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Very excited for this!

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….

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$\"Изображение24.jpg\"$

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very excited to run this model !!!

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Achieve Ultra-Realistic Image Restoration with SUPIR: The Future of Photo-Enhancement

\n\n

Links 🔗:

👉 Subscribe: https://www.youtube.com/@Arxflix
👉 Twitter: https://x.com/arxflix
👉 LMNT (Partner): https://lmnt.com/

By Arxflix
$\"9t4iCUHx_400x400-1.jpg\"$

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Leveraging multi-modal techniques and advanced generative prior,\nSUPIR marks a significant advance in intelligent and realistic image\nrestoration. As a pivotal catalyst within SUPIR, model scaling dramatically\nenhances its capabilities and demonstrates new potential for image restoration.\nWe collect a dataset comprising 20 million high-resolution, high-quality images\nfor model training, each enriched with descriptive text annotations. SUPIR\nprovides the capability to restore images guided by textual prompts, broadening\nits application scope and potential. Moreover, we introduce negative-quality\nprompts to further improve perceptual quality. We also develop a\nrestoration-guided sampling method to suppress the fidelity issue encountered\nin generative-based restoration. 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Papers

arxiv:2401.13627

Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild

Published on Jan 24, 2024

· Submitted by

AK on Jan 25, 2024

#1 Paper of the day

Upvote

Authors:

Jinfan Hu ,

Xintao Wang ,

Jingwen He ,

Abstract

SUPIR, an advanced image restoration method combining generative prior and model scaling, achieves high-quality results and flexible restoration guided by textual prompts.

AI-generated summary

We introduce SUPIR (Scaling-UP Image Restoration), a groundbreaking image restoration method that harnesses generative prior and the power of model scaling up. Leveraging multi-modal techniques and advanced generative prior, SUPIR marks a significant advance in intelligent and realistic image restoration. As a pivotal catalyst within SUPIR, model scaling dramatically enhances its capabilities and demonstrates new potential for image restoration. We collect a dataset comprising 20 million high-resolution, high-quality images for model training, each enriched with descriptive text annotations. SUPIR provides the capability to restore images guided by textual prompts, broadening its application scope and potential. Moreover, we introduce negative-quality prompts to further improve perceptual quality. We also develop a restoration-guided sampling method to suppress the fidelity issue encountered in generative-based restoration. Experiments demonstrate SUPIR's exceptional restoration effects and its novel capacity to manipulate restoration through textual prompts.