UltraImage: Rethinking Resolution Extrapolation in Image Diffusion Transformers
Recent image diffusion transformers achieve high-fidelity generation, but struggle to generate images beyond these scales, suffering from content repetition and quality degradation. In this work, we present UltraImage, a principled framework that addresses both issues. Through frequency-wise analysis of positional embeddings, we identify that repetition arises from the periodicity of the dominant frequency, whose period aligns with the training resolution. We introduce a recursive dominant frequency correction to constrain it within a single period after extrapolation. Furthermore, we find that quality degradation stems from diluted attention and thus propose entropy-guided adaptive attention concentration, which assigns higher focus factors to sharpen local attention for fine detail and lower ones to global attention patterns to preserve structural consistency. Experiments show that UltraImage consistently outperforms prior methods on Qwen-Image and Flux (around 4K) across three generation scenarios, reducing repetition and improving visual fidelity. Moreover, UltraImage can generate images up to 6K*6K without low-resolution guidance from a training resolution of 1328p, demonstrating its extreme extrapolation capability. Project page is available at https://thu-ml.github.io/ultraimage.github.io/{https://thu-ml.github.io/ultraimage.github.io/}.
693288bf6d1060ca587a27f9
Min Zhao
693288bf6d1060ca587a27fa
Bokai Yan
693288bf6d1060ca587a27fb
Xue Yang
693288bf6d1060ca587a27fc
Hongzhou Zhu
claimed_verified
2025-12-05T08:28:42.854Z
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/avatars/d497a960f8aef6a974907b68ed750c1c.svg
Zhu Hongzhou
zhuhz22
user
zhuhz22
693288bf6d1060ca587a27fd
Jintao Zhang
693288bf6d1060ca587a27fe
Shilong Liu
693288bf6d1060ca587a27ff
Chongxuan Li
693288bf6d1060ca587a2800
Jun Zhu
UltraImage: Rethinking Resolution Extrapolation in Image Diffusion Transformers
Recent image diffusion transformers achieve high-fidelity generation, but struggle to generate images beyond these scales, suffering from content repetition and quality degradation. In this work, we present UltraImage, a principled framework that addresses both issues. Through frequency-wise analysis of positional embeddings, we identify that repetition arises from the periodicity of the dominant frequency, whose period aligns with the training resolution. We introduce a recursive dominant frequency correction to constrain it within a single period after extrapolation. Furthermore, we find that quality degradation stems from diluted attention and thus propose entropy-guided adaptive attention concentration, which assigns higher focus factors to sharpen local attention for fine detail and lower ones to global attention patterns to preserve structural consistency. Experiments show that UltraImage consistently outperforms prior methods on Qwen-Image and Flux (around 4K) across three generation scenarios, reducing repetition and improving visual fidelity. Moreover, UltraImage can generate images up to 6K*6K without low-resolution guidance from a training resolution of 1328p, demonstrating its extreme extrapolation capability. Project page is available at https://thu-ml.github.io/ultraimage.github.io/{https://thu-ml.github.io/ultraimage.github.io/}.
UltraImage, a framework for high-fidelity image generation, addresses content repetition and quality degradation by correcting dominant frequency periodicity and using entropy-guided adaptive attention concentration, enabling high-resolution image generation without low-resolution guidance.
image diffusion transformers frequency-wise analysis positional embeddings dominant frequency correction entropy-guided adaptive attention concentration Qwen-Image Flux generation scenarios visual fidelity high-resolution image generation