Controlling Poisson Flow Generative Model

Abstract

With the following paper we are planning to present and explore the possibilities of the the newly introduced Poisson Flow Generative Model (PFGM). More specifically, this work aims to introduce the Conditional Poisson Flow Generative Model (CoPFGM), which by extending the existing repository of the PFGM, it will be able to be trained in a way that allows for conditional image sampling. The work aims to provide a more modular solution that can be easily adjusted for multiple data sets, including custom, as well as datasets taken directly from large Python libraries such as PyTorch and TensorFlow. Our proposed CoPFGM consists of two steps: (i) modifying the input of underlying UNet and (ii) modifying the loss function. More specifically, for (i) we have augmented the input channels of every image with one-hot-like class conditional images, and about (ii) we are introducing an updated loss function which incorporates the Cross-Entropy Loss of the generated images during training. The proposed model is tested against 2 datasets, the MNIST, and the Dilbert dataset, the latter, consists of 1100 custom images of the faces of 6 characters taken from the Dilbert Comic-Strip. The proposed model will be tested and presented in the form of an Ablation Study, with which, we show the conditional behavior of the channel augmentation, and the image improvement in terms of class representation with the Cross-Entropy loss.