title: Alleviating Distortion in Image Generation via Multi-Resolution
    Diffusion Models
summary: This paper presents innovative enhancements to diffusion models by
  integrating a novel multi-resolution network and time-dependent layer
  normalization. Diffusion models have gained prominence for their effectiveness
  in high-fidelity image generation. While conventional approaches rely on
  convolutional U-Net architectures, recent Transformer-based designs have
  demonstrated superior performance and scalability. However, Transformer
  architectures, which tokenize input data (via "patchification"), face a
  trade-off between visual fidelity and computational complexity due to the
  quadratic nature of self-attention operations concerning token length. While
  larger patch sizes enable attention computation efficiency, they struggle to
  capture fine-grained visual details, leading to image distortions. To address
  this challenge, we propose augmenting the Diffusion model with the
  Multi-Resolution network (DiMR), a framework that refines features across
  multiple resolutions, progressively enhancing detail from low to high
  resolution. Additionally, we introduce Time-Dependent Layer Normalization
  (TD-LN), a parameter-efficient approach that incorporates time-dependent
  parameters into layer normalization to inject time information and achieve
  superior performance. Our method's efficacy is demonstrated on the
  class-conditional ImageNet generation benchmark, where DiMR-XL variants
  outperform prior diffusion models, setting new state-of-the-art FID scores of
  1.70 on ImageNet 256 x 256 and 2.89 on ImageNet 512 x 512. Project page:
  https://qihao067.github.io/projects/DiMR
paper_authors._id: 666bb80de59c42693adae02a
paper_authors.name: Qihao Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-06-14T07:22:24.541Z
paper_authors._id: 639f1e519f1f2baab2f00d22
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paper_authors.fullname: Qihao Liu
paper_authors.type: user
paper_authors.user: QHL067
paper_authors._id: 666bb80de59c42693adae02b
paper_authors.name: Zhanpeng Zeng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-14T09:51:20.683Z
paper_authors._id: 646e2ddf4ad7f90727a0658b
paper_authors.avatarUrl: /avatars/f62a9f835279b49ca5899c3aa6228dca.svg
paper_authors.fullname: Zhanpeng Zeng
paper_authors.type: user
paper_authors.user: mlpen
paper_authors._id: 666bb80de59c42693adae02c
paper_authors.name: Ju He
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-04T09:36:37.337Z
paper_authors._id: 661c9059bcd78151e5c06ea1
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/661c9059bcd78151e5c06ea1/27bfNo1LZeZQ77vWuAa10.png
paper_authors.fullname: Ju He
paper_authors.type: user
paper_authors.user: turkeyju
paper_authors._id: 666bb80de59c42693adae02d
paper_authors.name: Qihang Yu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-14T09:51:41.891Z
paper_authors._id: 648010b00b9d0f49849adb19
paper_authors.avatarUrl: /avatars/54419bb6e6ee788aba10cd64cd921204.svg
paper_authors.fullname: Qihang Yu
paper_authors.type: user
paper_authors.user: yucornetto
paper_authors._id: 666bb80de59c42693adae02e
paper_authors.name: Xiaohui Shen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-14T09:51:48.062Z
paper_authors._id: 6430aa1b32a732121cd81f98
paper_authors.avatarUrl: /avatars/5419f8d6d4d36fa5ac83e30667b9fd99.svg
paper_authors.fullname: Xiaohui Shen
paper_authors.type: user
paper_authors.user: XiaohuiShen
paper_authors._id: 666bb80de59c42693adae02f
paper_authors.name: Liang-Chieh Chen
paper_title: Alleviating Distortion in Image Generation via Multi-Resolution
    Diffusion Models
paper_summary: This paper presents innovative enhancements to diffusion models by
  integrating a novel multi-resolution network and time-dependent layer
  normalization. Diffusion models have gained prominence for their effectiveness
  in high-fidelity image generation. While conventional approaches rely on
  convolutional U-Net architectures, recent Transformer-based designs have
  demonstrated superior performance and scalability. However, Transformer
  architectures, which tokenize input data (via "patchification"), face a
  trade-off between visual fidelity and computational complexity due to the
  quadratic nature of self-attention operations concerning token length. While
  larger patch sizes enable attention computation efficiency, they struggle to
  capture fine-grained visual details, leading to image distortions. To address
  this challenge, we propose augmenting the Diffusion model with the
  Multi-Resolution network (DiMR), a framework that refines features across
  multiple resolutions, progressively enhancing detail from low to high
  resolution. Additionally, we introduce Time-Dependent Layer Normalization
  (TD-LN), a parameter-efficient approach that incorporates time-dependent
  parameters into layer normalization to inject time information and achieve
  superior performance. Our method's efficacy is demonstrated on the
  class-conditional ImageNet generation benchmark, where DiMR-XL variants
  outperform prior diffusion models, setting new state-of-the-art FID scores of
  1.70 on ImageNet 256 x 256 and 2.89 on ImageNet 512 x 512. Project page:
  https://qihao067.github.io/projects/DiMR
paper_ai_summary: A novel multi-resolution network and time-dependent layer normalization enhance diffusion models, improving image generation fidelity and efficiency.
paper_ai_keywords: diffusion models Multi-Resolution network DiMR time-dependent layer normalization TD-LN Transformer-based designs patchification self-attention visual fidelity high-resolution synthesis class-conditional generation ImageNet FID scores