Instructions to use Drexubery/UniView with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Drexubery/UniView with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image, export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Drexubery/UniView", dtype=torch.bfloat16, device_map="cuda") pipe.to("cuda") prompt = "A man with short gray hair plays a red electric guitar." image = load_image( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png" ) output = pipe(image=image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Notebooks
- Google Colab
- Kaggle
Add model card for UniWorld-View
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by nielsr HF Staff - opened
README.md
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license: apache-2.0
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---
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license: apache-2.0
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pipeline_tag: image-to-video
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---
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# UniWorld-View: Large-Baseline View Synthesis via Video Diffusion Models
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UniWorld-View is a unified framework for controllable large-baseline novel view synthesis from monocular inputs (casual videos or single images) using video diffusion models.
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- **Paper:** [UniWorld-View: Large-Baseline View Synthesis via Video Diffusion Models](https://huggingface.co/papers/2608.04701)
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- **Project Page:** [https://zhouhyocean.github.io/uniworld-view/](https://zhouhyocean.github.io/uniworld-view/)
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- **GitHub Repository:** [https://github.com/PKU-YuanGroup/UniWorld-View](https://github.com/PKU-YuanGroup/UniWorld-View)
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## Citation
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```bibtex
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@misc{zhou2026uniworldviewlargebaselineviewsynthesis,
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title={UniWorld-View: Large-Baseline View Synthesis via Video Diffusion Models},
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author={Haiyang Zhou and Wangbo Yu and Chaoran Feng and Xunyu Zhou and Yonghong Tian and Li Yuan},
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year={2026},
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eprint={2608.04701},
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archivePrefix={arXiv},
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primaryClass={cs.CV},
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url={https://arxiv.org/abs/2608.04701},
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}
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```
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