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| Autori principali: | , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Accesso online: | https://arxiv.org/abs/2508.03320 |
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| _version_ | 1866911093115322368 |
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| author | Wang, Peiyu Peng, Yi Gan, Yimeng Hu, Liang Xie, Tianyidan Wang, Xiaokun Wei, Yichen Tang, Chuanxin Zhu, Bo Li, Changshi Wei, Hongyang Li, Eric Song, Xuchen Liu, Yang Zhou, Yahui |
| author_facet | Wang, Peiyu Peng, Yi Gan, Yimeng Hu, Liang Xie, Tianyidan Wang, Xiaokun Wei, Yichen Tang, Chuanxin Zhu, Bo Li, Changshi Wei, Hongyang Li, Eric Song, Xuchen Liu, Yang Zhou, Yahui |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_03320 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Skywork UniPic: Unified Autoregressive Modeling for Visual Understanding and Generation Wang, Peiyu Peng, Yi Gan, Yimeng Hu, Liang Xie, Tianyidan Wang, Xiaokun Wei, Yichen Tang, Chuanxin Zhu, Bo Li, Changshi Wei, Hongyang Li, Eric Song, Xuchen Liu, Yang Zhou, Yahui Computer Vision and Pattern Recognition 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. |
| title | Skywork UniPic: Unified Autoregressive Modeling for Visual Understanding and Generation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.03320 |