ResMaster: Mastering High-Resolution Image Generation via Structural and Fine-Grained Guidance

• URL: http://arxiv.org/abs/2406.16476v1
• Date: Mon, 24 Jun 2024 09:28:21 GMT
• Title: ResMaster: Mastering High-Resolution Image Generation via Structural and Fine-Grained Guidance
• Authors: Shuwei Shi, Wenbo Li, Yuechen Zhang, Jingwen He, Biao Gong, Yinqiang Zheng,
• Abstract summary: ResMaster is a training-free method that empowers resolution-limited diffusion models to generate high-quality images beyond resolution restrictions. It provides structural and fine-grained guidance for crafting high-resolution images on a patch-by-patch basis. Experiments validate that ResMaster sets a new benchmark for high-resolution image generation and demonstrates promising efficiency.
• Score: 46.64836025290448
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffusion models excel at producing high-quality images; however, scaling to higher resolutions, such as 4K, often results in over-smoothed content, 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 global 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 and demonstrates promising efficiency. The project page is https://shuweis.github.io/ResMaster .

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