1aeb52186e
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.
Initializers
This directory contains initializers run by Rails during bootup. Initializers are generally used to configure libraries or other global settings during application boot.