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<p>The potential for higher-resolution image generation using pretrained diffusion models is immense. However, these models often struggle with object repetition and structural artifacts especially when scaling to 4K resolution and beyond. Our analysis reveals that causes the problem, a single prompt for the generation of multiple scales provides insufficient efficacy. To address this, we propose HiPrompt, a new tuning-free solution that tackles the above problems by introducing hierarchical prompts. The hierarchical prompts provide both global and local semantic guidance. Specifically, the global prompt captures overall scene semantics from user input, while local guidance comes from patch-wise descriptions generated by MLLMs to refine regional structures and textures. Furthermore, during inverse denoising, noise is decomposed into low- and high-frequency components, each conditioned on different prompt levels, facilitating prompt-guided denoising under hierarchical semantic guidance. It further allows the generation to focus more on local spatial regions and ensures the generated images maintain coherent local and global semantics, structures, and textures with high definition. Extensive experiments demonstrate that HiPrompt outperforms state-of-the-art works in higher-resolution image generation, significantly reducing object repetition and enhancing structural quality. The demo and code can be found on the project website: <a href="https://liuxinyv.github.io/HiPrompt/">https://liuxinyv.github.io/HiPrompt/</a>.</p>

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HiPrompt: Tuning-free Higher-Resolution Generation with Hierarchical MLLM Prompts

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月6日

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  • Xinyu Liu,
  • Yingqing He,
  • Lanqing Guo,
  • Xiang Li,
  • Bu Jin,
  • Yan Li,
  • Chi-Min Chan,
  • Wei Xue,
  • Wenhan Luo,
  • Qifeng Liu,
  • Yike Guo

摘要

The potential for higher-resolution image generation using pretrained diffusion models is immense. However, these models often struggle with object repetition and structural artifacts especially when scaling to 4K resolution and beyond. Our analysis reveals that causes the problem, a single prompt for the generation of multiple scales provides insufficient efficacy. To address this, we propose HiPrompt, a new tuning-free solution that tackles the above problems by introducing hierarchical prompts. The hierarchical prompts provide both global and local semantic guidance. Specifically, the global prompt captures overall scene semantics from user input, while local guidance comes from patch-wise descriptions generated by MLLMs to refine regional structures and textures. Furthermore, during inverse denoising, noise is decomposed into low- and high-frequency components, each conditioned on different prompt levels, facilitating prompt-guided denoising under hierarchical semantic guidance. It further allows the generation to focus more on local spatial regions and ensures the generated images maintain coherent local and global semantics, structures, and textures with high definition. Extensive experiments demonstrate that HiPrompt outperforms state-of-the-art works in higher-resolution image generation, significantly reducing object repetition and enhancing structural quality. The demo and code can be found on the project website: https://liuxinyv.github.io/HiPrompt/.