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Add a database model for storing AI-predicted tags, and add a UI for browsing and searching these tags. AI tags are generated by the Danbooru Autotagger (https://github.com/danbooru/autotagger). See that repo for details about the model. The database schema is `ai_tags (media_asset_id integer, tag_id integer, score smallint)`. This is designed to be as space-efficient as possible, since in production we have over 300 million AI-generated tags (6 million images and 50 tags per post). This amounts to over 10GB in size, plus indexes. You can search for AI tags using e.g. `ai:scenery`. You can do `ai:scenery -scenery` to find posts where the scenery tag is potentially missing, or `scenery -ai:scenery` to find posts that are potentially mistagged (or more likely where the AI missed the tag). You can browse AI tags at https://danbooru.donmai.us/ai_tags. On this page you can filter by confidence level. You can also search unposted media assets by AI tag. To generate tags, use the `autotag` script from the Autotagger repo, something like this: docker run --rm -v ~/danbooru/public/data/360x360:/images ghcr.io/danbooru/autotagger ./autotag -c -f /images | gzip > tags.csv.gz To import tags, use the fix script in script/fixes/. Expect a Danbooru-size dataset to take hours to days to generate tags, then 20-30 minutes to import. Currently this all has to be done by hand.
Quickstart
Run this to start a basic Danbooru instance:
curl -sSL https://raw.githubusercontent.com/danbooru/danbooru/master/bin/danbooru | sh
This will install Docker Compose and use it to start Danbooru. When it's done, Danbooru will be running at http://localhost:3000.
Alternatively, if you already have Docker Compose installed, you can just do:
wget https://raw.githubusercontent.com/danbooru/danbooru/master/docker-compose.yaml
docker-compose up
Manual Installation
Follow the INSTALL.debian script to install Danbooru.
The INSTALL.debian script is written for Debian, but can be adapted for other distributions. Danbooru has been successfully installed on Debian, Ubuntu, Fedora, Arch, and OS X. It is recommended that you use an Ubuntu-based system since Ubuntu is what is used in development and production.
See here for a guide on how set up Danbooru inside a virtual machine.
For best performance, you will need at least 256MB of RAM for PostgreSQL and Rails. The memory requirement will grow as your database gets bigger.
In production, Danbooru uses PostgreSQL 10.18, but any release later than this should work.
Troubleshooting
If your setup is not working, here are the steps I usually recommend to people:
-
Test the database. Make sure you can connect to it using
psql. Make sure the tables exist. If this fails, you need to work on correctly installing PostgreSQL, importing the initial schema, and running the migrations. -
Test the Rails database connection by using
bin/rails console. RunPost.countto make sure Rails can connect to the database. If this fails, you need to make sure your Danbooru configuration files are correct. -
Test Nginx to make sure it's working correctly. You may need to debug your Nginx configuration file.
-
Check all log files.
Services
Danboou depends on a couple of cloud services and several microservices to implement certain features.
Amazon Web Services
The following features require an Amazon AWS account:
- Pool history
- Post history
Google APIs
The following features require a Google Cloud account:
- BigQuery database export
IQDB Service
IQDB integration is delegated to the IQDB service.
Archive Service
In order to access pool and post histories you will need to install and configure the Archives service.
Reportbooru Service
The following features are delegated to the Reportbooru service:
- Post views
- Missed searches report
- Popular searches report
Recommender Service
Post recommendations require the Recommender service.