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| license: apache-2.0 |
| pipeline_tag: image-to-text |
| tags: |
| - genlip |
| - tipsv2 |
| - vision |
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| ViT-B model |
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| <span style="color:red; border: 1px solid black;">Please do not download the model. The repo was kept for archival purposes.</span> |
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| The vision encoder can be pretrained with autoregressive language modeling objective - no contrastive loss, no dual-tower architecture, and no extra text decoder. |
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| The output includes both visual and textual data. The model encodes [text token](https://huggingface.co/nebulette/booru-character-aware-tokenizer) positions using ALiBi bias in attention. |
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| Causal attention was applied to text tokens, while fewer vision tokens were visited in the middle blocks. This resulted in a faster training cycle. |
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| SV(O) booru tags are applied with increased weighting in the loss calculation (2605.00809, figure 4) for both solo and multi-character images. |
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| Source data |
| - danbooru 2025-26 |
| - gelbooru for a certain locked tag |
| - Kimi K2 style visual descriptions from multiple thinking models |
| - cell1/tagutl was used for short tags until v0.5; a newer model was used later on |
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| References |
| - 2108.12409 |
| - 2501.04765 |
| - 2604.12012 |
| - 2605.00809 |
