ChangyuanWang17 / APQ-DM

This is the official pytorch implementation for the paper: Towards Accurate Post-training Quantization for Diffusion Models.(CVPR24 Poster Highlight)

APQ-DM

​ This is the official pytorch implementation for the paper: Towards Accurate Post-training Quantization for Diffusion Models. (CVPR24 Poster Highlight)

Quick Start

Prerequisites

• python>=3.8
• pytorch>=1.12.1
• torchvision>=0.13.0
• other packages like numpy, tqdm and math

Pretrained Models

You can get full-precision pretrained models from DDIM and DDPM.

Training and Testing

The following experiments were performed in GeForce RTX 3090 with 24GB memory.

Generate CIFAR-10 Images

You can run the following command to generate 50000 CIFAR-10 32*32 images in low bitwidths with differentiable group-wise quantization and active timestep selection.

sh sample_cifar.sh

Calculate FID

After generation, you can run the following command to evaluate IS and FID.

python -m pytorch_fid <dataset path> <image path>

Acknowledgements

We thank the authors of following works for opening source their excellent codes.

• DDIM
• DDPM
• Stable Diffusion
• PTQ4DM
• Q-diffusion
• guided-diffusion