An online AI image search engine based on the Clip model and Qdrant vector database. Supports keyword search and similar image search.
- Use the Clip model to generate 768-dimensional vectors for each image as the basis for search. No need for manual annotation or classification, unlimited classification categories.
- OCR Text search is supported, use PaddleOCR to extract text from images and use BERT to generate text vectors for search.
- Use Qdrant vector database for efficient vector search.
The above screenshots may contain copyrighted images from different artists, please do not use them for other purposes.
In most cases, we recommend using the Qdrant database to store metadata. The Qdrant database provides efficient retrieval performance, flexible scalability, and better data security.
Please deploy the Qdrant database according to the Qdrant documentation. It is recommended to use Docker for deployment.
If you don't want to deploy Qdrant yourself, you can use the online service provided by Qdrant.
Local file storage directly stores image metadata (including feature vectors, etc.) in a local SQLite database. It is only recommended for small-scale deployments or development deployments.
Local file storage does not require an additional database deployment process, but has the following disadvantages:
- Local storage does not index and optimize vectors, so the time complexity of all searches is
O(n). Therefore, if the data scale is large, the performance of search and indexing will decrease. - Using local file storage will make NekoImageGallery stateful, so it will lose horizontal scalability.
- When you want to migrate to Qdrant database for storage, the indexed metadata may be difficult to migrate directly.
- Clone the project directory to your own PC or server, then checkout to a specific version tag (like
v1.0.0). - It is highly recommended to install the dependencies required for this project in a Python venv virtual environment.
Run the following command:
python -m venv .venv . .venv/bin/activate - Install PyTorch. Follow the PyTorch documentation to install the torch
version suitable for your system using pip.
If you want to use CUDA acceleration for inference, be sure to install a CUDA-supported PyTorch version in this step. After installation, you can use
torch.cuda.is_available()to confirm whether CUDA is available. - Install other dependencies required for this project:
pip install -r requirements.txt
- Modify the project configuration file inside
config/, you can editdefault.envdirectly, but it's recommended to create a new file namedlocal.envand override the configuration indefault.env. - Run this application:
You can use
python main.py
--hostto specify the IP address you want to bind to (default is 0.0.0.0) and--portto specify the port you want to bind to (default is 8000).
You can see all available commands and options by runningpython main.py --help. - (Optional) Deploy the front-end application: NekoImageGallery.App is a simple web front-end application for this project. If you want to deploy it, please refer to its deployment documentation.
NekoImageGallery's docker image are built and released on Docker Hub, including serval variants:
| Tags | Description | Latest Image Size |
|---|---|---|
edgeneko/neko-image-gallery:<version>edgeneko/neko-image-gallery:<version>-cudaedgeneko/neko-image-gallery:<version>-cuda12.1 |
Supports GPU inferencing with CUDA12.1 | ) |
edgeneko/neko-image-gallery:<version>-cuda11.8 |
Supports GPU inferencing with CUDA11.8 | ) |
edgeneko/neko-image-gallery:<version>-cpu |
Only supports CPU inferencing | ) |
Where <version> is the version number or version alias of NekoImageGallery, as follows:
| Version | Description |
|---|---|
latest |
The latest stable version of NekoImageGallery |
v*.*.* / v*.* |
The specific version number (correspond to Git tags) |
edge |
The latest development version of NekoImageGallery, may contain unstable features and breaking changes |
In each image, we have bundled the necessary dependencies, openai/clip-vit-large-patch14 model
weights, bert-base-chinese model weights and easy-paddle-ocr models to provide a complete and ready-to-use image.
The images uses /opt/NekoImageGallery/static as volume to store image files, mount it to your own volume or directory
if local storage is required.
For configuration, we suggest using environment variables to override the default configuration. Secrets (like API tokens) can be provided by docker secrets.
If you want to use CUDA acceleration, you need to install nvidia-container-runtime on your system. Please refer to
the official documentation for installation.
Related Document:
- Download the
docker-compose.ymlfile from repository.# For cuda deployment (default) wget https://raw.githubusercontent.com/hv0905/NekoImageGallery/master/docker-compose.yml # For CPU-only deployment wget https://raw.githubusercontent.com/hv0905/NekoImageGallery/master/docker-compose-cpu.yml && mv docker-compose-cpu.yml docker-compose.yml
- Modify the docker-compose.yml file as needed
- Run the following command to start the server:
# start in foreground docker compose up # start in background(detached mode) docker compose up -d
There are serval ways to upload images to NekoImageGallery
- Through the web interface: You can use the web interface to upload images to the server. The web interface is provided by NekoImageGallery.App. Make sure you have enabled the Admin API and set your Admin Token in the configuration file.
- Through local indexing: This is suitable for local deployment or when the images you want to upload are already on the
server.
Use the following command to index your local image directory:
The above command will recursively upload all images in the specified directory and its subdirectories to the server. You can also specify categories/starred for images you upload, see
python main.py local-index <path-to-your-image-directory>
python main.py local-index --helpfor more information. - Through the API: You can use the upload API provided by NekoImageGallery to upload images. By using this method, the
server can prevent saving the image files locally but only store their URLs and metadata.
Make sure you have enabled the Admin API and set your Admin Token in the configuration file.
This method is suitable for automated image uploading or sync NekoImageGallery with external systems. Checkout API documentation for more information.
The API documentation is provided by FastAPI's built-in Swagger UI. You can access the API documentation by visiting
the /docs or /redoc path of the server.
Those project works with NekoImageGallery :D
There are many ways to contribute to the project: logging bugs, submitting pull requests, reporting issues, and creating suggestions.
Even if you with push access on the repository, you should create a personal feature branches when you need them. This keeps the main repository clean and your workflow cruft out of sight.
We're also interested in your feedback on the future of this project. You can submit a suggestion or feature request through the issue tracker. To make this process more effective, we're asking that these include more information to help define them more clearly.
Copyright 2023 EdgeNeko
Licensed under AGPLv3 license.