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
| license: apache-2.0 | |
| pipeline_tag: text-to-video | |
| # DomainShuttle: Freeform Open Domain Subject-driven Text-to-video Generation | |
| 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). | |
| DomainShuttle achieves both high subject fidelity and generative flexibility across diverse open-domain video personalization scenarios by decoupling reference and video features. | |
| - **Project Page:** [DomainShuttle Project Page](https://cn-makers.github.io/DomainShuttle/) | |
| - **Code:** [GitHub Repository](https://github.com/HKUST-C4G/DomainShuttle) | |
| ## Citation | |
| If you find our work useful in your research, please consider citing: | |
| ```bibtex | |
| @article{chen2026domainshuttle, | |
| title={DomainShuttle: Freeform Open Domain Subject-driven Text-to-video Generation}, | |
| 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}, | |
| journal={arXiv preprint arXiv:2606.26058}, | |
| year={2026} | |
| } | |
| ``` |