Instructions to use mit-han-lab/svdq-int4-flux.1-schnell with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use mit-han-lab/svdq-int4-flux.1-schnell with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("mit-han-lab/svdq-int4-flux.1-schnell", 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
- Local Apps Settings
- Draw Things
- DiffusionBee
Update README.md
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README.md
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- FLUX.1
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- Diffusion
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- Quantization
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language:
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- en
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base_model:
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If you find this model useful or relevant to your research, please cite
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```bibtex
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@
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li2024svdquant,
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title={SVDQuant: Absorbing Outliers by Low-Rank Components for 4-Bit Diffusion Models},
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author={Li*, Muyang and Lin*, Yujun and Zhang*, Zhekai and Cai, Tianle and Li, Xiuyu and Guo, Junxian and Xie, Enze and Meng, Chenlin and Zhu, Jun-Yan and Han, Song},
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year={
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}
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```
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- FLUX.1
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- Diffusion
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- Quantization
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- ICLR
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language:
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- en
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base_model:
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If you find this model useful or relevant to your research, please cite
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```bibtex
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@inproceedings{
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li2024svdquant,
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title={SVDQuant: Absorbing Outliers by Low-Rank Components for 4-Bit Diffusion Models},
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author={Li*, Muyang and Lin*, Yujun and Zhang*, Zhekai and Cai, Tianle and Li, Xiuyu and Guo, Junxian and Xie, Enze and Meng, Chenlin and Zhu, Jun-Yan and Han, Song},
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booktitle={The Thirteenth International Conference on Learning Representations},
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year={2025}
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}
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```
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