Instructions to use ZhengmingYu/DMAD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ZhengmingYu/DMAD with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("MiniMaxAI/MiniMax-H3", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("ZhengmingYu/DMAD") prompt = "A man with short gray hair plays a red electric guitar." output = pipe(prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Inference
- Notebooks
- Google Colab
- Kaggle
Model card: BibTeX as @article (arXiv preprint 2610.02188)
Browse files
README.md
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## Citation
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```bibtex
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@
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title
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author
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year
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eprint = {2610.02188},
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archivePrefix = {arXiv}
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}
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```
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## Citation
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```bibtex
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@article{yu2026dmad,
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title={DMAD: Distribution Matching as Adversarial Distillation for Fast Visual Generation},
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author={Yu, Zhengming and Yuan, Junkun and Yang, Haotian and Qian, Gordon Guocheng and Wang, Yizhi and Wang, Angtian and Yang, Yiding and Liu, Bo and Li, Xin and Wang, Wenping and Ma, Chongyang},
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journal={arXiv preprint arXiv:2610.02188},
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year={2026}
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}
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```
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