Model reference · open weights
Skywork-UniPic is an open-weight language model from Skywork. Skywork-UniPic-1.5B (BF16) weighs 7.6 GB; the smallest configuration that runs it is RTX 3060 12 GB.
Summary of the Skywork/Skywork-UniPic-1.5B model card, 2026-10-01
What it is
| Released by | Skywork |
|---|---|
| Released | 2025-07-29 |
| VRAM | 7.6 GB for the weights |
What it runs on
How much memory each request adds isn't estimated yet for this architecture. The weights need at least the cards below, plus room for the context.
| Card | Weights alone |
|---|---|
| RTX 3060 12 GB | fits |
| RTX 4060 Ti 16 GB | fits |
| RTX 3090 24 GB | fits |
| RTX 4090 24 GB | fits |
| RTX 5090 32 GB | fits |
| L40S 48 GB | fits |
| A100 80 GB | fits |
| H100 80 GB | fits |
| RTX PRO 6000 Blackwell 96 GB | fits |
| DGX Spark (GB10) 128 GB unified | fits |
| H200 141 GB | fits |
| B200 180 GB | fits |
From the model card
Skywork-UniPic is a unified autoregressive multimodal model with 1.5 billion parameters, capable of handling three key vision-language tasks within a single architecture:
Trained from scratch on a large-scale multimodal corpus, UniPic is designed to support a wide range of unified image-text tasks efficiently.
Skywork-UniPic achieves competitive results across a variety of vision-language tasks:
| Task | Score |
|---|---|
| 🧠 GenEval | 0.86 |
| 🖼️ DPG-Bench | 85.5 |
| ✂️ GEditBench-EN | 5.83 |
| 🧪 ImgEdit-Bench | 3.49 |
git clone https://github.com/SkyworkAI/UniPic
cd UniPic-1
conda create -n unipic python=3.10.14
conda activate unipic
pip install -r requirements.txt
export PYTHONPATH=./:$PYTHONPATH
python scripts/text2image.py configs/models/qwen2_5_1_5b_kl16_mar_h.py \
--checkpoint checkpoint/pytorch_model.bin \
--image_size 1024 \
--prompt "A glossy-coated golden retriever stands on the park lawn beside a life-sized penguin statue." \
--output output.jpg
The image editing feature within this unified model is an exploratory module at the forefront of research. And it is not yet production-ready.
export PYTHONPATH=./:$PYTHONPATH
python scripts/image_edit.py configs/models/qwen2_5_1_5b_kl16_mar_h.py \
--checkpoint checkpoint/pytorch_model.bin \
--image_size 1024 \
--image data/sample.png \
--prompt "Replace the stars with the candle." \
--output output.jpg
This model is released under the MIT License.
If you use Skywork-UniPic in your research, please cite:
@misc{wang2025skyworkunipicunifiedautoregressive,
title={Skywork UniPic: Unified Autoregressive Modeling for Visual Understanding and Generation},
author={Peiyu Wang and Yi Peng and Yimeng Gan and Liang Hu and Tianyidan Xie and Xiaokun Wang and Yichen Wei and Chuanxin Tang and Bo Zhu and Changshi Li and Hongyang Wei and Eric Li and Xuchen Song and Yang Liu and Yahui Zhou},
year={2025},
eprint={2508.03320},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2508.03320},
}
@misc{wei2025skyworkunipic20building,
title={Skywork UniPic 2.0: Building Kontext Model with Online RL for Unified Multimodal Model},
author={Hongyang Wei and Baixin Xu and Hongbo Liu and Cyrus Wu and Jie Liu and Yi Peng and Peiyu Wang and Zexiang Liu and Jingwen He and Yidan Xietian and Chuanxin Tang and Zidong Wang and Yichen Wei and Liang Hu and Boyi Jiang and William Li and Ying He and Yang Liu and Xuchen Song and Eric Li and Yahui Zhou},
year={2025},
eprint={2509.04548},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2509.04548},
}
Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.
How it works