ResMaster: Mastering High-Resolution Image Generation via Structural and Fine-Grained Guidance
Shuwei Shi, Wenbo Li, Yuechen Zhang, Jingwen He, Biao Gong, Yinqiang Zheng
Abstract
Diffusion models excel at producing high-quality images; however, scaling to higher resolutions, such as 4K, often results in structural distortions, and repetitive patterns. To this end, we introduce ResMaster, a novel, training-free method that empowers resolution-limited diffusion models to generate high-quality images beyond resolution restrictions. Specifically, ResMaster leverages a low-resolution reference image created by a pre-trained diffusion model to provide structural and fine-grained guidance for crafting high-resolution images on a patch-by-patch basis. To ensure a coherent structure, ResMaster meticulously aligns the low-frequency components of high-resolution patches with the low-resolution reference at each denoising step. For fine-grained guidance, tailored image prompts based on the low-resolution reference and enriched textual prompts produced by a vision-language model are incorporated. This approach could significantly mitigate local pattern distortions and improve detail refinement. Extensive experiments validate that ResMaster sets a new benchmark for high-resolution image generation.
BibTeX
@article{Shi_Li_Zhang_He_Gong_Zheng_2025, title={ResMaster: Mastering High-Resolution Image Generation via Structural and Fine-Grained Guidance}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/32739}, DOI={10.1609/aaai.v39i7.32739}, abstractNote={Diffusion models excel at producing high-quality images; however, scaling to higher resolutions, such as 4K, often results in structural distortions, and repetitive patterns. To this end, we introduce ResMaster, a novel, training-free method that empowers resolution-limited diffusion models to generate high-quality images beyond resolution restrictions. Specifically, ResMaster leverages a low-resolution reference image created by a pre-trained diffusion model to provide structural and fine-grained guidance for crafting high-resolution images on a patch-by-patch basis. To ensure a coherent structure, ResMaster meticulously aligns the low-frequency components of high-resolution patches with the low-resolution reference at each denoising step. For fine-grained guidance, tailored image prompts based on the low-resolution reference and enriched textual prompts produced by a vision-language model are incorporated. This approach could significantly mitigate local pattern distortions and improve detail refinement. Extensive experiments validate that ResMaster sets a new benchmark for high-resolution image generation.}, number={7}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Shi, Shuwei and Li, Wenbo and Zhang, Yuechen and He, Jingwen and Gong, Biao and Zheng, Yinqiang}, year={2025}, month={Apr.}, pages={6887-6895} }