Skywork UniPic: Unified Autoregressive Modeling for Visual Understanding
  and Generation
Authors: name: Peiyu Wang; name: Yi Peng; name: Yimeng Gan; name: Liang Hu; name: Tianyidan Xie; name: Xiaokun Wang; name: Yichen Wei; name: Chuanxin Tang; name: Bo Zhu; name: Changshi Li; name: Hongyang Wei; name: Eric Li; name: Xuchen Song; name: Yang Liu; name: Yahui Zhou
Published: 2025-08-05 10:59:01

Abstract: We introduce Skywork UniPic, a 1.5 billion-parameter autoregressive model
that unifies image understanding, text-to-image generation, and image editing
within a single architecture-eliminating the need for task-specific adapters or
inter-module connectors-and demonstrate that compact multimodal systems can
achieve state-of-the-art performance on commodity hardware. Skywork UniPic
achieves a GenEval score of 0.86, surpassing most existing unified models; sets
a new DPG-Bench complex-generation record of 85.5; attains 5.83 on
GEditBench-EN and 3.49 on ImgEdit-Bench for image editing; and generates 1024 x
1024 images with under 15 GB of GPU memory (e.g., RTX 4090). (1) a decoupled
encoding strategy that leverages a masked autoregressive encoder for synthesis
and a SigLIP2 encoder for understanding, all feeding a shared autoregressive
decoder; (2) a progressive, resolution-aware training schedule scaling from 256
x 256 to 1024 x 1024 while dynamically unfreezing parameters to balance
capacity and stability; and (3) meticulously curated, 100 million-scale
datasets augmented with task-specific reward models to refine generation and
editing objectives. By demonstrating that high-fidelity multimodal integration
need not incur prohibitive resource demands, Skywork UniPic establishes a
practical paradigm for deployable, high-fidelity multimodal AI. Code and
weights are publicly available at
https://huggingface.co/Skywork/Skywork-UniPic-1.5B.