title: DiCo: Revitalizing ConvNets for Scalable and Efficient Diffusion
    Modeling
summary: Diffusion Transformer (DiT), a promising diffusion model for visual
  generation, demonstrates impressive performance but incurs significant
  computational overhead. Intriguingly, analysis of pre-trained DiT models
  reveals that global self-attention is often redundant, predominantly capturing
  local patterns-highlighting the potential for more efficient alternatives. In
  this paper, we revisit convolution as an alternative building block for
  constructing efficient and expressive diffusion models. However, naively
  replacing self-attention with convolution typically results in degraded
  performance. Our investigations attribute this performance gap to the higher
  channel redundancy in ConvNets compared to Transformers. To resolve this, we
  introduce a compact channel attention mechanism that promotes the activation of
  more diverse channels, thereby enhancing feature diversity. This leads to
  Diffusion ConvNet (DiCo), a family of diffusion models built entirely from
  standard ConvNet modules, offering strong generative performance with
  significant efficiency gains. On class-conditional ImageNet benchmarks, DiCo
  outperforms previous diffusion models in both image quality and generation
  speed. Notably, DiCo-XL achieves an FID of 2.05 at 256x256 resolution and 2.53
  at 512x512, with a 2.7x and 3.1x speedup over DiT-XL/2, respectively.
  Furthermore, our largest model, DiCo-H, scaled to 1B parameters, reaches an FID
  of 1.90 on ImageNet 256x256-without any additional supervision during training.
  Code: https://github.com/shallowdream204/DiCo.
paper_authors._id: 682a82f600248a93f52aa65f
paper_authors.name: Yuang Ai
paper_authors._id: 682a82f600248a93f52aa660
paper_authors.name: Qihang Fan
paper_authors._id: 682a82f600248a93f52aa661
paper_authors.name: Xuefeng Hu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-26T08:23:03.134Z
paper_authors._id: 65cb212725a3b8e44dc0a09a
paper_authors.avatarUrl: /avatars/66c8457a8a8a85e676654479f6860a4b.svg
paper_authors.fullname: Xuefeng Hu
paper_authors.type: user
paper_authors.user: bytehxf
paper_authors._id: 682a82f600248a93f52aa662
paper_authors.name: Zhenheng Yang
paper_authors._id: 682a82f600248a93f52aa663
paper_authors.name: Ran He
paper_authors._id: 682a82f600248a93f52aa664
paper_authors.name: Huaibo Huang
paper_title: DiCo: Revitalizing ConvNets for Scalable and Efficient Diffusion
    Modeling
paper_summary: Diffusion Transformer (DiT), a promising diffusion model for visual
  generation, demonstrates impressive performance but incurs significant
  computational overhead. Intriguingly, analysis of pre-trained DiT models
  reveals that global self-attention is often redundant, predominantly capturing
  local patterns-highlighting the potential for more efficient alternatives. In
  this paper, we revisit convolution as an alternative building block for
  constructing efficient and expressive diffusion models. However, naively
  replacing self-attention with convolution typically results in degraded
  performance. Our investigations attribute this performance gap to the higher
  channel redundancy in ConvNets compared to Transformers. To resolve this, we
  introduce a compact channel attention mechanism that promotes the activation of
  more diverse channels, thereby enhancing feature diversity. This leads to
  Diffusion ConvNet (DiCo), a family of diffusion models built entirely from
  standard ConvNet modules, offering strong generative performance with
  significant efficiency gains. On class-conditional ImageNet benchmarks, DiCo
  outperforms previous diffusion models in both image quality and generation
  speed. Notably, DiCo-XL achieves an FID of 2.05 at 256x256 resolution and 2.53
  at 512x512, with a 2.7x and 3.1x speedup over DiT-XL/2, respectively.
  Furthermore, our largest model, DiCo-H, scaled to 1B parameters, reaches an FID
  of 1.90 on ImageNet 256x256-without any additional supervision during training.
  Code: https://github.com/shallowdream204/DiCo.
paper_ai_summary: Diffusion ConvNet (DiCo), using standard ConvNet modules with a compact channel attention mechanism, achieves high image quality and generation speed in visual generation tasks with efficiency gains compared to Diffusion Transformer (DiT).
paper_ai_keywords: diffusion model visual generation global self-attention convolution performance gap channel redundancy channel attention mechanism Diffusion ConvNet class-conditional ImageNet benchmarks FID speedup