Diffusion-GAN: Training GANs with Diffusion

Generative adversarial networks (GANs) are challenging to train stably, and a promising remedy of injecting instance noise into the discriminator input has not been very effective in practice. In this paper, we propose Diffusion-GAN, a novel GAN framework that leverages a forward diffusion chain to generate Gaussianmixture distributed instance noise. Diffusion-GAN consists of three components, including an adaptive diffusion process, a diffusion timestep-dependent discriminator, and a generator. Both the observed and generated data are diffused by the same adaptive diffusion process. At each diffusion timestep, there is a different noise-to-data ratio and the timestep-dependent discriminator learns to distinguish the diffused real data from the diffused generated data. The generator learns from the discriminator's feedback by backpropagating through the forward diffusion chain, whose length is adaptively adjusted to balance the noise and data levels. We theoretically show that the discriminator's timestep-dependent strategy gives consistent and helpful guidance to the generator, enabling it to match the true data distribution. We demonstrate the advantages of Diffusion-GAN over strong GAN baselines on various datasets, showing that it can produce more realistic images with higher stability and data efficiency than state-of-the-art GANs. Generative adversarial networks (GANs) (Goodfellow et al., 2014) and their variants (Brock et al., 2018; Karras et al., 2019; 2020a; Zhao et al., 2020) have achieved great success in synthesizing photo-realistic high-resolution images. GANs in practice, however, are known to suffer from a variety of issues ranging from non-convergence and training instability to mode collapse (Arjovsky and Bottou, 2017; Mescheder et al., 2018).