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arXiv · 2303.04248

TRACT: Denoising Diffusion Models with Transitive Closure Time-Distillation

David Berthelot·Arnaud Autef·Jierui Lin·Dian Ang Yap·Shuangfei Zhai·Siyuan Hu·Daniel Zheng·Walter Talbott·Eric Gu
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Abstract

Denoising Diffusion models have demonstrated their proficiency for generative sampling. However, generating good samples often requires many iterations. Consequently, techniques such as binary time-distillation (BTD) have been proposed to reduce the number of network calls for a fixed architecture. In this paper, we introduce TRAnsitive Closure Time-distillation (TRACT), a new method that extends BTD. For single step diffusion,TRACT improves FID by up to 2.4x on the same architecture, and achieves new single-step Denoising Diffusion Implicit Models (DDIM) state-of-the-art FID (7.4 for ImageNet64, 3.8 for CIFAR10). Finally we tease apart the method through extended ablations. The PyTorch implementation will be released soon.

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David Berthelot, Arnaud Autef, Jierui Lin, Dian Ang Yap, Shuangfei Zhai, Siyuan Hu, Daniel Zheng, Walter Talbott, Eric Gu. 2023-03-07. TRACT: Denoising Diffusion Models with Transitive Closure Time-Distillation. https://arxiv.org/abs/2303.04248

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