Reviving ConvNeXt for Efficient Convolutional Diffusion Models
📌 核心贡献
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.
📖 摘要
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 and 7.5 fewer training steps at 256256 and 512512 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.
🔍 关键信息
| 字段 | 内容 |
|---|---|
| 发表 | 2026-03-10 |
| 机构 | |
| 分类 | cs.CV, cs.AI, cs.LG |
| 链接 | arXiv |