README.md

PTQD

This is the official implementation of PTQD: Accurate Post-Training Quantization for Diffusion Models. If you find any bugs, please do not hesitate to contact me.

Getting Started

Requirements

Establish a virtual environment and install dependencies as referred to LDM.

Usage

Here we detail how to quantize models and collect quantization noise for correction and taking ImageNet as example.

First, downloading the pretrained models provided by LDM:

mkdir -p models/ldm/cin256-v2/
wget -O models/ldm/cin256-v2/model.ckpt https://ommer-lab.com/files/latent-diffusion/nitro/cin/model.ckpt 

Now you should be able to sample images with the following script:

python3 quant_scripts/main_imagenet.py

Then collecting input data of the model, which is required for calibration. You can modify the amount of data by setting different classes and n_samples_per_class in the script.

python3 quant_scripts/collect_diffusion_input_imagenet.py

Then you can quantize and calibrate the model by running:

python3 quant_scripts/quantize_ldm_brecq.py ## for weights
python3 quant_scripts/quantize_ldm_brecqA.py ## for activations

With quantized model and full-precision model, you can collect quantization noise by:

python3 quant_scripts/collect_quant_error_brecq.py

Also, you can modify the amount of data you want to collect.

Then you can analyze the quantization noise to get statistics (correlation, noise mean and variance) for correction:

python3 quant_scripts/analyze_error.py

Now sampling with quantized model!

python3 quant_scripts/main_brecq_imagenet.py

You can set correct=False in DDIMSampler_quantCorrection_imagenet to disable correction.

Acknowledgement

This repository is built upon LDM and BRECQ. We thank the authors for their open-sourced code.