Instructions to use CNcreator0331/DomainShuttle_weight with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use CNcreator0331/DomainShuttle_weight with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("CNcreator0331/DomainShuttle_weight", 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 model card, link to paper and project page
#2
by nielsr HF Staff - opened
README.md
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license:
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license: apache-2.0
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pipeline_tag: text-to-video
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# DomainShuttle: Freeform Open Domain Subject-driven Text-to-video Generation
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This repository contains the weights for **DomainShuttle**, an open-domain subject-driven text-to-video (S2V) generation method presented in [DomainShuttle: Freeform Open Domain Subject-driven Text-to-video Generation](https://huggingface.co/papers/2606.26058).
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DomainShuttle achieves both high subject fidelity and generative flexibility across diverse open-domain video personalization scenarios by decoupling reference and video features.
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- **Project Page:** [DomainShuttle Project Page](https://cn-makers.github.io/DomainShuttle/)
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- **Code:** [GitHub Repository](https://github.com/HKUST-C4G/DomainShuttle)
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## Citation
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If you find our work useful in your research, please consider citing:
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```bibtex
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@article{chen2026domainshuttle,
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title={DomainShuttle: Freeform Open Domain Subject-driven Text-to-video Generation},
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author={Chen, Nan and Cai, Yiyang Caps and Xie, Rongchang and Pan, Junwen and Chen, Cheng and Jia, Weinan and Chen, Zhuowei and Zhou, Wen and Sun, Zhenbang and Luo, Wenhan},
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journal={arXiv preprint arXiv:2606.26058},
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year={2026}
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}
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```
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