Name: 2x DAT_2_X2
Author: Zheng Chen

[Designing a Practical Degradation Model for Deep Blind Image Super-Resolution (ICCV, 2021)](https://github.com/cszn/BSRGAN)

BSRGANx2

This is a collection of pretrained models for Real-ESRGAN's Compact architecture. There are 1x, 2x, and 4x models, as well as 1x and 2x "UltraCompact" and "SuperUltraCompact" models (think of these as the equivalent to ESRGAN "lite" models). By using these are pretrains for your models, you can ensure that your models are able to be interpolated with other Compact models that were trained from these. These pretrains are compatible with most existing compact models.

Compact Pretrain

DAT_2_X2

2xESRGAN

[Omni Aggregation Networks for Lightweight Image Super-Resolution (OmniSR)](https://github.com/Francis0625/Omni-SR)

While lightweight ViT framework has made tremendous progress in image super-resolution, its uni-dimensional
self-attention modeling, as well as homogeneous aggregation scheme, limit its effective receptive field (ERF) to
include more comprehensive interactions from both spatial and channel dimensions. To tackle these drawbacks,
this work proposes two enhanced components under a new Omni-SR architecture. First, an Omni Self-Attention (OSA) block is proposed based on dense interaction principle, which can simultaneously model pixel-interaction from both spatial and channel dimensions, mining the potential correlations across omni-axis (i.e., spatial and channel). Coupling with mainstream window partitioning strategies, OSA can achieve superior performance with compelling computational budgets. Second, a multi-scale interaction scheme is proposed to mitigate sub-optimal ERF (i.e., premature saturation) in shallow models, which facilitates local propagation and meso-/global-scale interactions, rendering an omni-scale aggregation building block. Extensive experiments demonstrate that Omni-SR achieves recordhigh performance on lightweight super-resolution benchmarks (e.g., 26.95dB@Urban100 ×4 with only 792K parameters). Our code is available at https://github.com/Francis0625/Omni-SR

OmniSR 2x DF2K

OmniSR 2x DIV2K

Pretrained: RRDB_PSNR_x4.pth

The original RRDB_PSNR_x4.pth model converted to 1x, 2x, 8x and 16x scales, intended to be used as pretrained models for new models at those scales. These are compatible with victor's 4xESRGAN.pth conversions

2x PSNR Pretrained Model

Official paper pretrain model

SRFormer: Permuted Self-Attention for Single Image Super-Resolution

Yupeng Zhou 1, Zhen Li 1, Chun-Le Guo 1, Song Bai 2, Ming-Ming Cheng 1, Qibin Hou 1

1TMCC, School of Computer Science, Nankai University

2ByteDance, Singapore

arXiv

The official PyTorch implementation of SRFormer: Permuted Self-Attention for Single Image Super-Resolution (arxiv). SRFormer achieves state-of-the-art performance in

    classical image SR
    lightweight image SR
    real-world image SR

The results can be found here.

    Abstract: In this paper, we introduce SRFormer, a simple yet effective Transformer-based model for single image super-resolution. We rethink the design of the popular shifted window self-attention, expose and analyze several characteristic issues of it, and present permuted self-attention (PSA). PSA strikes an appropriate balance between the channel and spatial information for self-attention, allowing each Transformer block to build pairwise correlations within large windows with even less computational burden. Our permuted self-attention is simple and can be easily applied to existing super-resolution networks based on Transformers. Without any bells and whistles, we show that our SRFormer achieves a 33.86dB PSNR score on the Urban100 dataset, which is 0.46dB higher than that of SwinIR but uses fewer parameters and computations. We hope our simple and effective approach can serve as a useful tool for future research in super-resolution model design. Our code is publicly available at https://github.com/HVision-NKU/SRFormer.

Contents

    Installation & Dataset
    Training
    Testing
    Results
    Pretrain Models
    Citations
    License and Acknowledgement

Installation & Dataset

    python 3.8
    pyTorch >= 1.7.0

cd SRFormer
pip install -r requirements.txt
python setup.py develop

Dataset

We use the same training and testing sets as SwinIR, the following datasets need to be downloaded for training.
Task 	Training Set 	Testing Set
classical image SR 	DIV2K (800 training images) or DIV2K +Flickr2K (2650 images) 	Set5 + Set14 + BSD100 + Urban100 + Manga109
lightweight image SR 	DIV2K (800 training images) 	Set5 + Set14 + BSD100 + Urban100 + Manga109
real-world image SR 	DIV2K (800 training images) +Flickr2K (2650 images) + OST (10324 images for sky,water,grass,mountain,building,plant,animal) 	RealSRSet+5images
Training

    Please download the dataset corresponding to the task and place them in the folder specified by the training option in folder /options/train/SRFormer
    Follow the instructions below to train our SRFormer.

# train SRFormer for classical SR task
./scripts/dist_train.sh 4 options/train/SRFormer/train_SRFormer_SRx2_scratch.yml
./scripts/dist_train.sh 4 options/train/SRFormer/train_SRFormer_SRx3_scratch.yml
./scripts/dist_train.sh 4 options/train/SRFormer/train_SRFormer_SRx4_scratch.yml
# train SRFormer for lightweight SR task
./scripts/dist_train.sh 4 options/train/SRFormer/train_SRFormer_light_SRx2_scratch.yml
./scripts/dist_train.sh 4 options/train/SRFormer/train_SRFormer_light_SRx3_scratch.yml
./scripts/dist_train.sh 4 options/train/SRFormer/train_SRFormer_light_SRx4_scratch.yml

Testing

# test SRFormer for classical SR task
python basicsr/test.py -opt options/test/SRFormer/test_SRFormer_DF2Ksrx2.yml
python basicsr/test.py -opt options/test/SRFormer/test_SRFormer_DF2Ksrx3.yml
python basicsr/test.py -opt options/test/SRFormer/test_SRFormer_DF2Ksrx4.yml
# test SRFormer for lightweight SR task
python basicsr/test.py -opt options/test/SRFormer/test_SRFormer_light_DIV2Ksrx2.yml
python basicsr/test.py -opt options/test/SRFormer/test_SRFormer_light_DIV2Ksrx3.yml
python basicsr/test.py -opt options/test/SRFormer/test_SRFormer_light_DIV2Ksrx4.yml

Results

We provide the results on classical image SR, lightweight image SR, realworld image SR. More results can be found in the paper. The visual results of SRFormer will upload to google drive soon.

Classical image SR

    Results of Table 4 in the paper

    Results of Figure 4 in the paper

Lightweight image SR

    Results of Table 5 in the paper

    Results of Figure 5 in the paper

Model size comparison

    Results of Table 1 and Table 2 in the Supplementary Material

Realworld image SR

    Results of Figure 8 in the paper

Pretrain Models

Pretrain Models can be download from google drive. To reproduce the results in the article, you can download them and put them in the /PretrainModel folder.
Citations

You may want to cite:

@article{zhou2023srformer,
  title={SRFormer: Permuted Self-Attention for Single Image Super-Resolution},
  author={Zhou, Yupeng and Li, Zhen and Guo, Chun-Le and Bai, Song and Cheng, Ming-Ming and Hou, Qibin},
  journal={arXiv preprint arXiv:2303.09735},
  year={2023}
}

License and Acknowledgement

This project is released under the Apache 2.0 license. The codes are based on BasicSR, Swin Transformer, and SwinIR. Please also follow their licenses. Thanks for their awesome works.

SRFormerLight_SRx2_DIV2K

SuperUltraCompact Pretrain

[Swift-SRGAN - Rethinking Super-Resolution for real-time inference](https://github.com/Koushik0901/Swift-SRGAN)

In recent years, there have been several advancements in the task of image super-resolution using the state of the art Deep Learning-based architectures. Many super-resolution-based techniques previously published, require high-end and top-of-the-line Graphics Processing Unit (GPUs) to perform image super-resolution. With the increasing advancements in Deep Learning approaches, neural networks have become more and more compute hungry. We took a step back and, focused on creating a real-time efficient solution. We present an architecture that is faster and smaller in terms of its memory footprint. The proposed architecture uses Depth-wise Separable Convolutions to extract features and, it performs on-par with other super-resolution GANs (Generative Adversarial Networks) while maintaining real-time inference and a low memory footprint. A real-time super-resolution enables streaming high resolution media content even under poor bandwidth conditions. While maintaining an efficient trade-off between the accuracy and latency, we are able to produce a comparable performance model which is one-eighth (1/8) the size of super-resolution GANs and computes 74 times faster than super-resolution GANs.

NOTE: The author used the wrong file extensions for the models *on GitHub*. You will download a `.pth.tar` file. This is not actually a TAR file. Change the file extension to just `.pth` and the model will work.

Swift-SRGAN 2x

UltraCompact Pretrain

[Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN) aims at developing Practical Algorithms for General Image/Video Restoration.

Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration.
We extend the powerful ESRGAN to a practical restoration application (namely, Real-ESRGAN), which is trained with pure synthetic data.

RealESRGAN_x2Plus

This is just a very basic 2x model to start your anime models with on SPAN, nothing special. I tried to keep it as basic as possible

2x-span_anime_pretrain

Official 2x pretrain for [SPAN](https://github.com/hongyuanyu/span).

spanx2_ch48

Purpose: pre-workout to speed up your workouts

While experimenting with color models for cugan, I just ran into problems that the official models are not imported correctly and have additional processing in addition to resizing. As a result, for my project I trained 2 x cugans for neosr; only resizing losses were applied to the dataset `(“lanczos”, “bicubic”, “bilenear”, “HAMMING”, “NEAREST”)`

wtp manga cover pretrained

DAT_2_X3

Official 4x finetune pretrain from [ATD](https://github.com/LabShuHangGU/Adaptive-Token-Dictionary)

# Adaptive Token Dictionary

This repository is an official implementation of the paper "Transcending the Limit of Local Window: Advanced Super-Resolution Transformer with Adaptive Token Dictionary", CVPR, 2024.

[[Arxiv](https://arxiv.org/abs/2401.08209)] [[visual results](https://drive.google.com/drive/folders/1HwEbAGU6WEw9ZGbFdt__BOJo_5DKflEb?usp=sharing)] [[pretrained models](https://drive.google.com/drive/folders/1D3BvTS1xBcaU1mp50k3pBzUWb7qjRvmB?usp=sharing)]

By Leheng Zhang, Yawei Li, Xingyu Zhou, Xiaorui Zhao, and Shuhang Gu.

> **Abstract:** Single Image Super-Resolution is a classic computer vision problem that involves estimating high-resolution (HR) images from low-resolution (LR) ones. Although deep neural networks (DNNs), especially Transformers for super-resolution, have seen significant advancements in recent years, challenges still remain, particularly in limited receptive field caused by window-based self-attention. To address these issues, we introduce a group of auxiliary **A**daptive **T**oken **D**ictionary to SR Transformer and establish an **ATD**-SR method. The introduced token dictionary could learn prior information from training data and adapt the learned prior to specific testing image through an adaptive refinement step. The refinement strategy could not only provide global information to all input tokens but also group image tokens into categories. Based on category partitions, we further propose a category-based self-attention mechanism designed to leverage distant but similar tokens for enhancing input features. The experimental results show that our method achieves the best performance on various single image super-resolution benchmarks.
> 
> <img width="800" src="figures/tdca-acmsa.png"> 
> <img width="800" src="figures/arch.png"> 
> <br/><br/>
> <img width="800" src="figures/viscomp_076.png">

003_ATD_SRx4_finetune

Official DAT2 4x Pretrain of Dual Aggregation Transformer for Image Super-Resolution DAT

Transformer has recently gained considerable popularity in low-level vision tasks, including image super-resolution (SR). These networks utilize self-attention along different dimensions, spatial or channel, and achieve impressive performance. This inspires us to combine the two dimensions in Transformer for a more powerful representation capability. Based on the above idea, we propose a novel Transformer model, Dual Aggregation Transformer (DAT), for image SR. Our DAT aggregates features across spatial and channel dimensions, in the inter-block and intra-block dual manner. Specifically, we alternately apply spatial and channel self-attention in consecutive Transformer blocks. The alternate strategy enables DAT to capture the global context and realize inter-block feature aggregation. Furthermore, we propose the adaptive interaction module (AIM) and the spatial-gate feed-forward network (SGFN) to achieve intra-block feature aggregation. AIM complements two self-attention mechanisms from corresponding dimensions. Meanwhile, SGFN introduces additional non-linear spatial information in the feed-forward network. Extensive experiments show that our DAT surpasses current methods.

DAT_2_x4

Official 4x drct-l Pretrain model, as released on their [DRCT Github Page](https://github.com/ming053l/DRCT?tab=readme-ov-file)

DRCT-L_X4

[ESRGAN](https://github.com/xinntao/ESRGAN)

Pretrained: RRDB_ESRGAN_x4.pth

4xESRGAN

RGT x4

Official 4x rgt-s pretrain from [RGT](https://github.com/zhengchen1999/RGT)

# Recursive Generalization Transformer for Image Super-Resolution

[Zheng Chen](https://zhengchen1999.github.io/), [Yulun Zhang](http://yulunzhang.com/), [Jinjin Gu](https://www.jasongt.com/), [Linghe Kong](https://www.cs.sjtu.edu.cn/~linghe.kong/), and [Xiaokang Yang](https://scholar.google.com/citations?user=yDEavdMAAAAJ), "Recursive Generalization Transformer for Image Super-Resolution", ICLR, 2024

[[paper](https://openreview.net/pdf?id=owziuM1nsR)] [[arXiv](https://arxiv.org/abs/2303.06373)] [[supplementary material](https://openreview.net/attachment?id=owziuM1nsR&name=supplementary_material)] [[visual results](https://drive.google.com/drive/folders/1TWIl66LPtojEbnlUr-s7qkUuTd7RF7Hp?usp=sharing)] [[pretrained models](https://drive.google.com/drive/folders/1UNn5LvnfQAi6eHAHz-mTYWu8vCJs5kwu?usp=sharing)]

---

> **Abstract:** Transformer architectures have exhibited remarkable performance in image superresolution (SR). Since the quadratic computational complexity of the selfattention (SA) in Transformer, existing methods tend to adopt SA in a local region to reduce overheads. However, the local design restricts the global context exploitation, which is crucial for accurate image reconstruction. In this work, we propose the Recursive Generalization Transformer (RGT) for image SR, which can capture global spatial information and is suitable for high-resolution images. Specifically, we propose the recursive-generalization self-attention (RG-SA). It recursively aggregates input features into representative feature maps, and then utilizes cross-attention to extract global information. Meanwhile, the channel dimensions of attention matrices ($query$, $key$, and $value$) are further scaled to mitigate the redundancy in the channel domain. Furthermore, we combine the RG-SA with local self-attention to enhance the exploitation of the global context, and propose the hybrid adaptive integration (HAI) for module integration. The HAI allows the direct and effective fusion between features at different levels (local or global). Extensive experiments demonstrate that our RGT outperforms recent state-of-the-art methods quantitatively and qualitatively.
---

Architecture	DAT
Scale	2x
Size	mediums64\|8x3264nf180dim2.0ef1conv
Color Mode	RGB
License	Apache-2.0 Private use Commercial use Distribution Modifications Credit required State Changes No Liability & Warranty Disclaimer
Date	2023-07-16

DAT_2_X2

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