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The pre-trained text-to-image diffusion models have been increasingly employed to tackle the real-world image super-resolution (Real-ISR) problem due to their powerful generative image priors.
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
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Photo-realistic single image super-resolution using a generative adversarial network
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Decoupled weight decay regularization
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Esrgan: Enhanced super-resolution generative adversarial networks
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The unreasonable effectiveness of deep features as a perceptual metric
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang · 2018
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Jianrui Cai, Hui Zeng, Hongwei Yong, Zisheng Cao, and Lei Zhang · 2019
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Tao Dai, Jianrui Cai, Yongbing Zhang, Shu-Tao Xia, and Lei Zhang · 2019
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Zheng Chen, Yulun Zhang, Jinjin Gu, Linghe Kong, Xiaokang Yang, and Fisher Yu · 2023
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Lsdir: A large scale dataset for image restoration
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Diffbir: Towards blind image restoration with generative diffusion prior
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Lcm-lora: A universal stable-diffusion acceleration module
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Gan prior embedded network for blind face restoration in the wild
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Improving the stability of diffusion models for content consistent super-resolution
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Sinsr: Diffusion-based image super-resolution in a single step
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Desra: Detect and delete the artifacts of gan-based real-world super-resolution models
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Resshift: Efficient diffusion model for image super-resolution by residual shifting
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Adding conditional control to text-to-image diffusion models
Lvmin Zhang, Anyi Rao, and Maneesh Agrawala · 2023
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Swiftbrush v2: Make your one-step diffusion model better than its teacher
Trung Dao, Thuan Hoang Nguyen, Thanh Le, Duc Vu, Khoi Nguyen, Cuong Pham, and Anh Tran · 2024
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Visual instruction tuning
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Exploiting diffusion prior for real-world image super-resolution
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Scaling up to excellence: Practicing model scaling for photo-realistic image restoration in the wild
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