BinaryDM: Accurate Weight Binarization for Efficient Diffusion Models

This implementation supports the paper "BinaryDM: Accurate Weight Binarization for Efficient Diffusion Models". [PDF]

Requirements

Establish a virtual environment and install dependencies as referred to latent-diffusion.

Usage

• Replace the existing main.py in the LDM with our version of main.py.
• Place openaimodel_ours.py and ours_util.py in the directory ./ldm/modules/diffusionmodules.
• Place ddpm_ours.py in the directory ./ldm/models/diffusion
• run bash train.sh

Main Results

• Results for LDM-4 on LSUN-Bedrooms in unconditional generation by DDIM with 100 steps.

Visualization Results

• Samples generated by the binarized DM baseline and BinaryDM under W1A4 bit-width.

Comments

• Our codebase builds on latent-diffusion and stable-diffusion. Thanks for open-sourcing!

BibTeX

If you find BinaryDM is useful and helpful to your work, please kindly cite this paper:

@misc{zheng2024binarydmaccurateweightbinarization,
      title={BinaryDM: Accurate Weight Binarization for Efficient Diffusion Models}, 
      author={Xingyu Zheng and Xianglong Liu and Haotong Qin and Xudong Ma and Mingyuan Zhang and Haojie Hao and Jiakai Wang and Zixiang Zhao and Jinyang Guo and Michele Magno},
      year={2024},
      eprint={2404.05662},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2404.05662}, 
}