Beyond U: Making Diffusion Models Faster & Lighter
Diffusion models are a family of generative models that yield record-breaking
performance in tasks such as image synthesis, video generation, and molecule
design. Despite their capabilities, their efficiency, especially in the reverse
denoising process, remains a challenge due to slow convergence rates and high
computational costs. In this work, we introduce an approach that leverages
continuous dynamical systems to design a novel denoising network for diffusion
models that is more parameter-efficient, exhibits faster convergence, and
demonstrates increased noise robustness. Experimenting with denoising
probabilistic diffusion models, our framework operates with approximately a
quarter of the parameters and 30% of the Floating Point Operations (FLOPs)
compared to standard U-Nets in Denoising Diffusion Probabilistic Models
(DDPMs). Furthermore, our model is up to 70% faster in inference than the
baseline models when measured in equal conditions while converging to better
quality solutions.
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Sergio Calvo-Ordonez
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Jiahao Huang
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2023-11-02T16:16:24.695Z
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Jiahao Huang
user
JiahaoHuang
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Lipei Zhang
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Guang Yang
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Carola-Bibiane Schonlieb
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Angelica I Aviles-Rivero
Beyond U: Making Diffusion Models Faster & Lighter
Diffusion models are a family of generative models that yield record-breaking
performance in tasks such as image synthesis, video generation, and molecule
design. Despite their capabilities, their efficiency, especially in the reverse
denoising process, remains a challenge due to slow convergence rates and high
computational costs. In this work, we introduce an approach that leverages
continuous dynamical systems to design a novel denoising network for diffusion
models that is more parameter-efficient, exhibits faster convergence, and
demonstrates increased noise robustness. Experimenting with denoising
probabilistic diffusion models, our framework operates with approximately a
quarter of the parameters and 30% of the Floating Point Operations (FLOPs)
compared to standard U-Nets in Denoising Diffusion Probabilistic Models
(DDPMs). Furthermore, our model is up to 70% faster in inference than the
baseline models when measured in equal conditions while converging to better
quality solutions.
diffusion models continuous dynamical systems denoising network parameter-efficient convergence noise robustness denoising probabilistic diffusion models U-Nets Denoising Diffusion Probabilistic Models (DDPMs)