UniWorld

PKU-YuanGroup更新放缓

用高分辨率语义编码器把视觉理解与图像生成统一到一个模型里,既能读懂图片,也能做文生图和图像编辑。

UniWorld: High-Resolution Semantic Encoders for Unified Visual Understanding and Generation

v2.0.0发布于 2025-10-23 08:03 UTC894star31fork同步于 2026-11-07 07:31 UTC

怎么上手

由 README 提炼,执行前请确认
README 未给出安装命令,各子项目说明见 UniWorld-V1/README.md、UniWorld-V2/README.md、UniWorld-OSP2.0/README.md
硬件要求按 README 说明

README 未说明显存与 GPU 型号要求;OSP2.0 为 14B 参数模型,V2 基于 FLUX.1 与 Qwen-Image-Edit,实际显存需求需自行评估

下载:源码 zip·GitHub Releases(安装包)本站不镜像安装包

项目说明

由 README 自动翻译提炼

北大团队的统一视觉理解与生成框架,覆盖图像编辑、文生图与图生视频

用高分辨率语义编码器把视觉理解与图像生成统一到一个模型里,既能读懂图片,也能做文生图和图像编辑。

核心能力

  • 用高分辨率语义编码器统一理解与生成任务
  • UniWorld-V2 用 DiffusionNFT 与 MLLM 奖励微调图像编辑
  • UniWorld-OSP2.0 以 7B VLM 条件机制做图生视频
  • 开源 V1 的数据、模型、训练与评测代码
  • 覆盖 20+ 视觉任务的统一框架

容易踩的坑

模型权重需从 Hugging Face 单独下载,仓库不含权重;FLUX 权重受 FLUX.1 [dev] 非商用许可限制;V1、V2、OSP2.0 是三个独立子项目,需分别按其 README 配置环境

内容由模型根据 README 生成,可能不完整或不准确,以官方文档为准。看原文 README

最近发版

2 个版本v2.0.0
  1. v2.0.0Release v2.0.0最新正式版2025-10-23 · 2025-10-23 08:03 UTC

    🚀 Introducing UniWorld-V2: Reinforce Image Editing with Diffusion Negative-aware Finetuning and MLLM Implicit Feedback! 🌟 Surpassing GPT-Image-1 on multiple benchmarks, it showcases superior fine-grained control and complex language capabilities! Now, the new model, training framework, and evaluation results are fully open-source!

    ✨ Key Highlights

    1. 🧠 We introduce UniWorld-R1, the industry's post-training framework for image editing based on Reinforcement Learning (RL) policy optimization. It leverages our novel DiffusionNFT technique for more efficient training and compatibility with high-order samplers.
    2. 🏆 We pioneer the use of a Multi-modal Large Language Model (MLLM) as a training-free reward model. By leveraging its logits output for fine-grained feedback, we significantly improve the model's alignment with human intent.
    3. 🥇 UniWorld-V2 achieves new SOTA results, scoring an impressive 7.83 on GEdit-Bench (surpassing GPT-Image-1's 7.53) and leading on ImgEdit with 4.49, outperforming all known open and closed-source models.
    4. 🎨 We demonstrate unprecedented fine-grained controllability, including mastering complex artistic Chinese characters, achieving precise spatial editing with "Redbox Control," and rendering realistic global light & shadow fusion—capabilities that are challenging for traditional SFT models.

    🔭 Future Work

    1. Continue collecting data and explore joint training with Visual Language Models (VLMs).
    2. Integrate higher-resolution semantic encoders or adopt VLM techniques like multi-scale image gridding to increase input image resolution.
  2. v1.0.0Release v1.0.02025-06-04 · 2025-06-04 06:50 UTC
    查看更新说明

    🚀UniWorld: a unified model that skips VAEs and uses semantic features from SigLIP! Using just 1% of BAGEL’s data, it outperforms on image editing and excels in understanding & generation. 🌟Now data, model, training & evaluation script are open-source!

    Key features:

    1. We observe that GPT-4o likely employs a non-mandatory VAE injection, making it difficult to preserve low-level features consistent with the reference image.
    2. We demonstrate remarkable image perception capabilities, surpassing those of GPT-4o.
    3. We used only 2.7M data samples—just 0.1% of BAGEL—achieving high efficiency. All data, training and evaluation code, and models have been fully open-sourced.

    Future works:

    1. Continue collecting data and perform joint training with a VLM.
    2. Integrate higher-resolution semantic encoders or adopt VLM techniques to increase input-image resolution, such as multi-scale image gridding.

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    README 原文

    未翻译

    UniWorld-V2 UniWorld-V1 hf_paper hf_paper model model data License Twitter GitHub repo stars  GitHub repo forks  GitHub repo watchers  GitHub repo size GitHub repo contributors GitHub Commit Pr GitHub issues GitHub closed issues

    📣 News

    • [2025/11/25]:🤗 We release Uniworld-OSP2.0, a VLM-Enhanced Unified Framework for Image-to-Video Generation. The architecture scales FlashI2V to 14B parameters and introduces a novel conditioning mechanism based on a 7B VLM to losslessly inherit powerful semantic understanding. Uniworld-OSP2.0 surpasses the video generation model Wan2.1 across six key evaluation metrics on Vbench-I2V.

    • [2025/10/19]: We release UniWorld-V2, which employs DiffusionNFT and a training-free reward model derived from pretrained MLLMs to fine-tune diffusion models for image editing. UniWorld-Qwen-Image-Edit-2509 and UniWorld-FLUX.1-Kontext-Dev are open-sourced.

    • [2025.06.03] 🤗 We release UniWorld-V1, a unified framework for understanding, generation, and editing. All data, models, training code, and evaluation code are open-sourced. Checking our report for more details. Welcome to watch 👀 this repository for the latest updates.

    💡 Hub

    😍 Gallery

    UniWorld-OSP2.0

    ModelI2V ParadigmSubject Consistency ↑Background Consistency ↑Motion Smoothness ↑Dynamic Degree ↑Aesthetic Quality ↑Imaging Quality ↑I2V Subject Consistency ↑I2V Background Consistency ↑
    SVD-XT-1.0 (1.5B)Repeating Concat and Adding Noise95.5296.6198.0952.3660.1569.8097.5297.63
    SVD-XT-1.1 (1.5B)Repeating Concat and Adding Noise95.4296.7798.1243.1760.2370.2397.5197.62
    SEINE-512x512 (1.8B)Inpainting95.2897.1297.1227.0764.5571.3997.1596.94
    CogVideoX-5B-I2VZero-padding Concat and Adding Noise94.3496.4298.4033.1761.8770.0197.1996.74
    Wan2.1-I2V-14B-720PInpainting94.8697.0797.9051.3864.7570.4496.9596.44
    CogVideoX1.5-5B-I2VZero-padding Concat and Adding Noise95.0496.5298.4737.4862.6870.9997.7898.73
    Wan2.1-I2V-14B-480PInpainting95.6897.4498.4645.2061.4470.3797.8399.08
    Uniworld-OSP2.0FlashI2V96.2197.7198.4746.1066.5570.5797.9998.94

    UniWorld-V2

    OriginalPromptNano-bananaGPT-4oQwen-Image-EditUniWorld-V2 (Ours)
    Case 1: 把鸟移动到红框里,删除掉现在的鸟,最后移除红框 (✅正确执行指令)
    Case 2: 把中间白色衣服戴口罩女生的手势改成OK (✅OK手势 )
    Case 3: 提取画面中的吉他(✅弦钮上二下三 )
    Case 4: 把下面的所有文字并改用书法体。中间的“月满中秋”改成“千里团圆”。并且把月亮改成模糊的月饼。 (✅模糊月饼,✅书法字体)
    Case 5: 让画面中的形象坐在高档西餐厅,双手拿刀叉吃牛排 (✅人物特征,✅刀叉)

    UniWorld-V1

    UniWorld-V1 shows excellent performance in 20+ tasks.

    Click to play

    🔒 License

    ✏️ Citing

    @article{li2025uniworldv2,
        title={Uniworld-V2: Reinforce Image Editing with Diffusion Negative-aware Finetuning and MLLM Implicit Feedback},
        author={Li, Zongjian and Liu, Zheyuan and Zhang, Qihui and Lin, Bin and Yuan, Shenghai and Yan, Zhiyuan and Ye, Yang and Yu, Wangbo and Niu, Yuwei and Yuan, Li},
        journal={arXiv preprint arXiv:2510.16888},
        year={2025}
    }
    @article{lin2025uniworld,
      title={UniWorld: High-Resolution Semantic Encoders for Unified Visual Understanding and Generation},
      author={Lin, Bin and Li, Zongjian and Cheng, Xinhua and Niu, Yuwei and Ye, Yang and He, Xianyi and Yuan, Shenghai and Yu, Wangbo and Wang, Shaodong and Ge, Yunyang and others},
      journal={arXiv preprint arXiv:2506.03147},
      year={2025}
    }
    @article{ye2025imgedit,
      title={ImgEdit: A Unified Image Editing Dataset and Benchmark},
      author={Ye, Yang and He, Xianyi and Li, Zongjian and Lin, Bin and Yuan, Shenghai and Yan, Zhiyuan and Hou, Bohan and Yuan, Li},
      journal={arXiv preprint arXiv:2505.20275},
      year={2025}
    }
    @article{niu2025wise,
      title={Wise: A world knowledge-informed semantic evaluation for text-to-image generation},
      author={Niu, Yuwei and Ning, Munan and Zheng, Mengren and Lin, Bin and Jin, Peng and Liao, Jiaqi and Ning, Kunpeng and Zhu, Bin and Yuan, Li},
      journal={arXiv preprint arXiv:2503.07265},
      year={2025}
    }
    @article{yan2025gpt,
      title={Gpt-imgeval: A comprehensive benchmark for diagnosing gpt4o in image generation},
      author={Yan, Zhiyuan and Ye, Junyan and Li, Weijia and Huang, Zilong and Yuan, Shenghai and He, Xiangyang and Lin, Kaiqing and He, Jun and He, Conghui and Yuan, Li},
      journal={arXiv preprint arXiv:2504.02782},
      year={2025}
    }
    @article{lin2024open,
      title={Open-Sora Plan: Open-Source Large Video Generation Model},
      author={Lin, Bin and Ge, Yunyang and Cheng, Xinhua and Li, Zongjian and Zhu, Bin and Wang, Shaodong and He, Xianyi and Ye, Yang and Yuan, Shenghai and Chen, Liuhan and others},
      journal={arXiv preprint arXiv:2412.00131},
      year={2024}
    }
    

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