Skywork UniPic: Unified Autoregressive Modeling for Visual Understanding
  and Generation
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.
6892bd5c8da45ffb0a2b23fe
Peiyu Wang
6892bd5c8da45ffb0a2b23ff
Yi Peng
6892bd5c8da45ffb0a2b2400
Yimeng Gan
6892bd5c8da45ffb0a2b2401
Liang Hu
6892bd5c8da45ffb0a2b2402
Tianyidan Xie
6892bd5c8da45ffb0a2b2403
Xiaokun Wang
6892bd5c8da45ffb0a2b2404
Yichen Wei
6892bd5c8da45ffb0a2b2405
Chuanxin Tang
6892bd5c8da45ffb0a2b2406
Bo Zhu
6892bd5c8da45ffb0a2b2407
Changshi Li
6892bd5c8da45ffb0a2b2408
Hongyang Wei
6892bd5c8da45ffb0a2b2409
Eric Li
6892bd5c8da45ffb0a2b240a
Xuchen Song
6892bd5c8da45ffb0a2b240b
Yang Liu
6892bd5c8da45ffb0a2b240c
Yahui Zhou
Skywork UniPic: Unified Autoregressive Modeling for Visual Understanding
  and Generation
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.
Skywork UniPic, a 1.5 billion-parameter autoregressive model, unifies image understanding, text-to-image generation, and image editing with state-of-the-art performance on commodity hardware.
autoregressive model image understanding text-to-image generation image editing GenEval DPG-Bench GEditBench-EN ImgEdit-Bench decoupled encoding strategy masked autoregressive encoder SigLIP2 encoder shared autoregressive decoder progressive training schedule resolution-aware training parameter unfreezing high-fidelity multimodal integration