Q-DiT: Accurate Post-Training Quantization for Diffusion Transformers

Setup

First, download and set up the repo:

git clone https://github.com/Juanerx/Q-DiT.git
cd Q-DiT

Then create the environment and install required packages:

conda create -n qdit python=3.8
conda activate qdit
pip install -r requirements.txt
pip install .

Usage

If you want to use gptq or static quantization, calibration data should be generated by:

cd scripts
python collect_cali_data.py 

We can quantize the model:

bash quant_main.sh --image-size 256 --num-sampling-steps 50 --cfg-scale 1.5 --use_gptq

BibTeX

@misc{chen2024QDiT,
      title={Q-DiT: Accurate Post-Training Quantization for Diffusion Transformers}, 
      author={Lei Chen and Yuan Meng and Chen Tang and Xinzhu Ma and Jingyan Jiang and Xin Wang and Zhi Wang and Wenwu Zhu},
      year={2024},
      eprint={2406.17343},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
      url={https://arxiv.org/abs/2406.17343}, 
}

Acknowledgments

This codebase borrows from GPTQ, Atom and ADM. Thanks to the authors for releasing their codebases!