Instructions to use Overdog/LIFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Overdog/LIFT 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("Overdog/LIFT", 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
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README.md
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## Citation
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```bibtex
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@
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archivePrefix={arXiv},
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primaryClass={cs.CV},
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url={https://arxiv.org/abs/2609.38146},
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}
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```
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## Citation
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```bibtex
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@article{ji2026lift,
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title={LIFT: Layout-In-Future Video Generation under Large Viewpoint Change via On-Policy Self-Distillation},
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author={Ji, Shengxiang and Wang, Boyang and Xu, Haiyang and Li, Bingnan and Mao, Yucheng and Chen, Zeyuan and Shan, Xiaojun and Zhang, Xiang and Hua, Gang and Xie, Jianwen and Cheng, Zezhou and Tu, Zhuowen},
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journal={arXiv preprint arXiv:2609.38146},
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year={2026}
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}
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```
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