Instructions to use peaes/GeoWorld with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use peaes/GeoWorld with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("peaes/GeoWorld", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Add pipeline tag to metadata
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license: apache-2.0
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}
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```
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license: apache-2.0
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pipeline_tag: image-to-3d
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---
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# GeoWorld: Providing Full-frame Geometry Features to Facilitate 3D Scene Generation
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This repository contains the model weights for GeoWorld, presented in the paper [GeoWorld: Providing Full-frame Geometry Features to Facilitate 3D Scene Generation](https://huggingface.co/papers/2511.23191).
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Authors: Yuhao Wan, Lijuan Liu, Jingzhi Zhou, Zihan Zhou, Xuying Zhang, Dongbo Zhang, Shaohui Jiao, Qibin Hou, Ming-Ming Cheng
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[Project Page](https://peaes.github.io/GeoWorld/) | [Paper(arXiv)](https://arxiv.org/abs/2511.23191) | [Code](https://github.com/peaes/GeoWorld)
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TL;DR: GeoWorld uses a two-stage video-generation pipeline with full-frame geometry features to produce high-fidelity image-to-3D scenes faster than prior methods (7.5× faster than Hunyuan-Voyager).
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## Usage
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Please refer to the [official GitHub repository](https://github.com/peaes/GeoWorld) for installation instructions.
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## Citation
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```bibtex
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@article{wan2025geoworld,
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title={GeoWorld: Unlocking the Potential of Geometry Models to Facilitate High-Fidelity 3D Scene Generation},
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author={Wan, Yuhao and Liu, Lijuan and Zhou, Jingzhi and Zhou, Zihan and Zhang, Xuying and Zhang, Dongbo and Jiao, Shaohui and Hou, Qibin and Cheng, Ming-Ming},
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journal={arXiv preprint arXiv:2511.23191},
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year={2025}
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}
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```
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