Native-Resolution Image Synthesis
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.
683fb2786a2b978ca4e62493
Zidong Wang
claimed_verified
2025-06-04T08:56:13.762Z
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/avatars/a7c7e703ccf8824259fc5a8a90a25746.svg
wzd
user
GoodEnough
683fb2786a2b978ca4e62494
Lei Bai
683fb2786a2b978ca4e62495
Xiangyu Yue
683fb2786a2b978ca4e62496
Wanli Ouyang
683fb2786a2b978ca4e62497
Yiyuan Zhang
Native-Resolution Image Synthesis
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.
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.
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