| --- |
| license: apache-2.0 |
| base_model_relation: adapter |
| tags: |
| - feature-projection |
| - moss-image |
| - qwen3_5 |
| language: |
| - en |
| library_name: diffusers |
| --- |
| |
| Moqs 2B |
|
|
|
|
| The model relies on [Qwen3.5 2B](https://huggingface.co/Qwen/Qwen3.5-2B), which provides text embeddings for the MOSS Image model. |
|
|
| The previous models were based on the 1M+ corpus, but the dataset here is much smaller for now. |
|
|
| The data samples covered half of the English vocabulary from the tokenizer, including most lowercase words. |
|
|
| Based on the work done in previous years, the LoRAs are expected to work out-of-box. |
|
|
| Here's an obligatory preview image with Moqs&Spectrum |
|
|
|  |
|
|
| ### Reason for being |
|
|
| Now that [the fast inference in diffusers](https://huggingface.co/nebulette/moss-spectrum-diffusers) is complete, the dataset must |
|
|
| - include both comma seperated labels and natural language texts |
| - include labels, concepts that are not available on danbooru and other censored image boards |
| - include frequent pixiv, xitter usernames |
| - support lightweight markdown formats |
|
|
| There are no plans to include text and infographic generation, nor to support the [layout format](https://github.com/OpenMOSS/MOSS-Image/blob/main/USERGUIDE.md#layout-prompts-experimental). |
|
|
| Expanding the knowledge of the model is not an objective due to the frozen text encoder, and other modules, which restrict its ability to learn more. |
|
|
| Source data |
| - common colors, verbs |
| - danbooru_tags_classified |
| - danbooru_tag_taxonomy |
| - danbooru2026_moe |
| - danbooru_wiki_2606 |
| - gelbooru 40k (each sample has a new, unexplored booru tag) |
| - openart_painterly_foundations |
| - pexels_portrait |
| - saber (2610.22602) |
| - sdxl_artists |
| - spright_coco |
| - z_image_ethnicity |
|
|
| Common phrases were removed from the text entries, then they were refined further. |
|
|
| The model was designed to cover the most common character features, visual and spatial tasks. |
|
|
| ### Scaling |
|
|
| Multiply the output tensor by a single scalar value. These are three images at different scales |
|
|
|  |
|
|
| ## Improved bigrams |
|
|
| The model has seen the most common English words (30k), booru tags (40k) and bigrams. |
|
|
|  |
