Qwen text adapter for LingBot Vision Giant This is a small experimental adapter model that aligns Qwen3.5 0.8B text features with frozen LingBot Vision Giant patch features. It is mostly just an experiment to add some of Qwen's text understanding to LingBot via training a simple text queryable tag head on top of it. Model LingBot G supplies the image representation and Qwen supplies one cached 1024 dimensional vector for each tag. A small trainable head projects both into a normalized 256 dimensional space, where a learned scale is applied to their cosine similarity to give a response for every image patch and tag. Only the ~1.58M parameter alignment head is trained. For the bundled vocabulary, Qwen is run once to encode the tags. Those cached features are stored with the adapter, so Qwen is not required at inference time unless new text queries need to be encoded. Training The adapter was trained with image level supervision from Booru Essence and took about 2 hours on an rtx 5090: 39,576 training images and 2,107 validation images 2,048 training concepts 384 x 384 letterboxed inputs and batch size 16 random horizontal flips with 50% probability binary cross entropy with per tag p…
We use cookies for essential functionality and analytics. You can accept or reject analytics cookies.Cookie policy