Accelerating Diffusion Models via Parallel Denoising
Abstract
Recently, diffusion models have demonstrated powerful capabilities in image generation. However, the repetitive and sequential denoising process adds significant time and computational costs, limiting their application. In this paper, we propose a training-free and universal method, Horizontal-Vertical Accelerated Denoising (HVAD). It mainly utilizes the inherent temporal redundancy in the diffusion model to parallelize the time steps of the denoising process horizontally and reduces the computation of individual denoising time steps vertically. It achieves simultaneous inference acceleration from both the horizontal and vertical directions of the denoising process. Thus, significant inference acceleration is achieved without sacrificing generation quality. Experimental results based on the MS-COCO validation set show that the method achieves a practical speedup of 1.97× on Stable Diffusion v1.5, and achieves a theoretical speedup of 2.40× or even higher with guaranteed generation quality. To verify the generality of the method, better results were achieved on other versions of Stable Diffusion, such as Stable Diffusion v2.1 and Stable Diffusion XL.
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