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library_name: transformers
tags:
- vision-language-model
- image-decomposition
---
# SynLayers
This repository contains the assets behind SynLayers, our two-stage image decomposition system.
At the root is the bbox-caption model. Given one image, it predicts:
- a whole-image caption
- bounding boxes for visible objects or layers
The same repo also includes the Stage 2 SynLayers pipeline to do layer decomposition.
If you want the easiest way to try the full system, please use our public demo:
[SynLayers/synlayers](https://huggingface.co/spaces/SynLayers/synlayers)
This repo is not meant to be used as a single generic `DiffusionPipeline(prompt)` model.
The full SynLayers pipeline is:
1. bbox + whole-caption prediction
2. layer decomposition into transparent RGBA outputs
If you only want the Stage 1 model at the repo root, 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")
```
If you want to see more details of our implementation, please check our paper out here: https://arxiv.org/abs/2605.15167
If you find our work useful, please consider citing:
@misc{wu2026doessyntheticlayereddesign,
title={Does Synthetic Layered Design Data Benefit Layered Design Decomposition?},
author={Kam Man Wu and Haolin Yang and Qingyu Chen and Yihu Tang and Jingye Chen and Qifeng Chen},
year={2026},
eprint={2605.15167},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2605.15167},
}
Thanks for trying SynLayers.
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