Smooth Diffusion: Crafting Smooth Latent Spaces in Diffusion Models

Jiayi Guo, Xingqian Xu, Yifan Pu, Zanlin Ni, Chaofei Wang, Manushree Vasu, Shiji Song, Gao Huang, Humphrey Shi; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 7548-7558


Recently diffusion models have made remarkable progress in text-to-image (T2I) generation synthesizing images with high fidelity and diverse contents. Despite this advancement latent space smoothness within diffusion models remains largely unexplored. Smooth latent spaces ensure that a perturbation on an input latent corresponds to a steady change in the output image. This property proves beneficial in downstream tasks including image interpolation inversion and editing. In this work we expose the non-smoothness of diffusion latent spaces by observing noticeable visual fluctuations resulting from minor latent variations. To tackle this issue we propose Smooth Diffusion a new category of diffusion models that can be simultaneously high-performing and smooth. Specifically we introduce Step-wise Variation Regularization to enforce the proportion between the variations of an arbitrary input latent and that of the output image is a constant at any diffusion training step. In addition we devise an interpolation standard deviation (ISTD) metric to effectively assess the latent space smoothness of a diffusion model. Extensive quantitative and qualitative experiments demonstrate that Smooth Diffusion stands out as a more desirable solution not only in T2I generation but also across various downstream tasks. Smooth Diffusion is implemented as a plug-and-play Smooth-LoRA to work with various community models. Code is available at

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@InProceedings{Guo_2024_CVPR, author = {Guo, Jiayi and Xu, Xingqian and Pu, Yifan and Ni, Zanlin and Wang, Chaofei and Vasu, Manushree and Song, Shiji and Huang, Gao and Shi, Humphrey}, title = {Smooth Diffusion: Crafting Smooth Latent Spaces in Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2024}, pages = {7548-7558} }