Progressive Distillation for Fast Sampling of Diffusion Models (2202.00512v2)

Abstract: Diffusion models have recently shown great promise for generative modeling, outperforming GANs on perceptual quality and autoregressive models at density estimation. A remaining downside is their slow sampling time: generating high quality samples takes many hundreds or thousands of model evaluations. Here we make two contributions to help eliminate this downside: First, we present new parameterizations of diffusion models that provide increased stability when using few sampling steps. Second, we present a method to distill a trained deterministic diffusion sampler, using many steps, into a new diffusion model that takes half as many sampling steps. We then keep progressively applying this distillation procedure to our model, halving the number of required sampling steps each time. On standard image generation benchmarks like CIFAR-10, ImageNet, and LSUN, we start out with state-of-the-art samplers taking as many as 8192 steps, and are able to distill down to models taking as few as 4 steps without losing much perceptual quality; achieving, for example, a FID of 3.0 on CIFAR-10 in 4 steps. Finally, we show that the full progressive distillation procedure does not take more time than it takes to train the original model, thus representing an efficient solution for generative modeling using diffusion at both train and test time.

• The paper introduces a progressive distillation method that iteratively halves diffusion model sampling steps, achieving a FID of 3.0 on CIFAR-10 with only 4 steps.
• It employs novel parameterizations and deterministic samplers like DDIM to ensure model stability even with drastically fewer iterations.
• The approach cuts computational cost by matching the original training time while significantly speeding up sample generation for practical applications.

An Expert Analysis of "Progressive Distillation for Fast Sampling of Diffusion Models"

The paper "Progressive Distillation for Fast Sampling of Diffusion Models" by Tim Salimans and Jonathan Ho addresses one of the significant limitations associated with the utilitarian deployment of diffusion models—namely, their slow sampling speed when generating high-quality samples. The authors contribute an innovative method termed "progressive distillation" which seeks to ameliorate the high computational costs typically associated with these generative models by efficiently reducing the number of sampling steps required.

Diffusion models have cemented their role as a formidable class of generative models, demonstrating prowess in tasks such as image generation, super-resolution, and inpainting. Despite their superior performance metrics, such as better Fréchet Inception Distance (FID) in comparison to GANs and autoregressive models, their adoption in practical applications is constrained by the computational overhead during the sampling phase where numerous model evaluations are traditionally required to produce high-fidelity outputs.

In response to this bottleneck, this paper introduces two major improvements: new parameterizations of diffusion models that enhance stability when utilizing fewer sampling iterations, and a pioneering distillation method. This approach facilitates the transference of a trained diffusion model that typically uses many sampling steps into a more efficient model that utilizes progressively fewer steps while maintaining high-quality outputs.

The crux of the proposed methodology lies in the iterative halving of the number of sampling steps. Starting with a teacher sampler configured with a high number of steps, progressive distillation trains a student model that operates with half the number of steps of its teacher sampler. This iteration is repeated, progressively reducing the sampling steps by half until very few steps are needed. For instance, experiments on standard benchmarks like CIFAR-10 indicate that a model starting with 8192 steps can be distilled to as few as 4 steps, resulting in a FID of 3.0 with merely 4 steps—an outcome reflecting minimal permeability in quality despite the drastic reduction in computational steps.

Several notable results from the experimental evaluations underscore the effectiveness of the proposed distillation technique. On CIFAR-10, distillation enables a remarkable reduction from thousands of steps to merely 4, achieving near-optimal results without substantial degradation in sample quality, defining a new computational efficiency standard for generative diffusion models. The implications are particularly significant when considering hardware constraints or applications requiring rapid generation times.

The progressive distillation approach is designed to require no more time than training the original model, emphasizing its practicality. This method harnesses deterministic samplers like DDIM, enabling a streamlined computational path by avoiding stochastic iterations traditionally required in reverse processes.

This work invites future inquiries into the scalability of diffusion models and their capacity to generalize over different data modalities fast, such as audio and video sequences. Moreover, optimizing and exploring novel architectures for the student model in distillation could further enhance efficiency.

In conclusion, this paper presents a methodologically sound and practically beneficial advancement in the field of generative modeling using diffusion processes. By leveraging progressive distillation, the authors provide a viable solution to one of the most prominent challenges faced by users of diffusion models, setting a trajectory for future research aiming to refine and extend this work's applicability across diverse domains.