Create README.md

README.md

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+---
+base_model: Efficient-Large-Model/Sana_1600M_1024px
+base_model_relation: quantized
+datasets:
+- mit-han-lab/svdquant-datasets
+language:
+- en
+library_name: diffusers
+license: other
+license_link: https://huggingface.co/Efficient-Large-Model/Sana_1600M_1024px/blob/main/LICENSE.txt
+license_name: nvidia-license
+pipeline_tag: text-to-image
+tags:
+- text-to-image
+- SVDQuant
+- SANA
+- Diffusion
+- Quantization
+- ICLR2025
+
+---
+**This repository has been deprecated and will be hidden in December 2025. Please use https://huggingface.co/nunchaku-tech/nunchaku-sana.**
+
+<p align="center" style="border-radius: 10px">
+  <img src="https://huggingface.co/datasets/nunchaku-tech/cdn/resolve/main/nunchaku/assets/nunchaku.svg" width="30%" alt="Nunchaku Logo"/>
+</p>
+
+# Model Card for svdq-int4-sana-1600m
+
+## Model Details
+
+### Model Description
+
+- **Developed by:** Nunchaku Team
+- **Model type:** text-to-image
+- **License:** [NVIDIA License](https://huggingface.co/Efficient-Large-Model/Sana_1600M_1024px/blob/main/LICENSE.txt)
+- **Quantized from model:** [Sana_1600M_1024px](https://huggingface.co/Efficient-Large-Model/Sana_1600M_1024px)
+
+### Model Sources
+
+- **Inference Engine:** [nunchaku](https://github.com/nunchaku-tech/nunchaku)
+- **Quantization Library:** [deepcompressor](https://github.com/nunchaku-tech/deepcompressor)
+- **Paper:** [SVDQuant: Absorbing Outliers by Low-Rank Components for 4-Bit Diffusion Models](http://arxiv.org/abs/2411.05007)
+- **Demo:** [svdquant.mit.edu](https://svdquant.mit.edu)
+
+## Usage
+
+See [sana1.6b.py](https://github.com/nunchaku-tech/nunchaku/blob/main/examples/sana1.6b.py).
+
+## Performance
+
+![performance](https://huggingface.co/datasets/nunchaku-tech/cdn/resolve/main/nunchaku/assets/efficiency.jpg)
+
+## Citation
+
+```bibtex
+@inproceedings{
+  li2024svdquant,
+  title={SVDQuant: Absorbing Outliers by Low-Rank Components for 4-Bit Diffusion Models},
+  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},
+  booktitle={The Thirteenth International Conference on Learning Representations},
+  year={2025}
+}
+@article{
+  xie2024sana,
+  title={Sana: Efficient high-resolution image synthesis with linear diffusion transformers},
+  author={Xie, Enze and Chen, Junsong and Chen, Junyu and Cai, Han and Tang, Haotian and Lin, Yujun and Zhang, Zhekai and Li, Muyang and Zhu, Ligeng and Lu, Yao and others},
+  journal={arXiv preprint arXiv:2410.10629},
+  year={2024}
+}
+```