Deterministic Diffusion for Sequential Tasks

Meital Bojan, Orr Krupnik, Aviv Tamar

Primary Area: generative models

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Keywords: Diffusion Models, Sequence Prediction, Robotic Manipulation

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TL;DR: A novel approach for accelerating sequence prediction with diffusion models

Abstract: Diffusion models have been used effectively in sequential tasks such as video prediction and robot trajectory generation. Existing approaches typically predict sequence segments autoregressively by denoising Gaussian noise. This iterative denoising process is time-consuming, a problem compounded by the autoregressive nature of sequence prediction. In this paper, we aim to expedite inference by leveraging the properties of the sequence prediction task. Drawing on recent work on deterministic denoising diffusion, we initialize the denoising process with a non-Gaussian source distribution obtained using the context available when predicting sequence elements. Our main insight is that starting from a distribution more closely resembling the target enables inference with fewer iterations, leading to quicker generation. We demonstrate the effectiveness of our method on diffusion for video prediction and in robot control using diffusion policies. Our method attains faster sequence generation with minimal loss of prediction quality, in some cases even improving performance over existing methods.

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Submission Number: 3549