App code

LICENSE

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+
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app.py

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+#!/usr/bin/env python3
+
+# Copyright 2023 Yi Xie
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import gradio as gr
+
+import glob
+import hashlib
+import logging
+import os
+import shutil
+import subprocess
+import sys
+import yaml
+
+OUT_DIR = '/tmp'
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger()
+
+known_models_yaml = None
+with open('known_models.yaml', 'r') as f:
+    known_models_yaml = yaml.load(f.read(), Loader=yaml.Loader)
+
+def convert(input_model):
+    last_outputs = glob.glob('*.wifm', root_dir=OUT_DIR) + glob.glob('*.mlpackage', root_dir=OUT_DIR)
+    for output in last_outputs:
+        try:
+            if os.path.isfile(OUT_DIR + '/' + output):
+                os.remove(OUT_DIR + '/' + output)
+            else:
+                shutil.rmtree(OUT_DIR + '/' + output)
+        except Exception as e:
+            logger.error('Failed to remove last output file: ' + str(e))
+
+    file = input_model.name
+    if not file.endswith('.pth'):
+        raise gr.Error('Uploaded file is not PyTorch weights.')
+    digest = None
+    with open(file, 'rb') as f:
+        digest = hashlib.sha256(f.read()).hexdigest()
+
+    for model in known_models_yaml['models']:
+        if digest != model['sha256']:
+            continue
+        name = model['name']
+        out_file = OUT_DIR + '/' + name + '.wifm'
+        logger.info('Converting model: %s', name)
+        command = [
+            'python', 'converter.py',
+            '--type', model['type'],
+            '--name', name,
+            '--scale', str(model['scale']),
+            '--out-dir', OUT_DIR,
+            '--description', model['description'],
+            '--source', model['source'],
+            '--author', model['author'],
+            '--license', model['license']
+        ]
+        if 'cuda' in model and model['cuda']:
+            command += ['--has-cuda']
+        if 'monochrome' in model and model['monochrome']:
+            command += ['--monochrome']
+        if 'features' in model:
+            command += ['--num-features', str(model['features'])]
+        if 'blocks' in model:
+            command += ['--num-blocks', str(model['blocks'])]
+        if 'convs' in model:
+            command += ['--num-convs', str(model['convs'])]
+        command += [file]
+        logger.debug('Command: %s', command)
+        process = subprocess.Popen(command, stdout=subprocess.PIPE)
+        for c in iter(lambda: process.stdout.read(1), b''):
+            sys.stdout.buffer.write(c)
+            sys.stdout.flush()
+        process.communicate()
+        if process.returncode != 0:
+            raise gr.Error('converter.py returned non-zero exit code ' + str(process.returncode))
+        if not os.path.exists(out_file):
+            raise gr.Error('Unknown error')
+        return out_file
+
+    raise gr.Error('Unknown model. Please create an issue in https://github.com/imxieyi/waifu2x-ios-model-converter if it has a supported architecture.')
+
+models_string = ''
+for model in known_models_yaml['models']:
+    models_string += '- ' + model['file'].split('/')[-1] + '\n'
+
+iface = gr.Interface(
+    fn=convert,
+    inputs='file',
+    outputs='file',
+    title='Web waifu2x-ios Model Converter',
+    description='''
+Please upload the `.pth` model file on the left. After submitting please wait until the output `.wifm` model file appears on the right. Then simply click `Download` to download converted custom model.
+''',
+    article='''
+Supported models (from [upscale.wiki Model Database](https://upscale.wiki/wiki/Model_Database)):
+{}
+
+Project: https://github.com/imxieyi/waifu2x-ios-model-converter
+Report issues: https://github.com/imxieyi/waifu2x-ios-model-converter/issues
+'''.format(models_string),
+)
+iface.launch()

converter.py

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+#!/usr/bin/env python3
+
+# Copyright 2023 Yi Xie
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+import json
+import logging
+import platform
+import os
+import shutil
+import sys
+import zipfile
+
+parser = argparse.ArgumentParser(
+    prog=os.path.basename(__file__),
+    description='Convert a ML model to waifu2x app custom model',
+)
+parser.add_argument('filename')
+required_args = parser.add_argument_group('required')
+required_args.add_argument('--type', choices=['esrgan_old', 'esrgan_old_lite', 'real_esrgan', 'real_esrgan_compact'], required=True, help='Type of the model')
+required_args.add_argument('--name', type=str, required=True, help='Name of the model')
+required_args.add_argument('--scale', type=int, required=True, help='Scale factor of the model')
+required_args.add_argument('--out-dir', type=str, required=True, help='Output directory')
+optional_args = parser.add_argument_group('optional')
+optional_args.add_argument('--monochrome', action='store_true', help='Input model is monochrome (single channel)')
+optional_args.add_argument('--has-cuda', action='store_true', help='Input model contains CUDA object')
+optional_args.add_argument('--num-features', type=int, help='Override number of features for (Real-)ESRGAN model')
+optional_args.add_argument('--num-blocks', type=int, help='Override number of blocks for (Real-)ESRGAN model')
+optional_args.add_argument('--num-convs', type=int, help='Override number of conv layers for Real-ESRGAN Compact model')
+optional_args.add_argument('--input-size', type=int, default=256, help='Input size (both width and height), default to 256')
+optional_args.add_argument('--shrink-size', type=int, default=20, help='Shrink size (applied to all 4 sides on input), default to 20')
+optional_args.add_argument('--description', type=str, required=False, help='Description of the model, supports Markdown')
+optional_args.add_argument('--source', type=str, required=False, help='Source of the model, supports Markdown')
+optional_args.add_argument('--author', type=str, required=False, help='Author of the model, supports Markdown')
+optional_args.add_argument('--license', type=str, required=False, help='License of the model, supports Markdown')
+optional_args.add_argument('--info-md', type=str, required=False, help='Use custom info.md instead of individual flags')
+optional_args.add_argument('--no-delete-mlmodel', action='store_true', help='Don\'t delete the intermediate Core ML model file')
+args = parser.parse_args()
+
+logger = logging.getLogger('converter')
+logger.setLevel(logging.INFO)
+handler = logging.StreamHandler(sys.stdout)
+handler.setLevel(logging.INFO)
+formatter = logging.Formatter('%(levelname)s - %(message)s')
+handler.setFormatter(formatter)
+logger.addHandler(handler)
+
+if args.input_size % 4 != 0:
+    logger.fatal('Input size must be multiple of 4')
+    sys.exit(-1)
+
+if args.shrink_size < 0:
+    logger.fatal('Shrink size must not be < 0')
+    sys.exit(-1)
+
+if args.input_size - 2 * args.shrink_size < 4:
+    logger.fatal('Input size after shrinking is too small')
+    sys.exit(-1)
+
+os.makedirs(args.out_dir, exist_ok=True)
+
+import coremltools as ct
+import torch
+
+torch_model = None
+input_tensor = None
+output_tensor = None
+
+device = torch.device('cpu')
+if platform.system() == 'Darwin' and torch.backends.mps.is_available():
+    device = torch.device('mps')
+    logger.info('Using torch device mps')
+elif torch.cuda.is_available():
+    device = torch.device('cuda')
+    logger.info('Using torch device cuda')
+else:
+    logger.info('Using torch device cpu, please be patient')
+
+logger.info('Creating model architecture')
+channels = 3
+if args.monochrome:
+    channels = 1
+
+num_features = 64
+num_blocks = 23
+num_convs = 16
+
+if args.type == 'esrgan_old_lite':
+    num_features = 32
+    num_blocks = 12
+
+if args.num_features is not None:
+    num_features = args.num_features
+if args.num_blocks is not None:
+    num_blocks = args.num_blocks
+if args.num_convs is not None:
+    num_convs = args.num_convs
+
+if args.type == 'esrgan_old' or args.type == 'esrgan_old_lite':
+    from esrgan_old import architecture
+    torch_model = architecture.RRDB_Net(
+        channels, channels, num_features, num_blocks, gc=32, upscale=args.scale, norm_type=None,
+        act_type='leakyrelu', mode='CNA', res_scale=1, upsample_mode='upconv')
+elif args.type == 'real_esrgan':
+    from basicsr.archs.rrdbnet_arch import RRDBNet
+    torch_model = RRDBNet(num_in_ch=channels, num_out_ch=channels, num_feat=num_features, num_block=num_blocks, num_grow_ch=32, scale=args.scale)
+elif args.type == 'real_esrgan_compact':
+    from basicsr.archs.srvgg_arch import SRVGGNetCompact
+    torch_model = SRVGGNetCompact(num_in_ch=channels, num_out_ch=channels, num_feat=num_features, num_conv=num_convs, upscale=args.scale, act_type='prelu')
+else:
+    logger.fatal('Unknown model type: %s', args.type)
+    sys.exit(-1)
+
+logger.info('Loading weights')
+loadnet = None
+if args.has_cuda:
+    loadnet = torch.load(args.filename, map_location=device)
+else:
+    loadnet = torch.load(args.filename)
+
+if 'params_ema' in loadnet:
+    loadnet = loadnet['params_ema']
+elif 'params' in loadnet:
+    loadnet = loadnet['params']
+torch_model.load_state_dict(loadnet, strict=True)
+
+if args.monochrome:
+    from torch import nn
+    class MonochromeWrapper(nn.Module):
+        def __init__(self, model: nn.Module):
+            super(MonochromeWrapper, self).__init__()
+            self.model = model
+        def forward(self, x: torch.Tensor):
+            x = torch.mean(x, dim=1, keepdim=True)
+            x = self.model(x)
+            x = x.repeat([1, 3, 1, 1])
+            return x
+    torch_model = MonochromeWrapper(torch_model)
+
+logger.info('Tracing model, will take a long time and a lot of RAM')
+torch_model.eval()
+torch_model = torch_model.to(device)
+example_input = torch.zeros(1, 3, 16, 16)
+example_input = example_input.to(device)
+traced_model = torch.jit.trace(torch_model, example_input)
+out = traced_model(example_input)
+logger.info('Successfully traced model')
+
+input_size = example_input.shape[-1]
+output_size = out.shape[-1]
+if args.scale != output_size / input_size:
+    logger.fatal('Expected output scale to be %d, but is actually %.2f', args.scale, output_size / input_size)
+    sys.exit(-1)
+
+logger.info('Converting to Core ML')
+input_shape = [1, 3, args.input_size, args.input_size]
+output_size = args.input_size * args.scale
+output_shape = [1, 3, output_size, output_size]
+model = ct.convert(
+    traced_model,
+    convert_to="mlprogram",
+    inputs=[ct.TensorType(shape=input_shape)]
+)
+model_name = args.filename.split('/')[-1].split('.')[0]
+mlmodel_file = args.out_dir + '/' + model_name + '.mlpackage'
+model.save(mlmodel_file)
+
+logger.info('Packaging model')
+spec = model.get_spec()
+input_name = spec.description.input[0].name
+output_name = spec.description.output[0].name
+logger.debug('Model input name: %s, size: %s', input_name, args.input_size)
+output_size_shrinked = (args.input_size - 2 * args.shrink_size) * args.scale
+logger.debug('Model output name: %s, size: %s, after shrinking: %s', output_name, output_size, output_size_shrinked)
+
+manifest = {
+    "version": 1,
+    "name": args.name,
+    "type": "coreml",
+    "subModels": {
+        "main": {
+            "file": mlmodel_file,
+            "inputName": input_name,
+            "outputName": output_name
+        }
+    },
+    "dataFormat": "nchw",
+    "inputShape": input_shape,
+    "shrinkSize": args.shrink_size,
+    "scale": args.scale,
+    "alphaMode": "sameAsMain"
+}
+
+info_md = '''
+{}
+===
+Converted by [waifu2x-ios-model-converter](https://github.com/imxieyi/waifu2x-ios-model-converter).
+
+'''.format(args.name)
+
+if args.description is not None:
+    info_md += '''
+## Description
+{}
+
+'''.format(args.description)
+
+if args.author is not None:
+    info_md += '''
+## Author
+{}
+
+'''.format(args.author)
+
+if args.source is not None:
+    info_md += '''
+## Source
+{}
+
+'''.format(args.source)
+
+if args.license is not None:
+    info_md += '''
+## License
+{}
+
+'''.format(args.license)
+
+if len(info_md) > 1024 * 1024:
+    logger.fatal('Model info.md too large. Try to reduce license file size, etc.')
+    sys.exit(-1)
+
+def add_folder_to_zip(folder, zipfile):
+    for folderName, subfolders, filenames in os.walk(folder):
+        for filename in filenames:
+            filePath = os.path.join(folderName, filename)
+            zipfile.write(filePath, filePath)
+
+zip_file = args.out_dir + '/' + args.name + '.wifm'
+with zipfile.ZipFile(zip_file, 'w', compression=zipfile.ZIP_DEFLATED) as modelzip:
+    modelzip.writestr('manifest.json', json.dumps(manifest))
+    modelzip.writestr('info.md', info_md)
+    if os.path.isfile(mlmodel_file):
+        modelzip.write(mlmodel_file)
+    else:
+        add_folder_to_zip(mlmodel_file, modelzip)
+
+if not args.no_delete_mlmodel:
+    if os.path.isfile(mlmodel_file):
+        os.remove(mlmodel_file)
+    else:
+        shutil.rmtree(mlmodel_file)
+
+logger.info('Successfully converted model: %s', zip_file)

esrgan_old/LICENSE

@@ -0,0 +1,201 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
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esrgan_old/README.md

@@ -0,0 +1,174 @@
+# ESRGAN (Enhanced SRGAN) [[Paper]](https://arxiv.org/abs/1809.00219) [[BasicSR]](https://github.com/xinntao/BasicSR) 
+## :smiley: Training codes are in [BasicSR](https://github.com/xinntao/BasicSR) repo.
+### Enhanced Super-Resolution Generative Adversarial Networks
+By Xintao Wang, [Ke Yu](https://yuke93.github.io/), Shixiang Wu, [Jinjin Gu](http://www.jasongt.com/), Yihao Liu, [Chao Dong](https://scholar.google.com.hk/citations?user=OSDCB0UAAAAJ&hl=en), [Yu Qiao](http://mmlab.siat.ac.cn/yuqiao/), [Chen Change Loy](http://personal.ie.cuhk.edu.hk/~ccloy/)
+
+This repo only provides simple testing codes, pretrained models and the network strategy demo. 
+
+### **For full training and testing codes, please refer to  [BasicSR](https://github.com/xinntao/BasicSR).**
+
+We won the first place in [PIRM2018-SR competition](https://www.pirm2018.org/PIRM-SR.html) (region 3) and got the best perceptual index.
+The paper is accepted to [ECCV2018 PIRM Workshop](https://pirm2018.org/).
+
+:triangular_flag_on_post: Add [Frequently Asked Questions](https://github.com/xinntao/ESRGAN/blob/master/QA.md).
+
+> For instance,
+> 1. How to reproduce your results in the PIRM18-SR Challenge (with low perceptual index)?
+> 2. How do you get the perceptual index in your ESRGAN paper?
+
+#### BibTeX
+<!--
+    @article{wang2018esrgan,
+        author={Wang, Xintao and Yu, Ke and Wu, Shixiang and Gu, Jinjin and Liu, Yihao and Dong, Chao and Loy, Chen Change and Qiao, Yu and Tang, Xiaoou},
+        title={ESRGAN: Enhanced super-resolution generative adversarial networks},
+        journal={arXiv preprint arXiv:1809.00219},
+        year={2018}
+    }
+-->    
+    @InProceedings{wang2018esrgan,
+        author = {Wang, Xintao and Yu, Ke and Wu, Shixiang and Gu, Jinjin and Liu, Yihao and Dong, Chao and Qiao, Yu and Loy, Chen Change},
+        title = {ESRGAN: Enhanced super-resolution generative adversarial networks},
+        booktitle = {The European Conference on Computer Vision Workshops (ECCVW)},
+        month = {September},
+        year = {2018}
+    }
+
+<p align="center">
+  <img src="figures/baboon.jpg">
+</p>
+
+The **RRDB_PSNR** PSNR_oriented model trained with DF2K dataset (a merged dataset with [DIV2K](https://data.vision.ee.ethz.ch/cvl/DIV2K/) and [Flickr2K](http://cv.snu.ac.kr/research/EDSR/Flickr2K.tar) (proposed in [EDSR](https://github.com/LimBee/NTIRE2017))) is also able to achive high PSNR performance.
+    
+| <sub>Method</sub> | <sub>Training dataset</sub> | <sub>Set5</sub> | <sub>Set14</sub> | <sub>BSD100</sub> | <sub>Urban100</sub> | <sub>Manga109</sub> |
+|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
+| <sub>[SRCNN](http://mmlab.ie.cuhk.edu.hk/projects/SRCNN.html)</sub>| <sub>291</sub>| <sub>30.48/0.8628</sub> |<sub>27.50/0.7513</sub>|<sub>26.90/0.7101</sub>|<sub>24.52/0.7221</sub>|<sub>27.58/0.8555</sub>|
+| <sub>[EDSR](https://github.com/thstkdgus35/EDSR-PyTorch)</sub> | <sub>DIV2K</sub> | <sub>32.46/0.8968</sub> | <sub>28.80/0.7876</sub> | <sub>27.71/0.7420</sub> | <sub>26.64/0.8033</sub> | <sub>31.02/0.9148</sub> |
+| <sub>[RCAN](https://github.com/yulunzhang/RCAN)</sub> |  <sub>DIV2K</sub> | <sub>32.63/0.9002</sub> | <sub>28.87/0.7889</sub> | <sub>27.77/0.7436</sub> | <sub>26.82/ 0.8087</sub>| <sub>31.22/ 0.9173</sub>|
+|<sub>RRDB(ours)</sub>| <sub>DF2K</sub>| <sub>**32.73/0.9011**</sub> |<sub>**28.99/0.7917**</sub> |<sub>**27.85/0.7455**</sub> |<sub>**27.03/0.8153**</sub> |<sub>**31.66/0.9196**</sub>|
+
+## Quick Test
+#### Dependencies
+- Python 3
+- [PyTorch >= 0.4](https://pytorch.org/) (CUDA version >= 7.5 if installing with CUDA. [More details](https://pytorch.org/get-started/previous-versions/))
+- Python packages:  `pip install numpy opencv-python`
+
+### Test models
+1. Clone this github repo. 
+```
+git clone https://github.com/xinntao/ESRGAN
+cd ESRGAN
+```
+2. Place your own **low-resolution images** in `./LR` folder. (There are two sample images - baboon and comic). 
+3. Download pretrained models from [Google Drive](https://drive.google.com/drive/u/0/folders/17VYV_SoZZesU6mbxz2dMAIccSSlqLecY) or [Baidu Drive](https://pan.baidu.com/s/1-Lh6ma-wXzfH8NqeBtPaFQ). Place the models in `./models`. We provide two models with high perceptual quality and high PSNR performance (see [model list](https://github.com/xinntao/ESRGAN/tree/master/models)).
+4. Run test. We provide ESRGAN model and RRDB_PSNR model.
+```
+python test.py models/RRDB_ESRGAN_x4.pth
+python test.py models/RRDB_PSNR_x4.pth
+```
+5. The results are in `./results` folder.
+### Network interpolation demo
+You can interpolate the RRDB_ESRGAN and RRDB_PSNR models with alpha in [0, 1].
+
+1. Run `python net_interp.py 0.8`, where *0.8* is the interpolation parameter and you can change it to any value in [0,1].
+2. Run `python test.py models/interp_08.pth`, where *models/interp_08.pth* is the model path.
+
+<p align="center">
+  <img height="400" src="figures/43074.gif">
+</p>
+
+## Perceptual-driven SR Results
+
+You can download all the resutls from [Google Drive](https://drive.google.com/drive/folders/1iaM-c6EgT1FNoJAOKmDrK7YhEhtlKcLx?usp=sharing). (:heavy_check_mark: included;  :heavy_minus_sign: not included; :o: TODO)
+
+HR images can be downloaed from [BasicSR-Datasets](https://github.com/xinntao/BasicSR#datasets).
+
+| Datasets |LR | [*ESRGAN*](https://arxiv.org/abs/1809.00219) | [SRGAN](https://arxiv.org/abs/1609.04802) | [EnhanceNet](http://openaccess.thecvf.com/content_ICCV_2017/papers/Sajjadi_EnhanceNet_Single_Image_ICCV_2017_paper.pdf) | [CX](https://arxiv.org/abs/1803.04626) |
+|:---:|:---:|:---:|:---:|:---:|:---:|
+| Set5 |:heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark:| :o: |
+| Set14 | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark:| :o: |
+| BSDS100 | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark:| :o: |
+| [PIRM](https://pirm.github.io/) <br><sup>(val, test)</sup> | :heavy_check_mark: | :heavy_check_mark: | :heavy_minus_sign: | :heavy_check_mark:| :heavy_check_mark: |
+| [OST300](https://arxiv.org/pdf/1804.02815.pdf) |:heavy_check_mark: | :heavy_check_mark: | :heavy_minus_sign: | :heavy_check_mark:| :o: |
+| urban100 | :heavy_check_mark: | :heavy_check_mark: | :heavy_minus_sign: | :heavy_check_mark:| :o: |
+| [DIV2K](https://data.vision.ee.ethz.ch/cvl/DIV2K/) <br><sup>(val, test)</sup> | :heavy_check_mark: | :heavy_check_mark: | :heavy_minus_sign: | :heavy_check_mark:| :o: |
+
+## ESRGAN
+We improve the [SRGAN](https://arxiv.org/abs/1609.04802) from three aspects:
+1. adopt a deeper model using Residual-in-Residual Dense Block (RRDB) without batch normalization layers.
+2. employ [Relativistic average GAN](https://ajolicoeur.wordpress.com/relativisticgan/) instead of the vanilla GAN.
+3. improve the perceptual loss by using the features before activation.
+
+In contrast to SRGAN, which claimed that **deeper models are increasingly difficult to train**, our deeper ESRGAN model shows its superior performance with easy training.
+
+<p align="center">
+  <img height="120" src="figures/architecture.jpg">
+</p>
+<p align="center">
+  <img height="180" src="figures/RRDB.png">
+</p>
+
+## Network Interpolation
+We propose the **network interpolation strategy** to balance the visual quality and PSNR.
+
+<p align="center">
+  <img height="500" src="figures/net_interp.jpg">
+</p>
+
+We show the smooth animation with the interpolation parameters changing from 0 to 1. 
+Interestingly, it is observed that the network interpolation strategy provides a smooth control of the RRDB_PSNR model and the fine-tuned ESRGAN model.
+
+<p align="center">
+  <img height="480" src="figures/81.gif">
+  &nbsp &nbsp
+  <img height="480" src="figures/102061.gif">
+</p>
+  
+## Qualitative Results
+PSNR (evaluated on the Y channel) and the perceptual index used in the PIRM-SR challenge are also provided for reference.
+
+<p align="center">
+  <img src="figures/qualitative_cmp_01.jpg">
+</p>
+<p align="center">
+  <img src="figures/qualitative_cmp_02.jpg">
+</p>
+<p align="center">
+  <img src="figures/qualitative_cmp_03.jpg">
+</p>
+<p align="center">
+  <img src="figures/qualitative_cmp_04.jpg">
+</p>
+
+## Ablation Study
+Overall visual comparisons for showing the effects of each component in
+ESRGAN. Each column represents a model with its configurations in the top.
+The red sign indicates the main improvement compared with the previous model.
+<p align="center">
+  <img src="figures/abalation_study.png">
+</p>
+
+## BN artifacts
+We empirically observe that BN layers tend to bring artifacts. These artifacts,
+namely BN artifacts, occasionally appear among iterations and different settings,
+violating the needs for a stable performance over training. We find that 
+the network depth, BN position, training dataset and training loss
+have impact on the occurrence of BN artifacts.
+<p align="center">
+  <img src="figures/BN_artifacts.jpg">
+</p>
+
+## Useful techniques to train a very deep network
+We find that residual scaling and smaller initialization can help to train a very deep network. More details are in the Supplementary File attached in our [paper](https://arxiv.org/abs/1809.00219).
+
+<p align="center">
+  <img height="250" src="figures/train_deeper_neta.png">
+  <img height="250" src="figures/train_deeper_netb.png">
+</p>
+
+## The influence of training patch size
+We observe that training a deeper network benefits from a larger patch size. Moreover, the deeper model achieves more improvement (∼0.12dB) than the shallower one (∼0.04dB) since larger model capacity is capable of taking full advantage of
+larger training patch size. (Evaluated on Set5 dataset with RGB channels.)
+<p align="center">
+  <img height="250" src="figures/patch_a.png">
+  <img height="250" src="figures/patch_b.png">
+</p>

esrgan_old/architecture.py

@@ -0,0 +1,38 @@
+import math
+import torch
+import torch.nn as nn
+from . import block as B
+
+
+class RRDB_Net(nn.Module):
+    def __init__(self, in_nc, out_nc, nf, nb, gc=32, upscale=4, norm_type=None, act_type='leakyrelu', \
+            mode='CNA', res_scale=1, upsample_mode='upconv'):
+        super(RRDB_Net, self).__init__()
+        n_upscale = int(math.log(upscale, 2))
+        if upscale == 3:
+            n_upscale = 1
+
+        fea_conv = B.conv_block(in_nc, nf, kernel_size=3, norm_type=None, act_type=None)
+        rb_blocks = [B.RRDB(nf, kernel_size=3, gc=32, stride=1, bias=True, pad_type='zero', \
+            norm_type=norm_type, act_type=act_type, mode='CNA') for _ in range(nb)]
+        LR_conv = B.conv_block(nf, nf, kernel_size=3, norm_type=norm_type, act_type=None, mode=mode)
+
+        if upsample_mode == 'upconv':
+            upsample_block = B.upconv_blcok
+        elif upsample_mode == 'pixelshuffle':
+            upsample_block = B.pixelshuffle_block
+        else:
+            raise NotImplementedError('upsample mode [%s] is not found' % upsample_mode)
+        if upscale == 3:
+            upsampler = upsample_block(nf, nf, 3, act_type=act_type)
+        else:
+            upsampler = [upsample_block(nf, nf, act_type=act_type) for _ in range(n_upscale)]
+        HR_conv0 = B.conv_block(nf, nf, kernel_size=3, norm_type=None, act_type=act_type)
+        HR_conv1 = B.conv_block(nf, out_nc, kernel_size=3, norm_type=None, act_type=None)
+
+        self.model = B.sequential(fea_conv, B.ShortcutBlock(B.sequential(*rb_blocks, LR_conv)),\
+            *upsampler, HR_conv0, HR_conv1)
+
+    def forward(self, x):
+        x = self.model(x)
+        return x

esrgan_old/block.py

@@ -0,0 +1,261 @@
+from collections import OrderedDict
+import torch
+import torch.nn as nn
+
+####################
+# Basic blocks
+####################
+
+
+def act(act_type, inplace=True, neg_slope=0.2, n_prelu=1):
+    # helper selecting activation
+    # neg_slope: for leakyrelu and init of prelu
+    # n_prelu: for p_relu num_parameters
+    act_type = act_type.lower()
+    if act_type == 'relu':
+        layer = nn.ReLU(inplace)
+    elif act_type == 'leakyrelu':
+        layer = nn.LeakyReLU(neg_slope, inplace)
+    elif act_type == 'prelu':
+        layer = nn.PReLU(num_parameters=n_prelu, init=neg_slope)
+    else:
+        raise NotImplementedError('activation layer [%s] is not found' % act_type)
+    return layer
+
+
+def norm(norm_type, nc):
+    # helper selecting normalization layer
+    norm_type = norm_type.lower()
+    if norm_type == 'batch':
+        layer = nn.BatchNorm2d(nc, affine=True)
+    elif norm_type == 'instance':
+        layer = nn.InstanceNorm2d(nc, affine=False)
+    else:
+        raise NotImplementedError('normalization layer [%s] is not found' % norm_type)
+    return layer
+
+
+def pad(pad_type, padding):
+    # helper selecting padding layer
+    # if padding is 'zero', do by conv layers
+    pad_type = pad_type.lower()
+    if padding == 0:
+        return None
+    if pad_type == 'reflect':
+        layer = nn.ReflectionPad2d(padding)
+    elif pad_type == 'replicate':
+        layer = nn.ReplicationPad2d(padding)
+    else:
+        raise NotImplementedError('padding layer [%s] is not implemented' % pad_type)
+    return layer
+
+
+def get_valid_padding(kernel_size, dilation):
+    kernel_size = kernel_size + (kernel_size - 1) * (dilation - 1)
+    padding = (kernel_size - 1) // 2
+    return padding
+
+
+class ConcatBlock(nn.Module):
+    # Concat the output of a submodule to its input
+    def __init__(self, submodule):
+        super(ConcatBlock, self).__init__()
+        self.sub = submodule
+
+    def forward(self, x):
+        output = torch.cat((x, self.sub(x)), dim=1)
+        return output
+
+    def __repr__(self):
+        tmpstr = 'Identity .. \n|'
+        modstr = self.sub.__repr__().replace('\n', '\n|')
+        tmpstr = tmpstr + modstr
+        return tmpstr
+
+
+class ShortcutBlock(nn.Module):
+    #Elementwise sum the output of a submodule to its input
+    def __init__(self, submodule):
+        super(ShortcutBlock, self).__init__()
+        self.sub = submodule
+
+    def forward(self, x):
+        output = x + self.sub(x)
+        return output
+
+    def __repr__(self):
+        tmpstr = 'Identity + \n|'
+        modstr = self.sub.__repr__().replace('\n', '\n|')
+        tmpstr = tmpstr + modstr
+        return tmpstr
+
+
+def sequential(*args):
+    # Flatten Sequential. It unwraps nn.Sequential.
+    if len(args) == 1:
+        if isinstance(args[0], OrderedDict):
+            raise NotImplementedError('sequential does not support OrderedDict input.')
+        return args[0]  # No sequential is needed.
+    modules = []
+    for module in args:
+        if isinstance(module, nn.Sequential):
+            for submodule in module.children():
+                modules.append(submodule)
+        elif isinstance(module, nn.Module):
+            modules.append(module)
+    return nn.Sequential(*modules)
+
+
+def conv_block(in_nc, out_nc, kernel_size, stride=1, dilation=1, groups=1, bias=True,
+               pad_type='zero', norm_type=None, act_type='relu', mode='CNA'):
+    """
+    Conv layer with padding, normalization, activation
+    mode: CNA --> Conv -> Norm -> Act
+        NAC --> Norm -> Act --> Conv (Identity Mappings in Deep Residual Networks, ECCV16)
+    """
+    assert mode in ['CNA', 'NAC', 'CNAC'], 'Wong conv mode [%s]' % mode
+    padding = get_valid_padding(kernel_size, dilation)
+    p = pad(pad_type, padding) if pad_type and pad_type != 'zero' else None
+    padding = padding if pad_type == 'zero' else 0
+
+    c = nn.Conv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding, \
+            dilation=dilation, bias=bias, groups=groups)
+    a = act(act_type) if act_type else None
+    if 'CNA' in mode:
+        n = norm(norm_type, out_nc) if norm_type else None
+        return sequential(p, c, n, a)
+    elif mode == 'NAC':
+        if norm_type is None and act_type is not None:
+            a = act(act_type, inplace=False)
+            # Important!
+            # input----ReLU(inplace)----Conv--+----output
+            #        |________________________|
+            # inplace ReLU will modify the input, therefore wrong output
+        n = norm(norm_type, in_nc) if norm_type else None
+        return sequential(n, a, p, c)
+
+
+####################
+# Useful blocks
+####################
+
+
+class ResNetBlock(nn.Module):
+    """
+    ResNet Block, 3-3 style
+    with extra residual scaling used in EDSR
+    (Enhanced Deep Residual Networks for Single Image Super-Resolution, CVPRW 17)
+    """
+
+    def __init__(self, in_nc, mid_nc, out_nc, kernel_size=3, stride=1, dilation=1, groups=1, \
+            bias=True, pad_type='zero', norm_type=None, act_type='relu', mode='CNA', res_scale=1):
+        super(ResNetBlock, self).__init__()
+        conv0 = conv_block(in_nc, mid_nc, kernel_size, stride, dilation, groups, bias, pad_type, \
+            norm_type, act_type, mode)
+        if mode == 'CNA':
+            act_type = None
+        if mode == 'CNAC':  # Residual path: |-CNAC-|
+            act_type = None
+            norm_type = None
+        conv1 = conv_block(mid_nc, out_nc, kernel_size, stride, dilation, groups, bias, pad_type, \
+            norm_type, act_type, mode)
+        # if in_nc != out_nc:
+        #     self.project = conv_block(in_nc, out_nc, 1, stride, dilation, 1, bias, pad_type, \
+        #         None, None)
+        #     print('Need a projecter in ResNetBlock.')
+        # else:
+        #     self.project = lambda x:x
+        self.res = sequential(conv0, conv1)
+        self.res_scale = res_scale
+
+    def forward(self, x):
+        res = self.res(x).mul(self.res_scale)
+        return x + res
+
+
+class ResidualDenseBlock_5C(nn.Module):
+    """
+    Residual Dense Block
+    style: 5 convs
+    The core module of paper: (Residual Dense Network for Image Super-Resolution, CVPR 18)
+    """
+
+    def __init__(self, nc, kernel_size=3, gc=32, stride=1, bias=True, pad_type='zero', \
+            norm_type=None, act_type='leakyrelu', mode='CNA'):
+        super(ResidualDenseBlock_5C, self).__init__()
+        # gc: growth channel, i.e. intermediate channels
+        self.conv1 = conv_block(nc, gc, kernel_size, stride, bias=bias, pad_type=pad_type, \
+            norm_type=norm_type, act_type=act_type, mode=mode)
+        self.conv2 = conv_block(nc+gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type, \
+            norm_type=norm_type, act_type=act_type, mode=mode)
+        self.conv3 = conv_block(nc+2*gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type, \
+            norm_type=norm_type, act_type=act_type, mode=mode)
+        self.conv4 = conv_block(nc+3*gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type, \
+            norm_type=norm_type, act_type=act_type, mode=mode)
+        if mode == 'CNA':
+            last_act = None
+        else:
+            last_act = act_type
+        self.conv5 = conv_block(nc+4*gc, nc, 3, stride, bias=bias, pad_type=pad_type, \
+            norm_type=norm_type, act_type=last_act, mode=mode)
+
+    def forward(self, x):
+        x1 = self.conv1(x)
+        x2 = self.conv2(torch.cat((x, x1), 1))
+        x3 = self.conv3(torch.cat((x, x1, x2), 1))
+        x4 = self.conv4(torch.cat((x, x1, x2, x3), 1))
+        x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
+        return x5.mul(0.2) + x
+
+
+class RRDB(nn.Module):
+    """
+    Residual in Residual Dense Block
+    """
+
+    def __init__(self, nc, kernel_size=3, gc=32, stride=1, bias=True, pad_type='zero', \
+            norm_type=None, act_type='leakyrelu', mode='CNA'):
+        super(RRDB, self).__init__()
+        self.RDB1 = ResidualDenseBlock_5C(nc, kernel_size, gc, stride, bias, pad_type, \
+            norm_type, act_type, mode)
+        self.RDB2 = ResidualDenseBlock_5C(nc, kernel_size, gc, stride, bias, pad_type, \
+            norm_type, act_type, mode)
+        self.RDB3 = ResidualDenseBlock_5C(nc, kernel_size, gc, stride, bias, pad_type, \
+            norm_type, act_type, mode)
+
+    def forward(self, x):
+        out = self.RDB1(x)
+        out = self.RDB2(out)
+        out = self.RDB3(out)
+        return out.mul(0.2) + x
+
+
+####################
+# Upsampler
+####################
+
+
+def pixelshuffle_block(in_nc, out_nc, upscale_factor=2, kernel_size=3, stride=1, bias=True,
+                        pad_type='zero', norm_type=None, act_type='relu'):
+    """
+    Pixel shuffle layer
+    (Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional
+    Neural Network, CVPR17)
+    """
+    conv = conv_block(in_nc, out_nc * (upscale_factor ** 2), kernel_size, stride, bias=bias,
+                        pad_type=pad_type, norm_type=None, act_type=None)
+    pixel_shuffle = nn.PixelShuffle(upscale_factor)
+
+    n = norm(norm_type, out_nc) if norm_type else None
+    a = act(act_type) if act_type else None
+    return sequential(conv, pixel_shuffle, n, a)
+
+
+def upconv_blcok(in_nc, out_nc, upscale_factor=2, kernel_size=3, stride=1, bias=True,
+                pad_type='zero', norm_type=None, act_type='relu', mode='nearest'):
+    # Up conv
+    # described in https://distill.pub/2016/deconv-checkerboard/
+    upsample = nn.Upsample(scale_factor=upscale_factor, mode=mode)
+    conv = conv_block(in_nc, out_nc, kernel_size, stride, bias=bias,
+                        pad_type=pad_type, norm_type=norm_type, act_type=act_type)
+    return sequential(upsample, conv)

esrgan_old/net_interp.py

@@ -0,0 +1,21 @@
+import sys
+import torch
+from collections import OrderedDict
+
+alpha = float(sys.argv[1])
+
+net_PSNR_path = './models/RRDB_PSNR_x4.pth'
+net_ESRGAN_path = './models/RRDB_ESRGAN_x4.pth'
+net_interp_path = './models/interp_{:02d}.pth'.format(int(alpha*10))
+
+net_PSNR = torch.load(net_PSNR_path)
+net_ESRGAN = torch.load(net_ESRGAN_path)
+net_interp = OrderedDict()
+
+print('Interpolating with alpha = ', alpha)
+
+for k, v_PSNR in net_PSNR.items():
+    v_ESRGAN = net_ESRGAN[k]
+    net_interp[k] = (1 - alpha) * v_PSNR + alpha * v_ESRGAN
+
+torch.save(net_interp, net_interp_path)

known_models.yaml

@@ -0,0 +1,168 @@
+models:
+- name: "AnimeSharp 4x"
+  type: esrgan_old
+  file: "./torch_models/4x-AnimeSharp.pth"
+  sha256: e7a7de2dafd7331c1992862bbbcd9e9712a9f9f8e6303f0aaa59b4341d359bab
+  scale: 4
+  description: "Anime or Text"
+  author: "[Kim2091](https://upscale.wiki/wiki/User:Kim2091)"
+  source: "[upscale.wiki Model Database](https://upscale.wiki/wiki/Model_Database)"
+  license: "[CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+- name: "AnimeSharp Lite 4x"
+  type: esrgan_old_lite
+  file: "./torch_models/4x-AnimeSharp-lite.pth"
+  sha256: a0a224521dcc547768e8442e83b68f98f485b3cbcc8bd207a6284fff6636329c
+  scale: 4
+  description: "Anime"
+  author: "[Kim2091](https://upscale.wiki/wiki/User:Kim2091)"
+  source: "[upscale.wiki Model Database](https://upscale.wiki/wiki/Model_Database)"
+  license: "[CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+- name: "UltraSharp 4x"
+  type: esrgan_old
+  file: "./torch_models/4x-UltraSharp.pth"
+  sha256: a5812231fc936b42af08a5edba784195495d303d5b3248c24489ef0c4021fe01
+  scale: 4
+  description: "Universal Upscaler"
+  author: "[Kim2091](https://upscale.wiki/wiki/User:Kim2091)"
+  source: "[upscale.wiki Model Database](https://upscale.wiki/wiki/Model_Database)"
+  license: "[CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+- name: "NMKD Siax (\"CX\") 4x"
+  type: esrgan_old
+  file: "./torch_models/4x_NMKD-Siax_200k.pth"
+  sha256: 560424d9f68625713fc47e9e7289a98aabe1d744e1cd6a9ae5a35e9957fd127e
+  scale: 4
+  description: "Universal upscaler for clean and slightly compressed images"
+  author: "[Nmkd](https://upscale.wiki/wiki/User:Nmkd)"
+  source: "[upscale.wiki Model Database](https://upscale.wiki/wiki/Model_Database)"
+  license: "[WTFPL](http://www.wtfpl.net/)"
+- name: "NMKD Jaywreck3 Lite"
+  type: esrgan_old_lite
+  file: "./torch_models/1x_NMKD-Jaywreck3-Lite_320k.pth"
+  sha256: 3b2654a3bfa6e07bdebde48414d69b89ee3a1d1516ff9b26c3f4c6ee14f7d3f0
+  scale: 1
+  description: "Restore JPEG compression"
+  author: "[Nmkd](https://upscale.wiki/wiki/User:Nmkd)"
+  source: "[upscale.wiki Model Database](https://upscale.wiki/wiki/Model_Database)"
+  license: "[WTFPL](http://www.wtfpl.net/)"
+- name: "NMKD Jaywreck3 Soft Lite"
+  type: esrgan_old_lite
+  file: "./torch_models/1x_NMKD-Jaywreck3-Soft-Lite_320k.pth"
+  sha256: bbd1b6e9002ad9cbb4c5049850a361e110b8ed0ae22f5ed0fb49ddf5a6951f53
+  scale: 1
+  description: "Restore JPEG compression"
+  author: "[Nmkd](https://upscale.wiki/wiki/User:Nmkd)"
+  source: "[upscale.wiki Model Database](https://upscale.wiki/wiki/Model_Database)"
+  license: "[WTFPL](http://www.wtfpl.net/)"
+- name: "DeGif 2x"
+  type: esrgan_old
+  file: "./torch_models/2x_NMKD-DeGIF_210000_G.pth"
+  sha256: a1c4aad3eb19894afda5dcc12982541ce15f79bd47916137cbeb26e702040626
+  scale: 2
+  description: "GIF Restoration"
+  author: "[Nmkd](https://upscale.wiki/wiki/User:Nmkd)"
+  source: "[upscale.wiki Model Database](https://upscale.wiki/wiki/Model_Database)"
+  license: "[WTFPL](http://www.wtfpl.net/)"
+- name: "4x eula digimanga bw v1"
+  type: esrgan_old
+  file: "./torch_models/4x_eula_digimanga_bw_v1_860k.pth"
+  sha256: a3ba78a589cf2f2c39fe77cf3f472173cdcb3ade7dcc77ee5d1cd266d733e3d3
+  scale: 4
+  description: "Black and white digital manga with halftones."
+  author: "end user license agreement#9756"
+  source: "[upscale.wiki Model Database](https://upscale.wiki/wiki/Model_Database)"
+  license: "[CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+- name: "4x eula digimanga bw v2 (Monochrome)"
+  type: esrgan_old
+  file: "./torch_models/4x_eula_digimanga_bw_v2_nc1_307k.pth"
+  sha256: 0c3ff9f7b4fc11b21e1262bca06efada0b0723436623db5e2af37fa2291cb750
+  scale: 4
+  monochrome: true
+  cuda: true
+  description: "Black and white digital manga with halftones. Vast improvement over v1 in low frequency detail. v1 may still be better in some edge cases."
+  author: "end user license agreement#9756"
+  source: "[upscale.wiki Model Database](https://upscale.wiki/wiki/Model_Database)"
+  license: "[CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+- name: "ReFocus V3"
+  type: esrgan_old
+  file: "./torch_models/1x_ReFocus_V3_140000_G.pth"
+  sha256: 94d8f38726c7abaa83a3fad27c01863ba961f76a995d8541c173eb3195062547
+  scale: 1
+  description: "DeBlur, ReFocus, Sharpen Manga, Anime and cartoon style images, but will work on real life images too."
+  author: "[Twittman](https://upscale.wiki/wiki/User:Twittman)"
+  source: "[upscale.wiki Model Database](https://upscale.wiki/wiki/Model_Database)"
+  license: "[CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+- name: "Realistic Rescaler 4x"
+  type: real_esrgan
+  file: "./torch_models/4x_RealisticRescaler_100000_G.pth"
+  sha256: 7381a1229143c9301a94421b610d95eb312e2555743cc9e80099a0e15ac5bd3b
+  scale: 4
+  description: "This model was made to upscale realistic low-res textures that are compressed by either JPEG or BC1. From my testing, this works rather well on realistic GameCube textures such as the ones from Shrek Extra Large and the board textures from Mario Party 4. This model could also work on some real life images, especially the ones that are taken outdoors."
+  author: "Mutin Choler"
+  source: "[upscale.wiki Model Database](https://upscale.wiki/wiki/Model_Database)"
+  license: "[WTFPL](http://www.wtfpl.net/)"
+- name: "Pixel Perfect V4 4x"
+  type: esrgan_old
+  file: "./torch_models/4x_PixelPerfectV4_137000_G.pth"
+  sha256: 52bcda86effc93e07d868036f53dd2f4a38016cdd636b66ebbf6c4525223eb5f
+  scale: 4
+  description: "Sprite Upscaler"
+  author: "Mutin Choler"
+  source: "[upscale.wiki Model Database](https://upscale.wiki/wiki/Model_Database)"
+  license: "[WTFPL](http://www.wtfpl.net/)"
+- name: "Anime Undeint"
+  type: real_esrgan_compact
+  file: "./torch_models/1x_AnimeUndeint_Compact_130k_net_g.pth"
+  sha256: f8b2b98c4e5dd3989b836414e2624e0795d74a8eba44e32cba6d18cce181140c
+  scale: 1
+  description: "This model corrects jagged lines on animation that has been deinterlaced. It handles simple line doubling, line interpolation, and even Yadif-style artifacts. It can also handle sources that were resized after deinterlacing, for example resizing from ntsc to pal resolutions. If a source has been upscaled after deinterlacing, it will need to be downsized before applying this model."
+  author: "Zarxrax"
+  source: "[upscale.wiki Model Database](https://upscale.wiki/wiki/Model_Database)"
+  license: "[WTFPL](http://www.wtfpl.net/)"
+- name: "Fabric 4x"
+  type: esrgan_old
+  file: "./torch_models/4x-Fabric.pth"
+  sha256: 4a4c7924dfa830aed4dfd1126b1a7ee64a6611260edd03da79e3de842e52024a
+  scale: 4
+  description: "This model set upscales fabric or cloth textures (works on cats too!). The Alt model is just an earlier iteration version. It may work better on some images.The images need to be minimally compressed or passed through a decompression model first. It works with DDS compression though."
+  author: "[Kim2091](https://upscale.wiki/wiki/User:Kim2091)"
+  source: "[upscale.wiki Model Database](https://upscale.wiki/wiki/Model_Database)"
+  license: "[CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+- name: "Face Focus 4x"
+  type: esrgan_old
+  file: "./torch_models/4x_face_focus_275k.pth"
+  sha256: a5b0546243a01f98ae63faaa691351be55416bb723271fb4f5147548b8c0b613
+  scale: 4
+  description: "Face De-blur - slightly out of focus / blurred images of faces. It is aimed at faces / hair"
+  author: "[LyonHrt](https://upscale.wiki/wiki/User:LyonHrt"
+  source: "[upscale.wiki Model Database](https://upscale.wiki/wiki/Model_Database)"
+  license: "[CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+- name: "FatePlus lite 4x"
+  type: esrgan_old_lite
+  file: "./torch_models/4x-FatePlus-lite.pth"
+  sha256: 959c8d9c39e57092d02cb90d82ad1a8d9297cd57d59e43c0243bea2c9eae83da
+  scale: 4
+  description: "Anime PSP games, Fate Extra\n\nThis model was trained as a favor to Demon and the Fate Extra community. It leaves a nice grain on the images and upscales lines and details accurately without looking odd. This model works on most anime-style PSP games. Enjoy! It works best on content with dithering and quantization."
+  author: "[Kim2091](https://upscale.wiki/wiki/User:Kim2091)"
+  source: "[upscale.wiki Model Database](https://upscale.wiki/wiki/Model_Database)"
+  license: "[CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+- name: "Remacri Upscaler 4x"
+  type: esrgan_old
+  file: "./torch_models/4x_foolhardy_Remacri.pth"
+  sha256: e1a73bd89c2da1ae494774746398689048b5a892bd9653e146713f9df8bca86a
+  scale: 4
+  description: "A creation of BSRGAN with more details and less smoothing, made by interpolating IRL models such as Siax, Superscale, Superscale Artisoft, Pixel Perfect, etc. This was, things like skin and other details don't become mushy and blurry."
+  author: "Foolhardy"
+  source: "[upscale.wiki Model Database](https://upscale.wiki/wiki/Model_Database)"
+  license: "[CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)"
+- name: "sudo UltraCompact 2x 1.121.175 G"
+  type: real_esrgan_compact
+  file: "./torch_models/sudo_UltraCompact_2x_1.121.175_G.pth"
+  sha256: e53987f0312dee424b4dbd9dce7b2eacbe03fdf1380e44a11f8a4d2ca88c99e3
+  scale: 2
+  features: 64
+  convs: 8
+  cuda: true
+  description: "Realtime animation restauration and doing stuff like deblur and compression artefact removal"
+  author: "sudo"
+  source: "[upscale.wiki Model Database](https://upscale.wiki/wiki/Model_Database)"
+  license: "[CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)"

requirements.txt

@@ -0,0 +1,6 @@
+torch==1.13.1
+torchvision==0.14.1
+basicsr==1.4.2
+coremltools==6.2
+psutil==5.9.0
+chardet==4.0.0