metadata
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
Model Card for nunchaku-sana
This repository contains Nunchaku-quantized versions of SANA-1.6B, designed to generate high-quality images from text prompts. It is optimized for efficient inference while maintaining minimal loss in performance.
Model Details
Model Description
- Developed by: Nunchaku Team
- Model type: text-to-image
- License: NVIDIA License
- Quantized from model: Sana_1600M_1024px
Model Files
svdq-int4_r32-sana1.6b.safetensors: SVDQuant quantized INT4 SANA-1.6B model. For users with non-Blackwell GPUs (pre-50-series).
Model Sources
- Inference Engine: nunchaku
- Quantization Library: deepcompressor
- Paper: SVDQuant: Absorbing Outliers by Low-Rank Components for 4-Bit Diffusion Models
- Demo: svdquant.mit.edu
Usage
See sana1.6b.py.
Performance
Citation
@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}
}