README.md

Public source code for our paper: TV/SNR - Enhancing Diffusion Models Efficiency by Disentangling Total-Variance and Signal-to-Noise Ratio

While the repository is currently being refactored, we will provide the notebook to generate our toy examples under analytic_score_toy_example.ipynb. We added the code for our method to the tv_snr folder. The refactored version will be made public in the coming days.

The computer vision code is based on the original EDM repo and can be used in the same way using generate_tv_snr.py to generate samples.

Example command to generate images using our best method:

python generate_tv_snr.py --outdir=out --snr_schedule sig3 --linear_time --num_steps 64 --tau 1.0 \
    --network=https://nvlabs-fi-cdn.nvidia.com/edm/pretrained/baseline/baseline-cifar10-32x32-uncond-vp.pkl --grid

How to cite

if you use TV/SNR in your research, please cite the corresponding publication:

Kahouli, K., Ripken, W., Gugler, S., Unke, O. T., Müller, K. R., & Nakajima, S. (2025). Enhancing Diffusion Models Efficiency by Disentangling Total-Variance and Signal-to-Noise Ratio. arXiv preprint arXiv:2502.08598.

@article{kahouli2025disentangling,
  title={Disentangling Total-Variance and Signal-to-Noise-Ratio Improves Diffusion Models},
  author={Kahouli, Khaled and Ripken, Winfried and Gugler, Stefan and Unke, Oliver T and M{\"u}ller, Klaus-Robert and Nakajima, Shinichi},
  journal={arXiv preprint arXiv:2502.08598},
  year={2025}
}