Masked Generative Nested Transformers with Decode Time Scaling

TL;DR

This paper introduces a novel nested transformer approach with decode time scaling to improve inference efficiency in visual generation, reducing computational costs while maintaining competitive quality.

Contribution

It proposes a decode time model scaling schedule and computation caching, enabling smaller models to process more tokens, significantly reducing inference costs without increasing model size.

Findings

Achieves nearly 3x less compute than baseline methods.

Maintains competitive image and video generation quality.

Validates effectiveness on ImageNet, UCF101, and Kinetics600 datasets.

Abstract

Recent advances in visual generation have made significant strides in producing content of exceptional quality. However, most methods suffer from a fundamental problem - a bottleneck of inference computational efficiency. Most of these algorithms involve multiple passes over a transformer model to generate tokens or denoise inputs. However, the model size is kept consistent throughout all iterations, which makes it computationally expensive. In this work, we aim to address this issue primarily through two key ideas - (a) not all parts of the generation process need equal compute, and we design a decode time model scaling schedule to utilize compute effectively, and (b) we can cache and reuse some of the computation. Combining these two ideas leads to using smaller models to process more tokens while large models process fewer tokens. These different-sized models do not increase the parameter size, as they share parameters. We rigorously experiment with ImageNet256$\times$256 , UCF101, and Kinetics600 to showcase the efficacy of the proposed method for image/video generation and frame prediction. Our experiments show that with almost $3\times$ less compute than baseline, our model obtains competitive performance.