EAM: Enhancing Anything with Diffusion Transformers for Blind Super-Resolution

• URL: http://arxiv.org/abs/2505.05209v3
• Date: Sat, 05 Jul 2025 09:12:24 GMT
• Title: EAM: Enhancing Anything with Diffusion Transformers for Blind Super-Resolution
• Authors: Haizhen Xie, Kunpeng Du, Qiangyu Yan, Sen Lu, Jianhong Han, Hanting Chen, Hailin Hu, Jie Hu,
• Abstract summary: Enhancing Anything Model (EAM) is a novel Blind Super-Resolution method.<n>We introduce a novel block, $Psi$-DiT, which effectively guides the DiT to enhance image restoration.<n>EAM achieves state-of-the-art results across multiple datasets, outperforming existing methods in both quantitative metrics and visual quality.
• Score: 11.331361804059625
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Utilizing pre-trained Text-to-Image (T2I) diffusion models to guide Blind Super-Resolution (BSR) has become a predominant approach in the field. While T2I models have traditionally relied on U-Net architectures, recent advancements have demonstrated that Diffusion Transformers (DiT) achieve significantly higher performance in this domain. In this work, we introduce Enhancing Anything Model (EAM), a novel BSR method that leverages DiT and outperforms previous U-Net-based approaches. We introduce a novel block, $\Psi$-DiT, which effectively guides the DiT to enhance image restoration. This block employs a low-resolution latent as a separable flow injection control, forming a triple-flow architecture that effectively leverages the prior knowledge embedded in the pre-trained DiT. To fully exploit the prior guidance capabilities of T2I models and enhance their generalization in BSR, we introduce a progressive Masked Image Modeling strategy, which also reduces training costs. Additionally, we propose a subject-aware prompt generation strategy that employs a robust multi-modal model in an in-context learning framework. This strategy automatically identifies key image areas, provides detailed descriptions, and optimizes the utilization of T2I diffusion priors. Our experiments demonstrate that EAM achieves state-of-the-art results across multiple datasets, outperforming existing methods in both quantitative metrics and visual quality.

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