fix pretrained model link

Author: JingyunLiang

README.md

@@ -9,16 +9,16 @@
 
 
 This repository is the official PyTorch implementation of Mutual Affine Network for ***Spatially Variant*** Kernel Estimation in Blind Image Super-Resolution
-([arxiv](https://arxiv.org/pdf/2108.05302.pdf), [supplementary](https://github.com/JingyunLiang/MANet/releases/tag/v0.0)).
+([arxiv](https://arxiv.org/pdf/2108.05302.pdf), [supplementary](https://github.com/JingyunLiang/MANet/releases)).
 
  :rocket:  :rocket:  :rocket: **News**:
   - Aug. 7, 2021: We add an [online Colab demo for MANet kernel estimation <a href="https://colab.research.google.com/gist/JingyunLiang/4ed2524d6e08343710ee408a4d997e1c/manet-demo-on-spatially-variant-kernel-estimation.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a>](https://colab.research.google.com/gist/JingyunLiang/4ed2524d6e08343710ee408a4d997e1c/manet-demo-on-spatially-variant-kernel-estimation.ipynb)
   - Sep.06, 2021: See our recent work [SwinIR: Transformer-based image restoration](https://github.com/JingyunLiang/SwinIR).[![arXiv](https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg)](https://arxiv.org/abs/2108.10257)[![GitHub Stars](https://img.shields.io/github/stars/JingyunLiang/SwinIR?style=social)](https://github.com/JingyunLiang/SwinIR)[![download](https://img.shields.io/github/downloads/JingyunLiang/SwinIR/total.svg)](https://github.com/JingyunLiang/SwinIR/releases)[ <a href="https://colab.research.google.com/gist/JingyunLiang/a5e3e54bc9ef8d7bf594f6fee8208533/swinir-demo-on-real-world-image-sr.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a>](https://colab.research.google.com/gist/JingyunLiang/a5e3e54bc9ef8d7bf594f6fee8208533/swinir-demo-on-real-world-image-sr.ipynb)
-  - Aug. 17, 2021: See our previous spatially invariant kernel estimation work: *[Flow-based Kernel Prior with Application to Blind Super-Resolution (FKP), CVPR2021](https://github.com/JingyunLiang/FKP)  [![arXiv](https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg)](https://arxiv.org/abs/2103.15977)
+  - Aug. 17, 2021: See our previous work on [blind SR: Flow-based Kernel Prior with Application to Blind Super-Resolution (FKP), CVPR2021](https://github.com/JingyunLiang/FKP)  [![arXiv](https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg)](https://arxiv.org/abs/2103.15977)
 [![GitHub Stars](https://img.shields.io/github/stars/JingyunLiang/FKP?style=social)](https://github.com/JingyunLiang/FKP)*
- - Aug. 17, 2021: See our recent work for flow-based image SR: [Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling (HCFlow), ICCV2021](https://github.com/JingyunLiang/HCFlow) [![arXiv](https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg)](https://arxiv.org/abs/2108.05301)
+ - Aug. 17, 2021: See our recent work for [generative modelling of image SR: Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling (HCFlow), ICCV2021](https://github.com/JingyunLiang/HCFlow) [![arXiv](https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg)](https://arxiv.org/abs/2108.05301)
 [![GitHub Stars](https://img.shields.io/github/stars/JingyunLiang/HCFlow?style=social)](https://github.com/JingyunLiang/HCFlow)[![download](https://img.shields.io/github/downloads/JingyunLiang/HCFlow/total.svg)](https://github.com/JingyunLiang/HCFlow/releases)[ <a href="https://colab.research.google.com/gist/JingyunLiang/cdb3fef89ebd174eaa43794accb6f59d/hcflow-demo-on-x8-face-image-sr.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a>](https://colab.research.google.com/gist/JingyunLiang/cdb3fef89ebd174eaa43794accb6f59d/hcflow-demo-on-x8-face-image-sr.ipynb)
- - Aug. 17, 2021: See our recent work for real-world image SR: [Designing a Practical Degradation Model for Deep Blind Image Super-Resolution (BSRGAN), ICCV2021](https://github.com/cszn/BSRGAN)  [![arXiv](https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg)](https://arxiv.org/abs/2103.14006)
+ - Aug. 17, 2021: See our recent work for [real-world image SR: Designing a Practical Degradation Model for Deep Blind Image Super-Resolution (BSRGAN), ICCV2021](https://github.com/cszn/BSRGAN)  [![arXiv](https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg)](https://arxiv.org/abs/2103.14006)
 [![GitHub Stars](https://img.shields.io/github/stars/cszn/BSRGAN?style=social)](https://github.com/cszn/BSRGAN)
 
   ---
@@ -39,7 +39,7 @@ Note: this repository is based on [BasicSR](https://github.com/xinntao/BasicSR#m
 
 
 ## Quick Run
-Download `stage3_MANet+RRDB_x4.pth` from [release](https://github.com/JingyunLiang/MANet/releases/tag/v0.0) and put it in `./pretrained_models`. Then, run following command. Or you can go to our [online Colab demo for MANet kernel estimation <a href="https://colab.research.google.com/gist/JingyunLiang/4ed2524d6e08343710ee408a4d997e1c/manet-demo-on-spatially-variant-kernel-estimation.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a>](https://colab.research.google.com/gist/JingyunLiang/4ed2524d6e08343710ee408a4d997e1c/manet-demo-on-spatially-variant-kernel-estimation.ipynb) to have a try.
+Download `stage3_MANet+RRDB_x4.pth` from [release](https://github.com/JingyunLiang/MANet/releases) and put it in `./pretrained_models`. Then, run following command. Or you can go to our [online Colab demo for MANet kernel estimation <a href="https://colab.research.google.com/gist/JingyunLiang/4ed2524d6e08343710ee408a4d997e1c/manet-demo-on-spatially-variant-kernel-estimation.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a>](https://colab.research.google.com/gist/JingyunLiang/4ed2524d6e08343710ee408a4d997e1c/manet-demo-on-spatially-variant-kernel-estimation.ipynb) to have a try.
 ```bash
 cd codes
 python test.py --opt options/test/test_stage3.yml
@@ -70,7 +70,7 @@ Step3: to fine-tune RRDB with MANet, run this command:
 python train.py --opt options/train/train_stage3.yml
 ```
 
-All trained models can be downloaded from [release](https://github.com/JingyunLiang/MANet/releases/tag/v0.0). For testing, downloading stage3 models is enough.
+All trained models can be downloaded from [release](https://github.com/JingyunLiang/MANet/releases). For testing, downloading stage3 models is enough.
 
 
 ## Testing
@@ -107,7 +107,7 @@ python interactive_explore.py --path ../results/001_MANet_aniso_x4_test_stage1/t
 ```
 
 ## Results
-We conducted experiments on both spatially variant and invariant blind SR. Please refer to the [paper](https://arxiv.org/abs/2108.05302) and [supp](https://github.com/JingyunLiang/MANet/releases/tag/v0.0) for results. 
+We conducted experiments on both spatially variant and invariant blind SR. Please refer to the [paper](https://arxiv.org/abs/2108.05302) and [supp](https://github.com/JingyunLiang/MANet/releases) for results. 
 
 ## Citation
     @inproceedings{liang2021mutual,