On Fast Sampling of Diffusion Probabilistic Models (2106.00132v2)

Abstract: In this work, we propose FastDPM, a unified framework for fast sampling in diffusion probabilistic models. FastDPM generalizes previous methods and gives rise to new algorithms with improved sample quality. We systematically investigate the fast sampling methods under this framework across different domains, on different datasets, and with different amount of conditional information provided for generation. We find the performance of a particular method depends on data domains (e.g., image or audio), the trade-off between sampling speed and sample quality, and the amount of conditional information. We further provide insights and recipes on the choice of methods for practitioners.

Fast Sampling Framework for Diffusion Probabilistic Models

The paper presents FastDPM, a unified framework aimed at enhancing the sampling efficiency of diffusion probabilistic models (DPMs). The core innovation is offering a faster sampling process without necessitating retraining, thereby improving sample quality across various data domains. Diffusion models are revered for their capability to produce high-quality samples, particularly in image and audio domains. Nonetheless, they are often hampered by slow sampling speeds due to the lengthy Markov chains required in their denoising process.

Methodology and Contributions

FastDPM addresses the speed bottleneck of diffusion models by proposing a method to approximate both the diffusion and reverse processes with significantly fewer steps. It does this by:

1. Introducing Continuous Diffusion Steps: Unlike traditional methods that depend on discrete diffusion steps, FastDPM extends these steps to continuous domains. This crucial extension facilitates the mapping between diffusion steps and noise levels, allowing more granular control over the sampling process.
2. Bijective Mapping: It establishes a bijective function linking noise levels to continuous diffusion steps. This mapping provides the foundation for constructing approximate processes that maintain sample fidelity while reducing the number of steps.
3. New Algorithms: By generalizing existing fast sampling methods like the denoising diffusion implicit models (DDIM) and DiffWave, FastDPM proposes new algorithms that surpass prior techniques in terms of sample quality, especially when the number of steps in the reverse process is minimal.

The experiments conducted demonstrate the robustness of FastDPM across image and audio domains, revealing that the performance of certain reverse processes varies between these domains. Specifically, deterministic reverse processes perform better for images, while stochastic approaches are more suitable for audio.

Strong Numerical Results and Claims

The paper meticulously validates FastDPM's efficacy by comparing its sample quality with original DDPM models and other fast sampling methods. The results indicate that FastDPM achieves nearly equivalent sample quality with dramatically reduced steps (e.g., from 1000 to 50 in image generation tasks). This reduction leads to a notable decrease in sampling time while preserving the integrity of the generated data.

Practical and Theoretical Implications

Practically, FastDPM offers a compelling solution for deploying diffusion models where computational resources or time constraints are critical, such as real-time applications or large-scale data processing. Theoretically, it challenges existing assumptions about the necessity of lengthy sampling procedures in diffusion models and proposes a more flexible alternative. FastDPM's capacity to manage this trade-off inspires further research into optimizing and adapting generative models for varied applications and conditions.

Speculation on Future Developments

As the field progresses, FastDPM could pave the way for even more efficient generative models, potentially incorporating adaptive or dynamic sampling techniques based on real-time feedback or context-specific requirements. Moreover, its ability to generalize across data types suggests applications beyond audio and image, extending to text and multimodal data generation.

In conclusion, FastDPM represents a significant stride in accelerating the sampling process of diffusion probabilistic models, enabling broader use and accessibility without compromising quality. As researchers explore and refine these methodologies, diffusion models may see enhanced applicability and innovation potential across various domains of AI research and deployment.