MagnifiQ: Patch-aware Text Guided Progressive Upscaling for High-Resolution Image Restoration

2026-08-14

AI Summary

This study addresses the challenges of poor global consistency, repetitive textures, and high computational costs in high-resolution image inpainting by proposing an efficient progressive upsampling framework based on SDXL. The method replaces self-attention layers with linear-complexity convolutions and integrates tiled text semantic guidance with a multi-stage iterative strategy, effectively overcoming computational bottlenecks for 4K restoration. Experimental results demonstrate that the proposed model significantly outperforms existing methods in both perceptual quality and human preference, efficiently generating clear and coherent high-resolution images. Consequently, this work achieves a superior balance between restoration fidelity and computational efficiency, offering a scalable solution for ultra-high-resolution image editing tasks.

Abstract

High-resolution image restoration from degraded inputs is challenging because it must preserve global structural consistency while recovering fine-grained local details, especially at 4K resolution where direct diffusion-based restoration is computationally expensive and prone to repeated or inconsistent textures. In this work, we introduce MagnifiQ, an image restoration framework that progressively upscales and restores images across resolutions, e.g., from 1024x1024 to 4096x4096. Our approach leverages a pre-trained text-to-image diffusion model such as SDXL and adapts it for more scalable high-resolution inference by replacing its original self-attention layers with convolutional operations whose computational cost grows linearly with image resolution. We further propose a progressive upscaling strategy that iteratively restores images over multiple resolution stages, refining each intermediate output rather than directly hallucinating the final 4K image, thereby improving global coherence and reducing high-resolution artifacts. To enhance local details while controlling content drift, MagnifiQ uses patch-specific text prompts that provide spatially localized semantic guidance during restoration. Extensive experiments on synthetic and real-world degraded images show that MagnifiQ outperforms prior diffusion-based restoration methods in perceptual quality and human preference, producing sharper textures and more coherent 4K results while offering practical speed--quality trade-offs through its scalable backbone and progressive design.

Problem

• High-Resolution Image Restoration
• 4K Resolution
• Diffusion Models
• Global Structural Consistency
• Texture Artifacts

Innovation

• Progressive Upscaling
• Linear Attention Replacement
• Patch-aware Text Guidance
• High-Resolution Restoration
• Scalable Diffusion