UniPC: A Unified Predictor-Corrector Framework for Fast Sampling of Diffusion Models (2302.04867v4)

Abstract: Diffusion probabilistic models (DPMs) have demonstrated a very promising ability in high-resolution image synthesis. However, sampling from a pre-trained DPM is time-consuming due to the multiple evaluations of the denoising network, making it more and more important to accelerate the sampling of DPMs. Despite recent progress in designing fast samplers, existing methods still cannot generate satisfying images in many applications where fewer steps (e.g., $<$10) are favored. In this paper, we develop a unified corrector (UniC) that can be applied after any existing DPM sampler to increase the order of accuracy without extra model evaluations, and derive a unified predictor (UniP) that supports arbitrary order as a byproduct. Combining UniP and UniC, we propose a unified predictor-corrector framework called UniPC for the fast sampling of DPMs, which has a unified analytical form for any order and can significantly improve the sampling quality over previous methods, especially in extremely few steps. We evaluate our methods through extensive experiments including both unconditional and conditional sampling using pixel-space and latent-space DPMs. Our UniPC can achieve 3.87 FID on CIFAR10 (unconditional) and 7.51 FID on ImageNet 256$\times$256 (conditional) with only 10 function evaluations. Code is available at https://github.com/wl-zhao/UniPC.

• The paper presents UniPC, a unified predictor-corrector framework that enhances sampling efficiency in diffusion models using high-order accuracy with minimal evaluations.
• Key methodology features a unified formulation for both predictor and corrector components that supports arbitrary orders without extra computational cost.
• Experimental results demonstrate improved FID scores (e.g., 3.87 on CIFAR10) and superior sample quality with only 10 function evaluations.

A Study on the UniPC Framework for Efficient Sampling in Diffusion Models

The paper "UniPC: A Unified Predictor-Corrector Framework for Fast Sampling of Diffusion Models" introduces an innovative framework, termed UniPC, aimed at efficient sampling in diffusion probabilistic models (DPMs). DPMs have become prominent in generative modeling tasks, outperforming traditional GANs and VAEs in stability and hyperparameter sensitivity. Despite their robust generative capabilities, the sampling efficiency of DPMs remains a challenging aspect due to the need for iterative denoising, thereby necessitating approaches that can perform fast sampling without compromising output quality.

The novel contribution of this paper is the development of a unified predictor (UniP) and a corrector (UniC) that together form the UniPC framework. This framework is distinguished by its ability to increase the order of accuracy of existing samplers without additional computational cost, making it particularly effective in scenarios where fewer evaluation steps (i.e., extremely few steps like less than 10) are desired.

Methodology Overview

1. Unified Predictor (UniP): The UniP is crafted to support arbitrary orders, which provides flexibility and adaptability across different sampling procedures. Its design facilitates achieving higher-order accuracy through a unified analytical formulation.
2. Unified Corrector (UniC): The UniC can be appended after existing samplers to enhance accuracy order. It operates by utilizing the current timestep's outputs, effectively circumventing the need for additional evaluations while preserving speed efficiency.
3. Unified Analytical Form: Both UniP and UniC share a unified analytical form that simplifies implementation for various orders. This universal approach allows seamless integration across different DPM frameworks and enhances the adaptability of the UniPC framework for diverse applications.

Experimental Findings

The researchers conducted extensive experiments to validate the efficacy of UniPC, encompassing both unconditional and conditional sampling scenarios, employing pixel-space and latent-space DPMs. The paper reports that UniPC consistently surpassed previous state-of-the-art methods such as DPM-Solver++ in generating high-quality samples with remarkably few function evaluations.

• FID Scores: The framework achieved an FID of 3.87 on CIFAR10 and 7.51 on ImageNet 256x256 with only 10 evaluations, indicative of enhanced performance over traditional fast samplers.
• Qualitative Improvements: Visual assessments demonstrated that images sampled using UniPC displayed superior details and plausibility, especially noteworthy when only a limited number of steps was permissible.

Implications and Future Directions

The UniPC framework addresses a significant bottleneck in the utilization of DPMs by drastically reducing sample generation time without sacrificing detail and accuracy. This advancement not only enhances the feasibility of diffusion models in real-time applications but also their deployment in constrained computational environments. Furthermore, the versatile design of UniPC suggests potential adaptability across various architectures and scaling challenges in generative models.

Looking forward, future developments may explore further optimization of the UniPC coefficients or the exploration of alternative manifold learning techniques with DPMs to enhance convergence characteristics and sampling diversity. The potential integration of UniPC with training-dependent methods offers another intriguing avenue, potentially bridging the efficiency gap while leveraging the benefits of pre-trained networks.

In conclusion, the paper successfully delivers an impactful enhancement to the fast sampling capability of DPMs, demonstrating the practical and theoretical merits of a predictor-corrector framework in the generative modeling landscape.