title: Native-Resolution Image Synthesis
summary: We introduce native-resolution image synthesis, a novel generative modeling
  paradigm that enables the synthesis of images at arbitrary resolutions and
  aspect ratios. This approach overcomes the limitations of conventional
  fixed-resolution, square-image methods by natively handling variable-length
  visual tokens, a core challenge for traditional techniques. To this end, we
  introduce the Native-resolution diffusion Transformer (NiT), an architecture
  designed to explicitly model varying resolutions and aspect ratios within its
  denoising process. Free from the constraints of fixed formats, NiT learns
  intrinsic visual distributions from images spanning a broad range of
  resolutions and aspect ratios. Notably, a single NiT model simultaneously
  achieves the state-of-the-art performance on both ImageNet-256x256 and 512x512
  benchmarks. Surprisingly, akin to the robust zero-shot capabilities seen in
  advanced large language models, NiT, trained solely on ImageNet, demonstrates
  excellent zero-shot generalization performance. It successfully generates
  high-fidelity images at previously unseen high resolutions (e.g., 1536 x 1536)
  and diverse aspect ratios (e.g., 16:9, 3:1, 4:3), as shown in Figure 1. These
  findings indicate the significant potential of native-resolution modeling as a
  bridge between visual generative modeling and advanced LLM methodologies.
paper_authors._id: 683fb2786a2b978ca4e62493
paper_authors.name: Zidong Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-04T08:56:13.762Z
paper_authors._id: 64b7aa374df206a3ed1947d2
paper_authors.avatarUrl: /avatars/a7c7e703ccf8824259fc5a8a90a25746.svg
paper_authors.fullname: wzd
paper_authors.type: user
paper_authors.user: GoodEnough
paper_authors._id: 683fb2786a2b978ca4e62494
paper_authors.name: Lei Bai
paper_authors._id: 683fb2786a2b978ca4e62495
paper_authors.name: Xiangyu Yue
paper_authors._id: 683fb2786a2b978ca4e62496
paper_authors.name: Wanli Ouyang
paper_authors._id: 683fb2786a2b978ca4e62497
paper_authors.name: Yiyuan Zhang
paper_title: Native-Resolution Image Synthesis
paper_summary: We introduce native-resolution image synthesis, a novel generative modeling
  paradigm that enables the synthesis of images at arbitrary resolutions and
  aspect ratios. This approach overcomes the limitations of conventional
  fixed-resolution, square-image methods by natively handling variable-length
  visual tokens, a core challenge for traditional techniques. To this end, we
  introduce the Native-resolution diffusion Transformer (NiT), an architecture
  designed to explicitly model varying resolutions and aspect ratios within its
  denoising process. Free from the constraints of fixed formats, NiT learns
  intrinsic visual distributions from images spanning a broad range of
  resolutions and aspect ratios. Notably, a single NiT model simultaneously
  achieves the state-of-the-art performance on both ImageNet-256x256 and 512x512
  benchmarks. Surprisingly, akin to the robust zero-shot capabilities seen in
  advanced large language models, NiT, trained solely on ImageNet, demonstrates
  excellent zero-shot generalization performance. It successfully generates
  high-fidelity images at previously unseen high resolutions (e.g., 1536 x 1536)
  and diverse aspect ratios (e.g., 16:9, 3:1, 4:3), as shown in Figure 1. These
  findings indicate the significant potential of native-resolution modeling as a
  bridge between visual generative modeling and advanced LLM methodologies.
paper_ai_summary: A novel generative model, Native-resolution diffusion Transformer (NiT), synthesizes high-resolution and varied aspect ratio images with state-of-the-art performance and zero-shot generalization capabilities.
paper_ai_keywords: native-resolution image synthesis generative modeling paradigm variable-length visual tokens diffusion Transformer denoising process ImageNet-256x256 ImageNet-512x512 high-fidelity images aspect ratios zero-shot generalization