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
claimed_verified
2025-08-06T19:17:26.595Z
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Peiyu Wang
user
OrlandoHugBot
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Yi Peng
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Yimeng Gan
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Liang Hu
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Tianyidan Xie
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Xiaokun Wang
claimed_verified
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wangxiaokun
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shawn0wang
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Yichen Wei
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Chuanxin Tang
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Bo Zhu
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Changshi Li
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Hongyang Wei
claimed_verified
2025-08-14T13:40:20.904Z
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Hongyang Wei
user
nonwhy
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Eric Li
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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