Computer Science > Computer Vision and Pattern Recognition
arXiv:2603.09408 (cs)
[Submitted on 10 Mar 2026]
Title:Reviving ConvNeXt for Efficient Convolutional Diffusion Models
Authors:Taesung Kwon, Lorenzo Bianchi, Lennart Wittke, Felix Watine, Fabio Carrara, Jong Chul Ye, Romann Weber, Vinicius Azevedo
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Abstract:Recent diffusion models increasingly favor Transformer backbones, motivated by the remarkable scalability of fully attentional architectures. Yet the locality bias, parameter efficiency, and hardware friendliness--the attributes that established ConvNets as the efficient vision backbone--have seen limited exploration in modern generative modeling. Here we introduce the fully convolutional diffusion model (FCDM), a model having a backbone similar to ConvNeXt, but designed for conditional diffusion modeling. We find that using only 50% of the FLOPs of DiT-XL/2, FCDM-XL achieves competitive performance with 7$\times$ and 7.5$\times$ fewer training steps at 256$\times$256 and 512$\times$512 resolutions, respectively. Remarkably, FCDM-XL can be trained on a 4-GPU system, highlighting the exceptional training efficiency of our architecture. Our results demonstrate that modern convolutional designs provide a competitive and highly efficient alternative for scaling diffusion models, reviving ConvNeXt as a simple yet powerful building block for efficient generative modeling.
| Comments: | |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2603.09408 [cs.CV] |
| (or arXiv:2603.09408v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2603.09408
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View a PDF of the paper titled Reviving ConvNeXt for Efficient Convolutional Diffusion Models, by Taesung Kwon and 7 other authors
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