https://github.com/xiaoyao9184/docker-wam
Docker implementation of the paper "Watermark Anything with Localized Messages"
https://github.com/xiaoyao9184/docker-wam
cuda-support docker-image wam watermark-anything
Last synced: about 2 months ago
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Docker implementation of the paper "Watermark Anything with Localized Messages"
- Host: GitHub
- URL: https://github.com/xiaoyao9184/docker-wam
- Owner: xiaoyao9184
- License: apache-2.0
- Created: 2024-12-02T11:53:43.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2026-03-17T02:03:45.000Z (3 months ago)
- Last Synced: 2026-03-17T12:08:44.916Z (3 months ago)
- Topics: cuda-support, docker-image, wam, watermark-anything
- Language: Python
- Homepage:
- Size: 162 KB
- Stars: 2
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Docker watermark-anything
A Docker image built through Github Actions with Git commit version tag
[](https://github.com/xiaoyao9184/docker-wam/actions/workflows/docker-image-tag-commit.yml) [](https://hub.docker.com/r/xiaoyao9184/watermark-anything)
[](https://github.com/xiaoyao9184/docker-wam/actions/workflows/hf-space-sync.yml) [](https://huggingface.co/spaces/xiaoyao9184/watermark-anything) 
# Why
I found that watermark-anything's Docker image is difficult to find.
The code on [GitHub](https://github.com/facebookresearch/watermark-anything) does not provide a pre-built Docker image.
This project will use GitHub Actions and Docker Hub to build and publish images,
aiming to keep the process as clean as possible without custom configuration files.
# Tags
The images of this project will be published to Docker Hub under the repository [xiaoyao9184/watermark-anything](https://hub.docker.com/r/xiaoyao9184/watermark-anything).
Since this project references the watermark-anything project via a submodule, it cannot monitor push events on the watermark-anything project, and therefore cannot automatically create an image for every commit.
A good solution is to manually trigger the action and tag it with the commit id. For more details, see this article [set-dynamic-parameters-github-workflows-en](https://damienaicheh.github.io/github/actions/2022/01/20/set-dynamic-parameters-github-workflows-en.html).
The default image name format is `${DOCKERHUB_USERNAME}/watermark-anything`.
The tag uses the input parameter `commit_id`,
which can be either a branch name or a commit id,
when manually triggering the [docker-image-tag-commit](./.github/workflows/docker-image-tag-commit.yml) job.
if the job is triggered by a submodule update push,
the default branch name `master` will be used instead of the `commit_id` parameter.
This job will also use the shortened commit id as the tag.
Currently, only the `linux/amd64` platform is supported.
# Model
The models of this project will be synced to HuggingFace under the model [xiaoyao9184/watermark-anything](https://huggingface.co/xiaoyao9184/watermark-anything).
The Docker image does not include model files.
When running, the required models will be automatically downloaded.
If you need to run offline, you must pre-download the model files and enable offline mode.
See [cache/README.md](./cache/README.md) for detailed instructions.
# Service
By default, the Docker container runs the Jupyter Notebook, which comes from the original project.
However, this project also provides a Streamlit App and a Gradio App.
The Gradio App supports both a UI and API interface, and can even serve as an MCP server,
so it is recommended as the preferred option.
The source code for the Gradio App is located in the [gradio](./gradio) directory of this project.
A demo of this project is also available and auto-synced on Hugging Face Spaces: [xiaoyao9184/watermark-anything](https://huggingface.co/spaces/xiaoyao9184/watermark-anything)
To run the Gradio App, you can do so by modifying the Docker command. see the `up.gradio` sub-directory in the [docker](./docker) directory for details.
And this project also provides Label Studio ML Backend.
The source code for the Label Studio ML Backend is located in the [label](./label) directory of this project.
To run the Label Studio ML Backend, you can do so by modifying the Docker command. see the `up.label` sub-directory in the [docker](./docker) directory for details.
# Change
You can fork this project and build your own image. You will need to provide the following variables: `DOCKERHUB_USERNAME`, `DOCKERHUB_TOKEN`, `HF_USERNAME`, `HF_TOKEN`.
See [this](https://github.com/docker/login-action#docker-hub) for more details.