Accelerating Diffusion Transformers with Token-wise Feature Caching

• URL: http://arxiv.org/abs/2410.05317v2
• Date: Mon, 14 Oct 2024 09:35:35 GMT
• Title: Accelerating Diffusion Transformers with Token-wise Feature Caching
• Authors: Chang Zou, Xuyang Liu, Ting Liu, Siteng Huang, Linfeng Zhang,
• Abstract summary: Diffusion transformers have shown significant effectiveness in both image and video synthesis at the expense of huge computation costs. We introduce token-wise feature caching, allowing us to adaptively select the most suitable tokens for caching. Experiments on PixArt-$alpha$, OpenSora, and DiT demonstrate our effectiveness in both image and video generation with no requirements for training.
• Score: 19.140800616594294
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffusion transformers have shown significant effectiveness in both image and video synthesis at the expense of huge computation costs. To address this problem, feature caching methods have been introduced to accelerate diffusion transformers by caching the features in previous timesteps and reusing them in the following timesteps. However, previous caching methods ignore that different tokens exhibit different sensitivities to feature caching, and feature caching on some tokens may lead to 10$\times$ more destruction to the overall generation quality compared with other tokens. In this paper, we introduce token-wise feature caching, allowing us to adaptively select the most suitable tokens for caching, and further enable us to apply different caching ratios to neural layers in different types and depths. Extensive experiments on PixArt-$\alpha$, OpenSora, and DiT demonstrate our effectiveness in both image and video generation with no requirements for training. For instance, 2.36$\times$ and 1.93$\times$ acceleration are achieved on OpenSora and PixArt-$\alpha$ with almost no drop in generation quality.

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