Code for Diffusion Models as Plug-and-Play Priors.
The code in the mnist directory is sufficient to reproduce the MNIST experiments of Section 3.1. The mnist/train\_diffusion.ipynb Jupyter notebook trains the MNIST DDPM model. The mnist/inference.ipynb notebook can be used to run our inference algorithm with both hand-crafted and learned constraints.
The code in the ffhq directory reproduces the conditional generation procedure described in Section 3.2. The pre-trained DDPM model is taken from https://github.com/yandex-research/ddpm-segmentation. For the attribute classifier, use any pre-trained face-attribute classification model (such as this). The conditional generation process is presented step-by-step in the ffhq/infer_with_attributes.ipynb notebook.
The code in the tsp directory is sufficient to reproduce the experiments whose results are shown in Table 2. Running tsp/train.py trains a model and tsp/inference.py performs inference with the trained model under the default settings. To run the code, it is necessary to download the datasets from https://github.com/chaitjo/learning-tsp, and place them in the tsp/data folder. A trained model on 64x64 images of 50-node TSP problems is provided at this download link.
In all experiments, the U-Net denoiser network is taken from https://github.com/openai/guided-diffusion.