Model reference · open weights

Skywork-UniPic

LLMs Skywork Omni (any→any) 1 build Open weights 25 dl/mo

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.

  • Skywork-UniPic is a unified autoregressive multimodal model developed by Skywork with 1.5 billion parameters.
  • It supports image understanding, text-to-image generation, and image editing within a single architecture.
  • The model is released under the MIT license.

Summary of the Skywork/Skywork-UniPic-1.5B model card, 2026-10-01

What it is

Released bySkywork
Released2025-07-29
VRAM7.6 GB for the weights

What it runs on

Memory and cards for Skywork-UniPic-1.5B (BF16)

7.6 GBweights, file size
762 MBruntime overhead, at least

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.

CardWeights alone
RTX 3060 12 GBfits
RTX 4060 Ti 16 GBfits
RTX 3090 24 GBfits
RTX 4090 24 GBfits
RTX 5090 32 GBfits
L40S 48 GBfits
A100 80 GBfits
H100 80 GBfits
RTX PRO 6000 Blackwell 96 GBfits
DGX Spark (GB10) 128 GB unifiedfits
H200 141 GBfits
B200 180 GBfits

From the model card

What Skywork says about Skywork-UniPic

Read the model card

📖 Introduction

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:

  • 🖼️ Image Understanding
  • 🎨 Text-to-Image Generation
  • ✏️ Image Editing

Trained from scratch on a large-scale multimodal corpus, UniPic is designed to support a wide range of unified image-text tasks efficiently.


📊 Benchmarks

Skywork-UniPic achieves competitive results across a variety of vision-language tasks:

TaskScore
🧠 GenEval0.86
🖼️ DPG-Bench85.5
✂️ GEditBench-EN5.83
🧪 ImgEdit-Bench3.49

🧠 Usage

1. Clone the Repository

git clone https://github.com/SkyworkAI/UniPic
cd UniPic-1

2. Set Up the Environment

conda create -n unipic python=3.10.14
conda activate unipic
pip install -r requirements.txt

3.Text-to-Image Generation

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

4. Image Editing

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

📄 License

This model is released under the MIT License.

Citation

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

How language models work

Your prompttext / messagesTransformerattention over tokensNext-token loopgenerate + streamResponsetext · tool callsA language model reads your tokens and predicts the next one, again and again, streaming the reply back.
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