README.md

---
library_name: transformers
pipeline_tag: image-text-to-text
tags:
  - vision-language-model
  - image-to-text
  - bounding-box-detection
  - synlayers
---

# SynLayers Bbox-Caption Model

This repository hosts the **Stage 1 bbox-caption model** for SynLayers.

Given an input image, the model predicts:

- a whole-image caption
- bounding boxes for visible objects or layers

This repository is only for the Stage 1 detector. The full SynLayers system has two stages:

1. bbox + whole-caption prediction from this repo
2. layer decomposition into transparent RGBA outputs using the Stage 2 checkpoints

For the complete demo, please use our public Space:
[SynLayers/synlayers](https://huggingface.co/spaces/SynLayers/synlayers)

For the Stage 2 decomposition checkpoints and runtime assets, please see:
[SynLayers/synlayers](https://huggingface.co/SynLayers/synlayers)

This repo is not intended to be loaded as a generic `DiffusionPipeline(prompt)` model. If you only want the Stage 1 model, you can load it with `transformers`:

```python
from transformers import AutoProcessor, Qwen3VLForConditionalGeneration

model = Qwen3VLForConditionalGeneration.from_pretrained(
    "SynLayers/Bbox-caption-8b",
    torch_dtype="auto",
    device_map="auto",
)
processor = AutoProcessor.from_pretrained("SynLayers/Bbox-caption-8b")
```

This repository also includes lightweight inference helpers under `demo/infer/`. To run whole-caption and bbox inference on a folder of images:

```bash
python demo/infer/run_caption_bbox_infer.py \
  --model SynLayers/Bbox-caption-8b \
  --data-dir /path/to/images \
  --output outputs/caption_bbox_infer.jsonl \
  --vis-dir outputs/bbox_vis
```

For more details, please check our paper:
[https://arxiv.org/abs/2605.15167](https://arxiv.org/abs/2605.15167)

If you find our work useful, please consider citing:

```bibtex
@article{wu2026does,
  title={Does Synthetic Layered Design Data Benefit Layered Design Decomposition?},
  author={Wu, Kam Man and Yang, Haolin and Chen, Qingyu and Tang, Yihu and Chen, Jingye and Chen, Qifeng},
  journal={arXiv preprint arXiv:2605.15167},
  year={2026}
}
```

Thanks for trying SynLayers.