` for the CUDA-only high-fidelity profile.
|
||||
|
||||
```python
|
||||
from pathlib import Path
|
||||
|
||||
|
||||
@@ -55,8 +55,9 @@ manual Homebrew formula update is the fallback when its automation is blocked.
|
||||
|
||||
The conda job uses the published artifact rather than a locally built archive
|
||||
as the hash source and commits the resulting recipe change to `main`. Runtime
|
||||
dependency mapping remains review-controlled: keep it aligned with the core
|
||||
dependencies in `pyproject.toml`, and document any conda-forge package that is
|
||||
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.
|
||||
|
||||
## Source distribution boundary
|
||||
|
||||
@@ -33,7 +33,7 @@ when you can select the affected area yourself.
|
||||
|
||||
| Backend | Install | Behavior |
|
||||
| --- | --- | --- |
|
||||
| `cv2` | Core package | Classical OpenCV inpainting |
|
||||
| `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 |
|
||||
| `auto` | Depends on installed extras | Selects LaMa, then MI-GAN, then OpenCV |
|
||||
@@ -69,6 +69,9 @@ Pixel based image commands discover these extensions:
|
||||
- HEIC and HEIF;
|
||||
- AVIF.
|
||||
|
||||
HEIC, HEIF, and AVIF pixel decoding requires the independent `heif` extra in
|
||||
addition to the selected pixel feature. Metadata scanning does not.
|
||||
|
||||
Metadata inspection and removal additionally have container paths for:
|
||||
|
||||
- JPEG XL metadata;
|
||||
|
||||
@@ -224,7 +224,7 @@ from the test set + this doc).
|
||||
|
||||
## 6. Integration cost (rough)
|
||||
|
||||
- New deps: `diffusers` already in the gpu extra; PhotoMaker ships as a `.bin`
|
||||
- New deps: `diffusers` already in the diffusion extra; PhotoMaker ships as a `.bin`
|
||||
loaded via `pipeline.load_photomaker_adapter(...)`. The OpenCLIP encoder is the
|
||||
same one diffusers already pulls. No new heavy pip dep.
|
||||
- Weight download: PhotoMaker-V1 weights are ~3 GB. Add to the Modal HF volume
|
||||
|
||||
@@ -25,13 +25,10 @@ requirements:
|
||||
run:
|
||||
- python >=${{ python_min }}
|
||||
- pillow >=10.0.0
|
||||
- pillow-heif >=0.13.0
|
||||
- piexif >=1.1.3
|
||||
- numpy >=1.24.0
|
||||
- py-opencv >=4.8.0
|
||||
- click >=8.0.0
|
||||
- python-dotenv >=1.0.0
|
||||
# c2pa-python is a core PyPI dependency but is not packaged on conda-forge.
|
||||
# c2pa-python is a default PyPI dependency but is not packaged on conda-forge.
|
||||
# The guarded import falls back to the built-in C2PA byte scanner when it is
|
||||
# absent. Add it here once a c2pa-python feedstock exists.
|
||||
|
||||
@@ -52,11 +49,9 @@ about:
|
||||
homepage: https://github.com/wiltodelta/remove-ai-watermarks
|
||||
summary: Remove visible and invisible AI watermarks from images
|
||||
description: |
|
||||
Detect and remove registered visible AI-provenance marks and strip
|
||||
AI-provenance metadata (C2PA, EXIF, IPTC, and PNG text chunks) from images.
|
||||
The core package covers the identify, metadata, visible, and erase command
|
||||
surface. Optional pip extras add SynthID diffusion removal and additional
|
||||
invisible-watermark detectors.
|
||||
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.
|
||||
license: Apache-2.0
|
||||
license_file: LICENSE
|
||||
repository: https://github.com/wiltodelta/remove-ai-watermarks
|
||||
|
||||
+31
-25
@@ -1,6 +1,6 @@
|
||||
[project]
|
||||
name = "remove-ai-watermarks"
|
||||
version = "0.21.2"
|
||||
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"
|
||||
readme = "README.md"
|
||||
requires-python = ">=3.10.1"
|
||||
@@ -46,15 +46,7 @@ classifiers = [
|
||||
]
|
||||
dependencies = [
|
||||
"pillow>=10.0.0",
|
||||
# HEIC/AVIF pixel decode for the removal path (iPhone photos, modern exports):
|
||||
# OpenCV cannot decode these containers, so image_io.imread falls back to Pillow
|
||||
# and pillow-heif (bundled libheif, prebuilt wheels) registers the HEIF+AVIF
|
||||
# openers. The metadata path already handles them via a plugin-free binary scan;
|
||||
# this closes the same gap for the pixel path so `visible`/`all` work on them.
|
||||
"pillow-heif>=0.13.0",
|
||||
"piexif>=1.1.3",
|
||||
"numpy>=1.24.0",
|
||||
"opencv-python-headless>=4.8.0",
|
||||
"click>=8.0.0",
|
||||
"python-dotenv>=1.0.0",
|
||||
# Official C2PA reader (Content Authenticity Initiative, MIT/Apache-2.0). The
|
||||
@@ -67,7 +59,25 @@ dependencies = [
|
||||
]
|
||||
|
||||
[project.optional-dependencies]
|
||||
gpu = [
|
||||
pixels = [
|
||||
"numpy>=1.24.0",
|
||||
"opencv-python-headless>=4.8.0",
|
||||
]
|
||||
# Optional HEIC/AVIF pixel decode. Metadata scanning handles these containers
|
||||
# without this plugin; combine `heif` with any pixel feature only when needed.
|
||||
heif = [
|
||||
"pillow-heif>=0.13.0",
|
||||
]
|
||||
visible = ["remove-ai-watermarks[pixels]"]
|
||||
# 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.
|
||||
detect = [
|
||||
"remove-ai-watermarks[pixels]",
|
||||
"PyWavelets>=1.1.1",
|
||||
]
|
||||
diffusion = [
|
||||
"remove-ai-watermarks[pixels]",
|
||||
"torch>=2.0.0",
|
||||
# The default PyPI torch wheel is a CPU/CUDA build. To drive an Intel GPU
|
||||
# (Arc / Data Center) via ``--device xpu`` you need an XPU-enabled torch
|
||||
@@ -75,7 +85,7 @@ gpu = [
|
||||
# XPU build). Install that build first, then this extra (torch is then
|
||||
# already satisfied and won't be re-pulled):
|
||||
# pip install torch --index-url https://download.pytorch.org/whl/xpu
|
||||
# pip install 'remove-ai-watermarks[gpu]'
|
||||
# pip install 'remove-ai-watermarks[diffusion]'
|
||||
# uv users can target the ``pytorch-xpu`` index declared under [tool.uv]:
|
||||
# uv pip install torch --index-url https://download.pytorch.org/whl/xpu
|
||||
"diffusers>=0.38.0",
|
||||
@@ -95,23 +105,13 @@ gpu = [
|
||||
]
|
||||
# Full two-stage high-fidelity profile: Qwen-Image-2512 Lightning + DiffSynth
|
||||
# Canny ControlNet for the frame, then SAM-masked Z-Image Turbo face repair.
|
||||
# CUDA-only and intentionally separate from the normal gpu extra because the
|
||||
# CUDA-only and intentionally separate from the normal diffusion extra because the
|
||||
# additional model stack and DiffSynth runtime are large.
|
||||
qwen-zimage = [
|
||||
"remove-ai-watermarks[gpu]",
|
||||
"remove-ai-watermarks[diffusion]",
|
||||
"diffsynth>=2.0.17,<3",
|
||||
"torchvision>=0.20.0",
|
||||
]
|
||||
# Open invisible-watermark (imwatermark) decoder for detecting the DWT-DCT
|
||||
# watermarks embedded by Stable Diffusion / SDXL / FLUX. Optional because it
|
||||
# pulls non-headless opencv AND torch (invisible-watermark declares torch a hard
|
||||
# dependency, and WatermarkDecoder eagerly imports rivaGan -> torch at import
|
||||
# time, so the dwtDct-only detect path still needs torch present even though it
|
||||
# never runs on GPU). So `detect` alone pulls torch -- no need to add `gpu` for
|
||||
# detection. identify() guards the import and skips the signal when absent.
|
||||
detect = [
|
||||
"invisible-watermark>=0.2.0",
|
||||
]
|
||||
# Adobe TrustMark decoder -- the open, keyless watermark behind Adobe Durable
|
||||
# Content Credentials (soft-binding alg ``com.adobe.trustmark.P``). Optional
|
||||
# because it pulls torch and downloads model weights on first use. identify()
|
||||
@@ -124,6 +124,7 @@ trustmark = [
|
||||
# cached by huggingface_hub; it is never bundled in this repo. The default cv2
|
||||
# eraser backend needs none of this.
|
||||
lama = [
|
||||
"remove-ai-watermarks[visible]",
|
||||
"onnxruntime>=1.16.0",
|
||||
"huggingface-hub>=0.20.0",
|
||||
]
|
||||
@@ -133,6 +134,7 @@ lama = [
|
||||
# memory-tight learned tier (vs big-LaMa's ~4.7 GB). Select it explicitly when
|
||||
# LaMa, the quality-first `auto` choice, is too large. Same runtime as `lama`.
|
||||
migan = [
|
||||
"remove-ai-watermarks[visible]",
|
||||
"onnxruntime>=1.16.0",
|
||||
"huggingface-hub>=0.20.0",
|
||||
]
|
||||
@@ -146,12 +148,16 @@ migan = [
|
||||
# weights are fetched with torch.hub (bundled with spandrel's torch), so no extra
|
||||
# download dependency is needed.
|
||||
esrgan = [
|
||||
"remove-ai-watermarks[pixels]",
|
||||
"spandrel>=0.3.0",
|
||||
]
|
||||
dev = [
|
||||
"remove-ai-watermarks[visible]",
|
||||
"remove-ai-watermarks[detect]",
|
||||
"pytest>=8.0.0",
|
||||
"pytest-cov>=4.1.0",
|
||||
"pytest-xdist>=3.5.0",
|
||||
"packaging>=24.0",
|
||||
"ruff>=0.4.0",
|
||||
"pyright>=1.1.0",
|
||||
"invisible-watermark>=0.2.0",
|
||||
@@ -160,11 +166,11 @@ dev = [
|
||||
"uv-outdated>=0.1.0; python_version >= '3.12'",
|
||||
"uv-secure>=0.12.0; python_version >= '3.12'",
|
||||
]
|
||||
all = ["remove-ai-watermarks[gpu,detect,trustmark,lama,migan,dev]"]
|
||||
all = ["remove-ai-watermarks[visible,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
|
||||
# explicitly targets it (see the ``gpu`` extra comment), so it does not alter
|
||||
# explicitly targets it (see the ``diffusion`` extra comment), so it does not alter
|
||||
# the locked CPU/CUDA resolution. Linux/Windows only -- no macOS XPU build.
|
||||
[[tool.uv.index]]
|
||||
name = "pytorch-xpu"
|
||||
|
||||
@@ -25,7 +25,7 @@ _os.environ.setdefault("TRANSFORMERS_VERBOSITY", "error")
|
||||
_warnings.filterwarnings("ignore", message=r".*ImageProcessorFast.*")
|
||||
|
||||
|
||||
__version__ = "0.21.2"
|
||||
__version__ = "0.22.0"
|
||||
|
||||
__all__ = ["__version__", "remove_visible", "visible_provenance"]
|
||||
|
||||
|
||||
@@ -183,7 +183,7 @@ _upscaler_option = click.option(
|
||||
"--upscaler",
|
||||
type=click.Choice(["lanczos", "esrgan"]),
|
||||
default="lanczos",
|
||||
help="How to upscale a small input to the --min-resolution floor: lanczos (default, cv2, no deps) or "
|
||||
help="How to upscale a small input to the --min-resolution floor: lanczos (default, cv2, no model) or "
|
||||
"esrgan (Real-ESRGAN via the 'esrgan' extra; better detail, slower on CPU). Best for photo/texture "
|
||||
"content -- as a generic GAN with no face/glyph prior it can degrade faces (diffusion mitigates) and "
|
||||
"thin text, so lanczos stays the default. Falls back to lanczos if the extra is absent. Only when upscaling.",
|
||||
@@ -331,7 +331,7 @@ _visible_backend_option = click.option(
|
||||
default="auto",
|
||||
help="Fill backend for visible-mark removal (localize -> fill). auto: best available, "
|
||||
"LaMa > MI-GAN > cv2 (a learned backend needs the 'lama' or 'migan' extra; else cv2, "
|
||||
"with a warning). cv2: classical inpaint (no deps, smears texture). migan: MI-GAN ONNX "
|
||||
"with a warning). cv2: classical inpaint (no model download, smears texture). migan: MI-GAN ONNX "
|
||||
"(light, ~1 GB, the memory-tight pick). lama: big-LaMa ONNX (best quality, ~4.7 GB).",
|
||||
)
|
||||
|
||||
@@ -800,7 +800,7 @@ def _parse_region(spec: str) -> tuple[int, int, int, int]:
|
||||
"--backend",
|
||||
type=click.Choice(["cv2", "migan", "lama"]),
|
||||
default="cv2",
|
||||
help="Inpaint backend. cv2: instant, no deps. migan: light ONNX MI-GAN, ~1 GB RAM, "
|
||||
help="Inpaint backend. cv2: instant, no model download. migan: light ONNX MI-GAN, ~1 GB RAM, "
|
||||
"near-LaMa quality (extra 'migan'). lama: big-LaMa, best quality but ~4.7 GB RAM (extra 'lama').",
|
||||
)
|
||||
@click.option("--inpaint-method", type=click.Choice(["telea", "ns"]), default="telea", help="cv2 inpaint method.")
|
||||
@@ -941,13 +941,14 @@ def cmd_invisible(
|
||||
"""Remove invisible AI watermarks (SynthID, StableSignature, TreeRing).
|
||||
|
||||
Uses diffusion-based regeneration. Requires GPU for reasonable speed.
|
||||
Requires the [gpu] extra: pip install 'remove-ai-watermarks[gpu]'
|
||||
Requires the [diffusion] extra: pip install 'remove-ai-watermarks[diffusion]'
|
||||
"""
|
||||
from remove_ai_watermarks.invisible_engine import is_available as invisible_available
|
||||
|
||||
if not invisible_available():
|
||||
console.print(
|
||||
"Error: GPU dependencies not installed.\n Install them with: pip install 'remove-ai-watermarks[gpu]'"
|
||||
"Error: Diffusion dependencies not installed.\n"
|
||||
" Install them with: pip install 'remove-ai-watermarks[diffusion]'"
|
||||
)
|
||||
raise SystemExit(1)
|
||||
|
||||
@@ -1298,7 +1299,7 @@ def cmd_all(
|
||||
synthid_skipped = True
|
||||
console.print(
|
||||
" Warning: Skipped - GPU dependencies not installed.\n"
|
||||
" Install them with: pip install 'remove-ai-watermarks[gpu]'"
|
||||
" Install them with: pip install 'remove-ai-watermarks[diffusion]'"
|
||||
)
|
||||
elif _should_skip_invisible_scrub(force, source):
|
||||
# No locally-detectable invisible watermark -> skip the destructive
|
||||
@@ -1404,7 +1405,7 @@ def cmd_all(
|
||||
" visible mark and metadata were stripped.\n"
|
||||
"\n"
|
||||
" Install the extra and rerun to remove it:\n"
|
||||
" pip install 'remove-ai-watermarks[gpu]'\n"
|
||||
" pip install 'remove-ai-watermarks[diffusion]'\n"
|
||||
" ====================================================================="
|
||||
)
|
||||
raise SystemExit(1)
|
||||
@@ -1766,7 +1767,7 @@ def cmd_batch(
|
||||
f"\n WARNING: the invisible (SynthID) watermark was NOT removed on "
|
||||
f"{synthid_skipped_count} image(s) -- the GPU dependencies are not installed, "
|
||||
f"so those outputs still carry the invisible watermark.\n"
|
||||
f" Install the extra and rerun: pip install 'remove-ai-watermarks[gpu]'"
|
||||
f" Install the extra and rerun: pip install 'remove-ai-watermarks[diffusion]'"
|
||||
)
|
||||
|
||||
# Non-zero exit so a wrapping service detects an incomplete/failed run (batch used
|
||||
|
||||
@@ -0,0 +1,95 @@
|
||||
"""DWT-DCT decoder compatible with invisible-watermark's ``dwtDct`` path.
|
||||
|
||||
Derived from ShieldMnt/invisible-watermark ``imwatermark/maxDct.py`` (MIT),
|
||||
trimmed to the matrix path used by Stable Diffusion, SDXL, and FLUX.
|
||||
|
||||
Copyright (c) 2021 ShieldMnt
|
||||
|
||||
The complete upstream license is distributed in
|
||||
``licenses/invisible-watermark-MIT.txt``.
|
||||
"""
|
||||
|
||||
# pyright: reportUnknownMemberType=false, reportUnknownArgumentType=false, reportUnknownVariableType=false, reportMissingTypeStubs=false
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import TYPE_CHECKING, Any
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
import pywt
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from numpy.typing import NDArray
|
||||
|
||||
_DEFAULT_SCALES = (0, 36, 36)
|
||||
_DEFAULT_BLOCK = 4
|
||||
|
||||
|
||||
class _DecodeMaxDct:
|
||||
"""Extract frequency-domain bits using the upstream matrix algorithm."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
wm_lengths: tuple[int, ...],
|
||||
scales: tuple[int, int, int] = _DEFAULT_SCALES,
|
||||
block: int = _DEFAULT_BLOCK,
|
||||
) -> None:
|
||||
self._wm_lengths = wm_lengths
|
||||
self._scales = scales
|
||||
self._block = block
|
||||
|
||||
def decode(self, bgr: NDArray[Any]) -> dict[int, NDArray[Any]]:
|
||||
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}
|
||||
for channel in range(2):
|
||||
if self._scales[channel] <= 0:
|
||||
continue
|
||||
ca1, _detail = pywt.dwt2(yuv[: row // 4 * 4, : col // 4 * 4, channel], "haar")
|
||||
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()
|
||||
}
|
||||
|
||||
def _decode_frame(
|
||||
self,
|
||||
frame: NDArray[Any],
|
||||
scale: int,
|
||||
scores_by_length: dict[int, list[list[int]]],
|
||||
) -> None:
|
||||
row, col = frame.shape
|
||||
bit_index = 0
|
||||
for i in range(row // self._block):
|
||||
for j in range(col // self._block):
|
||||
block = frame[
|
||||
i * self._block : i * self._block + self._block,
|
||||
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)
|
||||
bit_index += 1
|
||||
|
||||
def _infer_bit(self, block: NDArray[Any], scale: int) -> int:
|
||||
position = int(np.argmax(np.abs(block.flatten()[1:]))) + 1
|
||||
i, j = position // self._block, position % self._block
|
||||
value = abs(float(block[i][j]))
|
||||
return int((value % scale) > 0.5 * scale)
|
||||
|
||||
|
||||
def decode_dwt_dct(bgr: NDArray[Any], wm_len: int) -> NDArray[Any]:
|
||||
"""Extract ``wm_len`` watermark bits from a BGR image."""
|
||||
return decode_dwt_dct_lengths(bgr, (wm_len,))[wm_len]
|
||||
|
||||
|
||||
def decode_dwt_dct_lengths(bgr: NDArray[Any], wm_lengths: tuple[int, ...]) -> dict[int, NDArray[Any]]:
|
||||
"""Extract several watermark lengths with one DWT and block scan."""
|
||||
if bgr.size == 0 or min(bgr.shape[:2]) * max(bgr.shape[:2]) < 256 * 256:
|
||||
raise RuntimeError("image too small, should be larger than 256x256")
|
||||
if not wm_lengths or any(wm_len <= 0 for wm_len in wm_lengths):
|
||||
raise ValueError("watermark lengths must be positive")
|
||||
return _DecodeMaxDct(wm_lengths=tuple(dict.fromkeys(wm_lengths))).decode(bgr)
|
||||
@@ -722,8 +722,8 @@ def _visible_text_marks(image_path: Path, *, image: NDArray[Any] | None = None)
|
||||
def _invisible_watermark(image_path: Path) -> str | None:
|
||||
"""Open invisible-watermark scheme name (SD/SDXL/FLUX) or None.
|
||||
|
||||
Optional: needs the imwatermark decoder (extra ``detect``). Returns None if
|
||||
it is not installed or no known watermark decodes.
|
||||
Optional: needs the torch-free DWT-DCT decoder (extra ``detect``). Returns
|
||||
None if it is not installed or no known watermark decodes.
|
||||
"""
|
||||
from remove_ai_watermarks.invisible_watermark import detect_invisible_watermark
|
||||
|
||||
@@ -761,6 +761,9 @@ def _collect_visible_signals(
|
||||
image = imread(image_path)
|
||||
except Exception as exc: # cv2 missing - detectors fall back / no-op
|
||||
logger.debug("visible-mark decode unavailable: %s", exc)
|
||||
return platform
|
||||
if image is None:
|
||||
return platform
|
||||
|
||||
sparkle_conf = _visible_sparkle(image_path, image=image)
|
||||
if sparkle_conf is not None and sparkle_conf >= _SPARKLE_THRESHOLD:
|
||||
@@ -1087,8 +1090,8 @@ def identify(
|
||||
image_path: Path to the image (PNG, JPEG, WebP, or ISOBMFF container).
|
||||
check_visible: Also run the registered visible-mark detectors through cv2.
|
||||
Set False for a metadata-only, dependency-light scan.
|
||||
check_invisible: Also decode open invisible watermarks (SD/SDXL/FLUX) via
|
||||
the optional imwatermark library. No-op when it is not installed.
|
||||
check_invisible: Also decode optional open invisible watermarks
|
||||
(SD/SDXL/FLUX). No-op when the decoder extra is not installed.
|
||||
|
||||
File-backed metadata extraction runs first. The extracted evidence is then
|
||||
evaluated independently, followed by the optional pixel-backed visible and
|
||||
|
||||
@@ -4,7 +4,7 @@ Wraps the vendored noai-watermark code for removing invisible AI watermarks
|
||||
(SynthID, StableSignature, TreeRing) via diffusion-based regeneration.
|
||||
|
||||
This module requires the 'gpu' extra dependencies:
|
||||
uv pip install 'remove-ai-watermarks[gpu]'
|
||||
uv pip install 'remove-ai-watermarks[diffusion]'
|
||||
"""
|
||||
|
||||
# cv2/torch boundary: this engine wraps cv2 (resize/imwrite/cvtColor) and the
|
||||
@@ -226,7 +226,7 @@ class InvisibleEngine:
|
||||
input size, so this is a transparent quality boost; it adds time
|
||||
and memory on small inputs. Ignored on a min > max misconfig.
|
||||
upscaler: How to upscale a small input to the ``min_resolution`` floor:
|
||||
``"lanczos"`` (default, cv2, no deps) or ``"esrgan"`` (Real-ESRGAN
|
||||
``"lanczos"`` (default, cv2, no model download) or ``"esrgan"`` (Real-ESRGAN
|
||||
via the ``esrgan`` extra). Only applies when UPscaling (the floor
|
||||
case); a ``max_resolution`` downscale always uses Lanczos. Falls back
|
||||
to Lanczos if the extra is absent.
|
||||
|
||||
@@ -14,21 +14,20 @@ source:
|
||||
|
||||
The watermark is fragile: it does NOT survive JPEG re-encoding or resizing
|
||||
(verified -- gone after JPEG q90), so detection works only on pristine PNG
|
||||
originals. Absence is never proof. Requires the optional ``invisible-watermark``
|
||||
package (extra: ``detect``); ``detect_invisible_watermark`` returns None when it
|
||||
is not installed.
|
||||
originals. Absence is never proof. Requires the optional ``detect`` extra;
|
||||
``detect_invisible_watermark`` returns None when it is not installed.
|
||||
"""
|
||||
|
||||
# imwatermark ships no type stubs (like cv2); its decoder returns are Unknown.
|
||||
# Relax the untyped-library diagnostics for this thin wrapper module only.
|
||||
# The optional numeric libraries do not provide complete types for this path.
|
||||
# pyright: reportMissingTypeStubs=false, reportUnknownMemberType=false, reportUnknownVariableType=false, reportUnknownArgumentType=false
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
from typing import TYPE_CHECKING, cast
|
||||
from typing import TYPE_CHECKING
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from collections.abc import Iterable
|
||||
from pathlib import Path
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
@@ -49,10 +48,10 @@ _MATCH_SD1_FRAC = 0.92 # fraction of the 136 string bits that must match
|
||||
|
||||
|
||||
def is_available() -> bool:
|
||||
"""True if the optional imwatermark decoder is installed."""
|
||||
"""True when all dependencies for the optional DWT-DCT decoder exist."""
|
||||
from .optional_deps import module_available
|
||||
|
||||
return module_available("imwatermark")
|
||||
return module_available("cv2", "numpy", "pywt")
|
||||
|
||||
|
||||
def _bits_match(value: int, ref: int, width: int = 48) -> int:
|
||||
@@ -68,6 +67,20 @@ def _bytes_match_frac(a: bytes, b: bytes) -> float:
|
||||
return 1.0 - diff / (8 * len(b))
|
||||
|
||||
|
||||
def _bits_to_int(bits: Iterable[object]) -> int:
|
||||
value = 0
|
||||
for bit in bits:
|
||||
value = (value << 1) | int(bool(bit))
|
||||
return value
|
||||
|
||||
|
||||
def _bits_to_bytes(bits: Iterable[object], nbytes: int) -> bytes:
|
||||
import numpy as np
|
||||
|
||||
packed = np.packbits([int(bool(bit)) for bit in bits])
|
||||
return bytes(int(value) for value in packed[:nbytes])
|
||||
|
||||
|
||||
def detect_invisible_watermark(image_path: Path) -> str | None:
|
||||
"""Return the embedding scheme name if a known open watermark is decoded.
|
||||
|
||||
@@ -78,32 +91,26 @@ def detect_invisible_watermark(image_path: Path) -> str | None:
|
||||
"""
|
||||
if not is_available():
|
||||
return None
|
||||
from imwatermark import WatermarkDecoder
|
||||
|
||||
from remove_ai_watermarks import image_io
|
||||
from remove_ai_watermarks.dwt_dct import decode_dwt_dct_lengths
|
||||
|
||||
img = image_io.imread(image_path)
|
||||
if img is None:
|
||||
return None
|
||||
|
||||
# 48-bit fixed-message watermarks (SDXL, FLUX.2).
|
||||
try:
|
||||
bits = WatermarkDecoder("bits", 48).decode(img, "dwtDct")
|
||||
value = 0
|
||||
for bit in bits:
|
||||
value = (value << 1) | (1 if bit else 0)
|
||||
for name, ref in _BITS_48.items():
|
||||
if _bits_match(value, ref) >= _MATCH_48:
|
||||
return name
|
||||
decoded = decode_dwt_dct_lengths(img, (48, 8 * len(_SD1_STRING)))
|
||||
except Exception as exc: # decode can fail on tiny images
|
||||
logger.debug("48-bit watermark decode failed for %s: %s", image_path, exc)
|
||||
logger.debug("watermark decode failed for %s: %s", image_path, exc)
|
||||
return None
|
||||
|
||||
# 136-bit default string watermark (SD 1.x / 2.x).
|
||||
try:
|
||||
raw = cast("bytes", WatermarkDecoder("bytes", 8 * len(_SD1_STRING)).decode(img, "dwtDct"))
|
||||
if _bytes_match_frac(raw, _SD1_STRING) >= _MATCH_SD1_FRAC:
|
||||
return "Stable Diffusion 1.x / 2.x"
|
||||
except Exception as exc:
|
||||
logger.debug("string watermark decode failed for %s: %s", image_path, exc)
|
||||
value = _bits_to_int(decoded[48])
|
||||
for name, ref in _BITS_48.items():
|
||||
if _bits_match(value, ref) >= _MATCH_48:
|
||||
return name
|
||||
|
||||
raw = _bits_to_bytes(decoded[8 * len(_SD1_STRING)], len(_SD1_STRING))
|
||||
if _bytes_match_frac(raw, _SD1_STRING) >= _MATCH_SD1_FRAC:
|
||||
return "Stable Diffusion 1.x / 2.x"
|
||||
|
||||
return None
|
||||
|
||||
@@ -0,0 +1,21 @@
|
||||
MIT License
|
||||
|
||||
Copyright (c) 2021 ShieldMnt
|
||||
|
||||
Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
of this software and associated documentation files (the "Software"), to deal
|
||||
in the Software without restriction, including without limitation the rights
|
||||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
copies of the Software, and to permit persons to whom the Software is
|
||||
furnished to do so, subject to the following conditions:
|
||||
|
||||
The above copyright notice and this permission notice shall be included in all
|
||||
copies or substantial portions of the Software.
|
||||
|
||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||
SOFTWARE.
|
||||
@@ -7,7 +7,7 @@ is exposed **lazily** via PEP 562 ``__getattr__``: importing a light submodule
|
||||
(e.g. ``noai.c2pa`` / ``noai.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 ``gpu``/``detect`` extras are present --
|
||||
torch) even in a full install where the ``diffusion`` extra is present --
|
||||
otherwise the mere presence of torch in the env inflated identify to ~420 MB and
|
||||
risked OOM on a 512 MB host.
|
||||
"""
|
||||
|
||||
@@ -43,7 +43,7 @@ from remove_ai_watermarks.noai.constants import (
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Official C2PA reader (c2pa-python, a core dependency). It is the primary,
|
||||
# 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
|
||||
|
||||
@@ -7,7 +7,7 @@ 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 core
|
||||
# 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).
|
||||
|
||||
@@ -476,8 +476,9 @@ class WatermarkRemover:
|
||||
"""Turn off the diffusers default invisible watermarker on an SDXL pipeline.
|
||||
|
||||
diffusers embeds an open "Stable Diffusion XL" DWT-DCT invisible watermark on
|
||||
EVERY SDXL output whenever ``invisible-watermark`` is installed (the ``detect``
|
||||
extra). A watermark REMOVER must not re-stamp a detectable AI watermark, or the
|
||||
EVERY SDXL output whenever ``invisible-watermark`` is installed (kept as a
|
||||
development parity dependency). A watermark REMOVER must not re-stamp a
|
||||
detectable AI watermark, or the
|
||||
cleaned output re-reads as AI (``identify`` -> "Open invisible watermark: Stable
|
||||
Diffusion XL"). Shared by both SDXL loaders; the ``ControlNetModel`` sub-model
|
||||
and the Qwen loader never call it (only the pipeline accepts the kwarg).
|
||||
|
||||
@@ -4,7 +4,7 @@ Mirrors ``region_eraser``'s optional-backend pattern: ``is_available()`` guards
|
||||
``spandrel`` import, a lazy singleton (double-checked lock) holds the loaded model, and
|
||||
the weights download on first use (cached by ``torch.hub``) -- they are never bundled.
|
||||
|
||||
The DEFAULT upscaler stays Lanczos (cv2, no deps); this is opt-in via the ``esrgan``
|
||||
The DEFAULT upscaler stays Lanczos (cv2, no model download); this is opt-in via the ``esrgan``
|
||||
extra and feeds the ``--upscaler esrgan`` path. ``spandrel`` is a pure model-loader
|
||||
(MIT) with NO basicsr dependency -- it pulls only torch/torchvision/safetensors/numpy/
|
||||
einops -- so it sidesteps the basicsr / ``torchvision.transforms.functional_tensor``
|
||||
|
||||
+5
-5
@@ -542,7 +542,7 @@ class TestAllCommand:
|
||||
result = runner.invoke(main, ["all", str(sample_png), "-o", str(output)])
|
||||
assert result.exit_code != 0, result.output
|
||||
assert "NOT removed" in result.output
|
||||
assert "remove-ai-watermarks[gpu]" in result.output
|
||||
assert "remove-ai-watermarks[diffusion]" in result.output
|
||||
assert output.exists() # visible + metadata still produced a file
|
||||
|
||||
def test_all_reports_metadata_that_survived_stripping(self, runner, sample_png, tmp_path):
|
||||
@@ -933,21 +933,21 @@ class TestBatchCommand:
|
||||
|
||||
|
||||
class TestGpuHintMarkup:
|
||||
"""The GPU-extra install hint must reach the user with the ``[gpu]`` token
|
||||
"""The diffusion install hint must reach the user with the ``[diffusion]`` token
|
||||
intact (plain output prints it verbatim, with no markup parsing)."""
|
||||
|
||||
def test_invisible_install_hint_keeps_gpu_extra(self, runner, sample_png):
|
||||
with patch("remove_ai_watermarks.invisible_engine.is_available", return_value=False):
|
||||
result = runner.invoke(main, ["invisible", str(sample_png)])
|
||||
assert result.exit_code != 0
|
||||
assert "remove-ai-watermarks[gpu]" in result.output
|
||||
assert "remove-ai-watermarks[diffusion]" in result.output
|
||||
|
||||
def test_all_install_hint_keeps_gpu_extra(self, runner, sample_png):
|
||||
# The `all` pipeline skips the invisible step with a warning that carries
|
||||
# the same hint; it must keep the [gpu] extra too.
|
||||
# the same hint; it must keep the [diffusion] extra too.
|
||||
with patch("remove_ai_watermarks.invisible_engine.is_available", return_value=False):
|
||||
result = runner.invoke(main, ["all", str(sample_png)])
|
||||
assert "remove-ai-watermarks[gpu]" in result.output
|
||||
assert "remove-ai-watermarks[diffusion]" in result.output
|
||||
|
||||
|
||||
class TestEraseCommand:
|
||||
|
||||
+10
-1
@@ -782,6 +782,15 @@ class TestIdentifyVisibleTextMarks:
|
||||
identify(tmp_clean_png, check_visible=True, check_invisible=False)
|
||||
assert mock_imread.call_count == 1
|
||||
|
||||
def test_missing_pixel_extra_preserves_metadata_verdict(self, tmp_png_with_ai_metadata: Path):
|
||||
import remove_ai_watermarks.image_io as image_io
|
||||
|
||||
with patch.object(image_io, "imread", side_effect=ModuleNotFoundError("No module named 'cv2'")):
|
||||
report = identify(tmp_png_with_ai_metadata, check_visible=True, check_invisible=False)
|
||||
|
||||
assert report.is_ai_generated is True
|
||||
assert report.confidence == "high"
|
||||
|
||||
|
||||
# ── Caveats and serialization ───────────────────────────────────────
|
||||
|
||||
@@ -989,7 +998,7 @@ class TestIdentifyC2paDevice:
|
||||
from remove_ai_watermarks.invisible_watermark import is_available as _wm_available # noqa: E402
|
||||
|
||||
|
||||
@pytest.mark.skipif(not _wm_available(), reason="invisible-watermark not installed")
|
||||
@pytest.mark.skipif(not _wm_available(), reason="detect extra not installed")
|
||||
class TestIdentifyInvisibleWatermark:
|
||||
def _sdxl_watermarked(self, tmp_path: Path) -> Path:
|
||||
import cv2
|
||||
|
||||
@@ -18,9 +18,9 @@ class TestIsAvailable:
|
||||
assert isinstance(result, bool)
|
||||
|
||||
def test_available_reflects_dependencies(self):
|
||||
"""is_available() is True iff torch + diffusers (the gpu extra) import.
|
||||
"""is_available() is True iff torch + diffusers (the diffusion extra) import.
|
||||
|
||||
Must not assume the full stack: the core+dev CI env has no diffusers.
|
||||
Must not assume the full stack: the default+dev CI env has no diffusers.
|
||||
"""
|
||||
import importlib.util
|
||||
|
||||
@@ -212,7 +212,7 @@ class TestCannyControlImage:
|
||||
|
||||
def test_edge_map_is_3channel_rgb(self):
|
||||
if not is_available():
|
||||
pytest.skip("gpu extra (torch/diffusers) not installed")
|
||||
pytest.skip("diffusion extra (torch/diffusers) not installed")
|
||||
import numpy as np
|
||||
|
||||
from remove_ai_watermarks.noai.watermark_remover import WatermarkRemover
|
||||
|
||||
@@ -1,8 +1,7 @@
|
||||
"""Tests for open invisible-watermark (imwatermark) detection.
|
||||
"""Tests for open DWT-DCT watermark detection.
|
||||
|
||||
Each known scheme is round-tripped: embed its exact upstream pattern with the
|
||||
encoder, then assert the detector names it. Skipped entirely if the optional
|
||||
``invisible-watermark`` package is not installed.
|
||||
The upstream encoder supplies known watermarks, while the in-tree decoder must
|
||||
both identify them and match the upstream decoder bit for bit.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
@@ -25,7 +24,7 @@ from remove_ai_watermarks.invisible_watermark import (
|
||||
is_available,
|
||||
)
|
||||
|
||||
pytestmark = pytest.mark.skipif(not is_available(), reason="invisible-watermark not installed")
|
||||
pytestmark = pytest.mark.skipif(not is_available(), reason="detect extra not installed")
|
||||
|
||||
|
||||
def _base_image() -> np.ndarray:
|
||||
@@ -61,6 +60,20 @@ class TestHelpers:
|
||||
|
||||
|
||||
class TestDetect:
|
||||
def test_in_tree_decoder_matches_upstream(self, tmp_path: Path):
|
||||
from imwatermark import WatermarkDecoder
|
||||
|
||||
from remove_ai_watermarks.dwt_dct import decode_dwt_dct
|
||||
from remove_ai_watermarks.image_io import imread
|
||||
|
||||
path = _write_bits_watermark(tmp_path, _BITS_48["Stable Diffusion XL"])
|
||||
image = imread(path)
|
||||
assert image is not None
|
||||
|
||||
upstream = np.asarray(WatermarkDecoder("bits", 48).decode(image, "dwtDct"), dtype=bool)
|
||||
ours = np.asarray(decode_dwt_dct(image, wm_len=48), dtype=bool)
|
||||
assert np.array_equal(ours, upstream)
|
||||
|
||||
def test_detects_sdxl(self, tmp_path: Path):
|
||||
path = _write_bits_watermark(tmp_path, _BITS_48["Stable Diffusion XL"])
|
||||
assert detect_invisible_watermark(path) == "Stable Diffusion XL"
|
||||
|
||||
+2
-2
@@ -52,8 +52,8 @@ class TestConstants:
|
||||
assert ".jpg" in SUPPORTED_FORMATS
|
||||
|
||||
def test_supported_formats_include_heic_avif(self):
|
||||
# HEIC/AVIF are first-class on the pixel path now (read+write via pillow-heif),
|
||||
# so batch discovers them and the CLI does not warn.
|
||||
# HEIC/AVIF are first-class when the visible pixel extra is installed
|
||||
# (read+write via pillow-heif), so batch discovers them without a warning.
|
||||
assert {".heic", ".heif", ".avif"} <= SUPPORTED_FORMATS
|
||||
|
||||
def test_supported_formats_exclude_jpeg_xl(self):
|
||||
|
||||
@@ -0,0 +1,67 @@
|
||||
"""Published dependency boundaries."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from importlib.metadata import metadata, requires
|
||||
|
||||
from packaging.requirements import Requirement
|
||||
from packaging.utils import canonicalize_name
|
||||
|
||||
|
||||
def _requirement_names(extra: str | None = None) -> set[str]:
|
||||
selected_extra = extra or ""
|
||||
parsed = (Requirement(value) for value in requires("remove-ai-watermarks") or [])
|
||||
return {
|
||||
canonicalize_name(requirement.name)
|
||||
for requirement in parsed
|
||||
if requirement.marker is None or requirement.marker.evaluate({"extra": selected_extra})
|
||||
}
|
||||
|
||||
|
||||
def test_default_install_is_metadata_focused():
|
||||
default = _requirement_names()
|
||||
|
||||
assert {
|
||||
"c2pa-python",
|
||||
"click",
|
||||
"piexif",
|
||||
"pillow",
|
||||
"python-dotenv",
|
||||
} <= default
|
||||
assert {
|
||||
"invisible-watermark",
|
||||
"numpy",
|
||||
"opencv-python-headless",
|
||||
"pillow-heif",
|
||||
"torch",
|
||||
"trustmark",
|
||||
}.isdisjoint(default)
|
||||
|
||||
|
||||
def test_pixels_extra_owns_shared_numeric_dependencies():
|
||||
assert {
|
||||
"numpy",
|
||||
"opencv-python-headless",
|
||||
} <= _requirement_names("pixels")
|
||||
|
||||
|
||||
def test_file_format_and_detector_dependencies_are_independent():
|
||||
assert "pillow-heif" in _requirement_names("heif")
|
||||
assert "pywavelets" in _requirement_names("detect")
|
||||
|
||||
|
||||
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 {"gpu", "remove", "detect-pywavelets"}.isdisjoint(extras)
|
||||
|
||||
|
||||
def test_production_all_does_not_include_development_tools():
|
||||
assert {
|
||||
"pyright",
|
||||
"pytest",
|
||||
"pytest-cov",
|
||||
"pytest-xdist",
|
||||
"ruff",
|
||||
}.isdisjoint(_requirement_names("all"))
|
||||
@@ -238,7 +238,7 @@ class TestQwenKwargs:
|
||||
"""_build_qwen_kwargs is pure (no torch); guards the Qwen-Image call shape.
|
||||
|
||||
watermark_remover imports torch under a try/except, so the module (and this pure
|
||||
helper) imports fine in the core+dev CI env where torch is absent.
|
||||
helper) imports fine in the default+dev CI env where torch is absent.
|
||||
"""
|
||||
|
||||
def test_uses_true_cfg_not_guidance_scale(self):
|
||||
@@ -431,7 +431,7 @@ class TestAvailability:
|
||||
|
||||
def test_watermark_removal_available(self):
|
||||
# Reflects the actual environment: True iff torch + diffusers (the gpu
|
||||
# extra) are importable. The core+dev CI env has no diffusers, so this
|
||||
# extra) are importable. The default+dev CI env has no diffusers, so this
|
||||
# must not assume the full stack is present.
|
||||
import importlib.util
|
||||
|
||||
|
||||
@@ -3187,78 +3187,100 @@ wheels = [
|
||||
|
||||
[[package]]
|
||||
name = "remove-ai-watermarks"
|
||||
version = "0.21.2"
|
||||
version = "0.22.0"
|
||||
source = { editable = "." }
|
||||
dependencies = [
|
||||
{ name = "c2pa-python" },
|
||||
{ name = "click" },
|
||||
{ name = "numpy" },
|
||||
{ name = "opencv-python-headless" },
|
||||
{ name = "piexif" },
|
||||
{ name = "pillow" },
|
||||
{ name = "pillow-heif" },
|
||||
{ name = "python-dotenv" },
|
||||
]
|
||||
|
||||
[package.optional-dependencies]
|
||||
all = [
|
||||
{ name = "accelerate" },
|
||||
{ name = "diffsynth" },
|
||||
{ name = "diffusers" },
|
||||
{ name = "huggingface-hub" },
|
||||
{ name = "invisible-watermark" },
|
||||
{ name = "numpy" },
|
||||
{ name = "onnxruntime", version = "1.24.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
|
||||
{ name = "onnxruntime", version = "1.27.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
|
||||
{ name = "pyright" },
|
||||
{ name = "pytest" },
|
||||
{ name = "pytest-cov" },
|
||||
{ name = "pytest-xdist" },
|
||||
{ name = "ruff" },
|
||||
{ name = "opencv-python-headless" },
|
||||
{ name = "pillow-heif" },
|
||||
{ name = "pywavelets", version = "1.8.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
|
||||
{ name = "pywavelets", version = "1.9.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
|
||||
{ name = "safetensors" },
|
||||
{ name = "spandrel" },
|
||||
{ name = "tokenizers" },
|
||||
{ name = "torch" },
|
||||
{ name = "torchvision" },
|
||||
{ name = "transformers" },
|
||||
{ name = "trustmark" },
|
||||
{ name = "uv-outdated", marker = "python_full_version >= '3.12'" },
|
||||
{ name = "uv-secure", marker = "python_full_version >= '3.12'" },
|
||||
]
|
||||
detect = [
|
||||
{ name = "invisible-watermark" },
|
||||
{ name = "numpy" },
|
||||
{ name = "opencv-python-headless" },
|
||||
{ name = "pywavelets", version = "1.8.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
|
||||
{ name = "pywavelets", version = "1.9.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
|
||||
]
|
||||
dev = [
|
||||
{ name = "invisible-watermark" },
|
||||
{ name = "numpy" },
|
||||
{ name = "opencv-python-headless" },
|
||||
{ name = "packaging" },
|
||||
{ name = "pyright" },
|
||||
{ name = "pytest" },
|
||||
{ name = "pytest-cov" },
|
||||
{ name = "pytest-xdist" },
|
||||
{ name = "pywavelets", version = "1.8.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
|
||||
{ name = "pywavelets", version = "1.9.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
|
||||
{ name = "ruff" },
|
||||
{ name = "uv-outdated", marker = "python_full_version >= '3.12'" },
|
||||
{ name = "uv-secure", marker = "python_full_version >= '3.12'" },
|
||||
]
|
||||
esrgan = [
|
||||
{ name = "spandrel" },
|
||||
]
|
||||
gpu = [
|
||||
diffusion = [
|
||||
{ name = "accelerate" },
|
||||
{ name = "diffusers" },
|
||||
{ name = "numpy" },
|
||||
{ name = "opencv-python-headless" },
|
||||
{ name = "safetensors" },
|
||||
{ name = "tokenizers" },
|
||||
{ name = "torch" },
|
||||
{ name = "transformers" },
|
||||
]
|
||||
esrgan = [
|
||||
{ name = "numpy" },
|
||||
{ name = "opencv-python-headless" },
|
||||
{ name = "spandrel" },
|
||||
]
|
||||
heif = [
|
||||
{ name = "pillow-heif" },
|
||||
]
|
||||
lama = [
|
||||
{ name = "huggingface-hub" },
|
||||
{ name = "numpy" },
|
||||
{ name = "onnxruntime", version = "1.24.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
|
||||
{ name = "onnxruntime", version = "1.27.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
|
||||
{ name = "opencv-python-headless" },
|
||||
]
|
||||
migan = [
|
||||
{ name = "huggingface-hub" },
|
||||
{ name = "numpy" },
|
||||
{ name = "onnxruntime", version = "1.24.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
|
||||
{ name = "onnxruntime", version = "1.27.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
|
||||
{ name = "opencv-python-headless" },
|
||||
]
|
||||
pixels = [
|
||||
{ name = "numpy" },
|
||||
{ name = "opencv-python-headless" },
|
||||
]
|
||||
qwen-zimage = [
|
||||
{ name = "accelerate" },
|
||||
{ name = "diffsynth" },
|
||||
{ name = "diffusers" },
|
||||
{ name = "numpy" },
|
||||
{ name = "opencv-python-headless" },
|
||||
{ name = "safetensors" },
|
||||
{ name = "tokenizers" },
|
||||
{ name = "torch" },
|
||||
@@ -3268,44 +3290,57 @@ qwen-zimage = [
|
||||
trustmark = [
|
||||
{ name = "trustmark" },
|
||||
]
|
||||
visible = [
|
||||
{ name = "numpy" },
|
||||
{ name = "opencv-python-headless" },
|
||||
]
|
||||
|
||||
[package.metadata]
|
||||
requires-dist = [
|
||||
{ name = "accelerate", marker = "extra == 'gpu'", specifier = ">=0.25.0" },
|
||||
{ name = "accelerate", marker = "extra == 'diffusion'", specifier = ">=0.25.0" },
|
||||
{ name = "c2pa-python", specifier = ">=0.35.0" },
|
||||
{ name = "click", specifier = ">=8.0.0" },
|
||||
{ name = "diffsynth", marker = "extra == 'qwen-zimage'", specifier = ">=2.0.17,<3" },
|
||||
{ name = "diffusers", marker = "extra == 'gpu'", specifier = ">=0.38.0" },
|
||||
{ name = "diffusers", marker = "extra == 'diffusion'", specifier = ">=0.38.0" },
|
||||
{ name = "huggingface-hub", marker = "extra == 'lama'", specifier = ">=0.20.0" },
|
||||
{ name = "huggingface-hub", marker = "extra == 'migan'", specifier = ">=0.20.0" },
|
||||
{ name = "invisible-watermark", marker = "extra == 'detect'", specifier = ">=0.2.0" },
|
||||
{ name = "invisible-watermark", marker = "extra == 'dev'", specifier = ">=0.2.0" },
|
||||
{ name = "numpy", specifier = ">=1.24.0" },
|
||||
{ name = "numpy", marker = "extra == 'pixels'", specifier = ">=1.24.0" },
|
||||
{ name = "onnxruntime", marker = "extra == 'lama'", specifier = ">=1.16.0" },
|
||||
{ name = "onnxruntime", marker = "extra == 'migan'", specifier = ">=1.16.0" },
|
||||
{ name = "opencv-python-headless", specifier = ">=4.8.0" },
|
||||
{ name = "opencv-python-headless", marker = "extra == 'pixels'", specifier = ">=4.8.0" },
|
||||
{ name = "packaging", marker = "extra == 'dev'", specifier = ">=24.0" },
|
||||
{ name = "piexif", specifier = ">=1.1.3" },
|
||||
{ name = "pillow", specifier = ">=10.0.0" },
|
||||
{ name = "pillow-heif", specifier = ">=0.13.0" },
|
||||
{ name = "pillow-heif", marker = "extra == 'heif'", specifier = ">=0.13.0" },
|
||||
{ name = "pyright", marker = "extra == 'dev'", specifier = ">=1.1.0" },
|
||||
{ name = "pytest", marker = "extra == 'dev'", specifier = ">=8.0.0" },
|
||||
{ name = "pytest-cov", marker = "extra == 'dev'", specifier = ">=4.1.0" },
|
||||
{ name = "pytest-xdist", marker = "extra == 'dev'", specifier = ">=3.5.0" },
|
||||
{ name = "python-dotenv", specifier = ">=1.0.0" },
|
||||
{ name = "remove-ai-watermarks", extras = ["gpu"], marker = "extra == 'qwen-zimage'" },
|
||||
{ name = "remove-ai-watermarks", extras = ["gpu", "detect", "trustmark", "lama", "migan", "dev"], marker = "extra == 'all'" },
|
||||
{ name = "pywavelets", marker = "extra == 'detect'", specifier = ">=1.1.1" },
|
||||
{ name = "remove-ai-watermarks", extras = ["detect"], marker = "extra == 'dev'" },
|
||||
{ name = "remove-ai-watermarks", extras = ["diffusion"], marker = "extra == 'qwen-zimage'" },
|
||||
{ name = "remove-ai-watermarks", extras = ["pixels"], marker = "extra == 'detect'" },
|
||||
{ name = "remove-ai-watermarks", extras = ["pixels"], marker = "extra == 'diffusion'" },
|
||||
{ name = "remove-ai-watermarks", extras = ["pixels"], marker = "extra == 'esrgan'" },
|
||||
{ name = "remove-ai-watermarks", extras = ["pixels"], marker = "extra == 'visible'" },
|
||||
{ name = "remove-ai-watermarks", extras = ["visible"], marker = "extra == 'dev'" },
|
||||
{ name = "remove-ai-watermarks", extras = ["visible"], marker = "extra == 'lama'" },
|
||||
{ name = "remove-ai-watermarks", extras = ["visible"], marker = "extra == 'migan'" },
|
||||
{ name = "remove-ai-watermarks", extras = ["visible", "heif", "detect", "trustmark", "diffusion", "qwen-zimage", "lama", "migan", "esrgan"], marker = "extra == 'all'" },
|
||||
{ name = "ruff", marker = "extra == 'dev'", specifier = ">=0.4.0" },
|
||||
{ name = "safetensors", marker = "extra == 'gpu'" },
|
||||
{ name = "safetensors", marker = "extra == 'diffusion'" },
|
||||
{ name = "spandrel", marker = "extra == 'esrgan'", specifier = ">=0.3.0" },
|
||||
{ name = "tokenizers", marker = "extra == 'gpu'", specifier = ">=0.22,<0.23" },
|
||||
{ name = "torch", marker = "extra == 'gpu'", specifier = ">=2.0.0" },
|
||||
{ name = "tokenizers", marker = "extra == 'diffusion'", specifier = ">=0.22,<0.23" },
|
||||
{ name = "torch", marker = "extra == 'diffusion'", specifier = ">=2.0.0" },
|
||||
{ name = "torchvision", marker = "extra == 'qwen-zimage'", specifier = ">=0.20.0" },
|
||||
{ name = "transformers", marker = "extra == 'gpu'", specifier = ">=5,<6" },
|
||||
{ name = "transformers", marker = "extra == 'diffusion'", specifier = ">=5,<6" },
|
||||
{ name = "trustmark", marker = "extra == 'trustmark'", specifier = ">=0.8.0" },
|
||||
{ name = "uv-outdated", marker = "python_full_version >= '3.12' and extra == 'dev'", specifier = ">=0.1.0" },
|
||||
{ name = "uv-secure", marker = "python_full_version >= '3.12' and extra == 'dev'", specifier = ">=0.12.0" },
|
||||
]
|
||||
provides-extras = ["gpu", "qwen-zimage", "detect", "trustmark", "lama", "migan", "esrgan", "dev", "all"]
|
||||
provides-extras = ["pixels", "heif", "visible", "detect", "diffusion", "qwen-zimage", "trustmark", "lama", "migan", "esrgan", "dev", "all"]
|
||||
|
||||
[[package]]
|
||||
name = "requests"
|
||||
|
||||
Reference in New Issue
Block a user