Oct 31, 2023 · This family of generative models yields record-breaking performance in tasks such as image synthesis, video generation, and molecule design.
Feb 18, 2024 · In this paper, we introduce an approach that leverages continuous dynamical systems to design a novel denoising network for diffusion models ...
Apr 5, 2024 · In this paper, we introduce an approach that leverages continuous dynamical systems to design a novel denoising network for diffusion models that is more ...
Apr 9, 2024 · New paper proposes replacing discrete U-Nets in diffusion models with continuous U-Nets using neural ODEs, enabling up to 80% faster inference.
Apr 9, 2024 · Anew paper proposes replacing the standard discrete U-Net architecture in diffusion models with a continuous U-Net leveraging neural ODEs.
Apr 7, 2024 · The paper presents a promising approach to improving the efficiency and performance of diffusion models, which are a crucial tool in generative ...
Apr 17, 2024 · I'm excited to announce that our paper, "The Missing U for Efficient Diffusion Models," has been accepted for publication in Transactions on Machine Learning ...
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Apr 10, 2024 · In conclusion, “The Missing U for Efficient Diffusion Models” represents a big step forward in making generative AI more practical and ...
Apr 8, 2024 · They claim they can make models up to 80% faster while using 75% fewer parameters and a fraction of the memory.
Faster and More Data-Efficient Training of Diffusion Models
www.semanticscholar.org › paper › Patc...
Patch Diffusion is proposed, a generic patch-wise training framework, to significantly reduce the training time costs while improving data efficiency, ...