Diffusion-GAN: Training GANs with Diffusion (2206.02262v4)

Abstract: 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 Gaussian-mixture 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.

• The paper introduces Diffusion-GAN, which incorporates a diffusion-based noise injection to stabilize GAN training.
• It leverages an adaptive forward diffusion chain to generate Gaussian-mixture noise, ensuring continuous gradients for the generator.
• Empirical evaluations on CIFAR-10, LSUN, and FFHQ demonstrate improved image fidelity and diversity, addressing common GAN issues.

An In-Depth Analysis of Diffusion-GAN: Enhancing GAN Training with Diffusion-Based Noise

The prevailing challenge of stabilizing the training of Generative Adversarial Networks (GANs) has led researchers to continually explore various methodologies to enhance their efficacy and robustness. The paper under consideration introduces a novel framework named Diffusion-GAN, which aims to incorporate a diffusion process into the conventional GAN architecture to bolster the generation of realistic images.

Core Methodology and Theoretical Contributions

Diffusion-GAN integrates a forward diffusion chain to generate Gaussian-mixture distributed instance noise, which is then injected into the discriminator inputs. This process is designed to address the instability often observed in GAN training by maintaining a balanced noise-to-data ratio, thereby facilitating the training dynamics between the generator and discriminator. Unlike traditional GANs that directly compare real and generated samples, Diffusion-GAN diffuses both the real and synthetic data, comparing them in their noisy states at different timesteps.

The proposed method rests upon three pivotal components: an adaptive diffusion process, a diffusion timestep-dependent discriminator, and the generator. The adaptive diffusion process helps dynamically adjust the noise levels, while the discriminator, which learns with timestep dependencies, provides consistent guidance for the generator. The generator, in turn, adapts by backpropagating through the forward diffusion chain.

The authors provide a rigorous theoretical framework to substantiate their approach, demonstrating that the adaptive diffusion process engenders a stable and efficient learning environment. The corrupted data distribution allows the generator to receive continuous, non-zero gradients from the discriminator, effectively reducing issues like mode collapse and helping the generator to converge towards the true data distribution.

Empirical Results and Implications

The empirical evaluation of Diffusion-GAN reveals its superiority over established GAN frameworks on benchmarks such as CIFAR-10, LSUN datasets, and FFHQ across various resolutions. Notably, Diffusion-GAN demonstrates improved fidelity and diversity, as measured by Fréchet Inception Distance (FID) and Recall scores, respectively. This indicates that the method not only enhances the quality of the generated images but also preserves their diversity.

Practical Implications

The practical implications of Diffusion-GAN are multifaceted. By reducing GAN training instability, this framework can be employed across a broad spectrum of applications in areas such as image synthesis, data augmentation, and potentially even in non-visual data domains, leveraging its model-agnostic properties.

Theoretical Contributions

The theoretical propositions put forth, particularly regarding the continuity and differentiability of the objective functions with respect to the generator's parameters, present significant advancements in understanding diffusion-based GAN training. The adequate noise injection ensures that the generators receive meaningful gradients, thus facilitating a smoother and more consistent optimization pathway.

Future Directions

The findings from Diffusion-GAN open several avenues for further exploration. One area of interest might involve optimizing the diffusion process parameters, such as the variance schedule or the maximum diffusion steps, tailored to specific data characteristics. Moreover, extending diffusion-based noise injection methods to other generative models or exploring their application in conditional generative settings might yield intriguing results.

Conclusion

Diffusion-GAN presents a substantial enhancement to GAN architectures, embedding a diffusion-based noise framework to mitigate the notorious instability in GAN training. Through methodical theoretical analyses and convincing empirical validations, this paper offers a promising paradigm shift towards more robust and efficient generative modeling. The implications of this research extend beyond conventional image generation, proposing a versatile toolset applicable in varied GAN-driven applications.