Tackling the Generative Learning Trilemma with Denoising Diffusion GANs (2112.07804v2)

Abstract: A wide variety of deep generative models has been developed in the past decade. Yet, these models often struggle with simultaneously addressing three key requirements including: high sample quality, mode coverage, and fast sampling. We call the challenge imposed by these requirements the generative learning trilemma, as the existing models often trade some of them for others. Particularly, denoising diffusion models have shown impressive sample quality and diversity, but their expensive sampling does not yet allow them to be applied in many real-world applications. In this paper, we argue that slow sampling in these models is fundamentally attributed to the Gaussian assumption in the denoising step which is justified only for small step sizes. To enable denoising with large steps, and hence, to reduce the total number of denoising steps, we propose to model the denoising distribution using a complex multimodal distribution. We introduce denoising diffusion generative adversarial networks (denoising diffusion GANs) that model each denoising step using a multimodal conditional GAN. Through extensive evaluations, we show that denoising diffusion GANs obtain sample quality and diversity competitive with original diffusion models while being 2000$\times$ faster on the CIFAR-10 dataset. Compared to traditional GANs, our model exhibits better mode coverage and sample diversity. To the best of our knowledge, denoising diffusion GAN is the first model that reduces sampling cost in diffusion models to an extent that allows them to be applied to real-world applications inexpensively. Project page and code can be found at https://nvlabs.github.io/denoising-diffusion-gan

• The paper overcomes the generative learning trilemma by introducing denoising diffusion GANs that combine GANs with diffusion processes.
• It reformulates the denoising step using a multimodal distribution, reducing required sampling steps from thousands to as few as two.
• The model achieves a 2000x faster sampling rate on CIFAR-10 while maintaining competitive sample quality and improved mode coverage.

Tackling the Generative Learning Trilemma with Denoising Diffusion GANs

The paper "Tackling the Generative Learning Trilemma with Denoising Diffusion GANs" presents an innovative approach to address the challenges faced by deep generative models in satisfying three critical requirements simultaneously: high sample quality, mode coverage, and rapid sampling. The authors refer to this challenge as the "generative learning trilemma."

Key Contributions

The paper introduces a novel model, "denoising diffusion GANs," which seeks to improve upon traditional denoising diffusion models by addressing the slow sampling rate inherent in such models due to the Gaussian assumption in the denoising step. The authors argue that this assumption is limiting and propose using a multimodal distribution for denoising, realized through conditional GANs.

Methodology

The authors tackle the slow sampling challenge by reformulating the denoising diffusion process. They show that the Gaussian assumption is valid for small denoising steps but becomes inadequate for larger steps, where a more complex multimodal distribution is needed. Denoising diffusion GANs apply a generative adversarial network structure to model these larger steps, effectively reducing the required number of denoising steps from thousands to as few as two, achieving substantial speed improvements.

Results and Evaluation

The extensive evaluation showed that the proposed model maintains competitive sample quality and diversity compared to the original diffusion models while achieving a 2000x faster sampling rate on the CIFAR-10 dataset. This advancement makes it feasible for real-world applications where computational efficiency is crucial. Moreover, the model demonstrated better mode coverage and sample diversity than traditional GANs, thus effectively addressing the trilemma.

Implications

The implications of this research are significant for the field of generative modeling. By resolving the trilemma to a large extent, it opens the door for fast, efficient, and diverse sample generation in applications such as interactive image editing and real-time synthesis tasks. The innovation lies in utilizing the expressive power of GANs for diffusion processes, providing a computationally tractable solution that does not compromise on sample quality or diversity.

Future Directions

This work sets the stage for further exploration of multimodal distributions in diffusion processes and the potential integration of other advanced generative techniques. Future research may explore the scalability of denoising diffusion GANs to higher-dimensional data and more complex real-world applications. Additionally, investigating other forms of expressive denoising distributions could yield further improvements in generative model performance.

In conclusion, this paper contributes a significant advancement in generative modeling, effectively tackling the longstanding generative learning trilemma by marrying the strengths of GANs with diffusion models, thereby enhancing both theoretical understanding and practical applicability.