Ultra High-Resolution Image Inpainting with Patch-Based Content Consistency Adapter
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866918161007247360 |
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| author | Zhang, Jianhui Cheng, Sheng Sun, Qirui Liu, Jia Luyang, Wang Feng, Chaoyu Fang, Chen Lei, Lei Wang, Jue Liu, Shuaicheng |
| author_facet | Zhang, Jianhui Cheng, Sheng Sun, Qirui Liu, Jia Luyang, Wang Feng, Chaoyu Fang, Chen Lei, Lei Wang, Jue Liu, Shuaicheng |
| contents | In this work, we present Patch-Adapter, an effective framework for high-resolution text-guided image inpainting. Unlike existing methods limited to lower resolutions, our approach achieves 4K+ resolution while maintaining precise content consistency and prompt alignment, two critical challenges in image inpainting that intensify with increasing resolution and texture complexity. Patch-Adapter leverages a two-stage adapter architecture to scale the diffusion model's resolution from 1K to 4K+ without requiring structural overhauls: (1) Dual Context Adapter learns coherence between masked and unmasked regions at reduced resolutions to establish global structural consistency; and (2) Reference Patch Adapter implements a patch-level attention mechanism for full-resolution inpainting, preserving local detail fidelity through adaptive feature fusion. This dual-stage architecture uniquely addresses the scalability gap in high-resolution inpainting by decoupling global semantics from localized refinement. Experiments demonstrate that Patch-Adapter not only resolves artifacts common in large-scale inpainting but also achieves state-of-the-art performance on the OpenImages and Photo-Concept-Bucket datasets, outperforming existing methods in both perceptual quality and text-prompt adherence. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_13419 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Ultra High-Resolution Image Inpainting with Patch-Based Content Consistency Adapter Zhang, Jianhui Cheng, Sheng Sun, Qirui Liu, Jia Luyang, Wang Feng, Chaoyu Fang, Chen Lei, Lei Wang, Jue Liu, Shuaicheng Computer Vision and Pattern Recognition In this work, we present Patch-Adapter, an effective framework for high-resolution text-guided image inpainting. Unlike existing methods limited to lower resolutions, our approach achieves 4K+ resolution while maintaining precise content consistency and prompt alignment, two critical challenges in image inpainting that intensify with increasing resolution and texture complexity. Patch-Adapter leverages a two-stage adapter architecture to scale the diffusion model's resolution from 1K to 4K+ without requiring structural overhauls: (1) Dual Context Adapter learns coherence between masked and unmasked regions at reduced resolutions to establish global structural consistency; and (2) Reference Patch Adapter implements a patch-level attention mechanism for full-resolution inpainting, preserving local detail fidelity through adaptive feature fusion. This dual-stage architecture uniquely addresses the scalability gap in high-resolution inpainting by decoupling global semantics from localized refinement. Experiments demonstrate that Patch-Adapter not only resolves artifacts common in large-scale inpainting but also achieves state-of-the-art performance on the OpenImages and Photo-Concept-Bucket datasets, outperforming existing methods in both perceptual quality and text-prompt adherence. |
| title | Ultra High-Resolution Image Inpainting with Patch-Based Content Consistency Adapter |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.13419 |