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

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
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.mdREADME 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
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
- 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
- 🧠 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.
- 🏆 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.
- 🥇 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.
- 🎨 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
- Continue collecting data and explore joint training with Visual Language Models (VLMs).
- Integrate higher-resolution semantic encoders or adopt VLM techniques like multi-scale image gridding to increase input image resolution.
- 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:
- 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.
- We demonstrate remarkable image perception capabilities, surpassing those of GPT-4o.
- 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:
- Continue collecting data and perform joint training with a VLM.
- Integrate higher-resolution semantic encoders or adopt VLM techniques to increase input-image resolution, such as multi-scale image gridding.
用户点评
登录用户的真实使用记录,可按版本和硬件筛选还没有人点评。第一个说出真实体验的人最有价值。
📣 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
| Model | I2V Paradigm | Subject 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 Noise | 95.52 | 96.61 | 98.09 | 52.36 | 60.15 | 69.80 | 97.52 | 97.63 |
| SVD-XT-1.1 (1.5B) | Repeating Concat and Adding Noise | 95.42 | 96.77 | 98.12 | 43.17 | 60.23 | 70.23 | 97.51 | 97.62 |
| SEINE-512x512 (1.8B) | Inpainting | 95.28 | 97.12 | 97.12 | 27.07 | 64.55 | 71.39 | 97.15 | 96.94 |
| CogVideoX-5B-I2V | Zero-padding Concat and Adding Noise | 94.34 | 96.42 | 98.40 | 33.17 | 61.87 | 70.01 | 97.19 | 96.74 |
| Wan2.1-I2V-14B-720P | Inpainting | 94.86 | 97.07 | 97.90 | 51.38 | 64.75 | 70.44 | 96.95 | 96.44 |
| CogVideoX1.5-5B-I2V | Zero-padding Concat and Adding Noise | 95.04 | 96.52 | 98.47 | 37.48 | 62.68 | 70.99 | 97.78 | 98.73 |
| Wan2.1-I2V-14B-480P | Inpainting | 95.68 | 97.44 | 98.46 | 45.20 | 61.44 | 70.37 | 97.83 | 99.08 |
| Uniworld-OSP2.0 | FlashI2V | 96.21 | 97.71 | 98.47 | 46.10 | 66.55 | 70.57 | 97.99 | 98.94 |
UniWorld-V2
| Original | Prompt | Nano-banana | GPT-4o | Qwen-Image-Edit | UniWorld-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
- See LICENSE for details. The FLUX weights fall under the FLUX.1 [dev] Non-Commercial 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}
}