Initial commit

.gitattributes

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+# Auto detect text files and perform LF normalization
+* text=auto

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
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+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
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+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
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+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
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+.coverage
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+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
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+
+# Django stuff:
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+
+# pyenv
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+#   intended to run in multiple environments; otherwise, check them in:
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+__pypackages__/
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+# Cython debug symbols
+cython_debug/

README.md

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+# Real-ESRGAN
+Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data
+
+Ported from https://github.com/xinntao/Real-ESRGAN
+
+
+## Dependencies
+- [NumPy](https://numpy.org/install)
+- [PyTorch](https://pytorch.org/get-started), preferably with CUDA. Note that `torchvision` and `torchaudio` are not required and hence can be omitted from the command.
+- [VapourSynth](http://www.vapoursynth.com/)
+
+
+## Installation
+```
+pip install --upgrade vsrealesrgan
+python -m vsrealesrgan
+```
+
+
+## Usage
+```python
+from vsrealesrgan import RealESRGAN
+
+ret = RealESRGAN(clip)
+```
+
+See `__init__.py` for the description of the parameters.

pyproject.toml

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+[build-system]
+requires = ["setuptools", "wheel"]
+build-backend = "setuptools.build_meta"

setup.cfg

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+[metadata]
+name = vsrealesrgan
+version = 1.0.0
+author = HolyWu
+description = RealESRGAN function for VapourSynth
+long_description = file: README.md
+long_description_content_type = text/markdown
+url = https://github.com/HolyWu/vs-realesrgan
+classifiers =
+    License :: OSI Approved :: BSD License
+    Operating System :: OS Independent
+    Programming Language :: Python :: 3
+    Programming Language :: Python :: 3 :: Only
+    Topic :: Multimedia :: Video
+
+[options]
+zip_safe = False
+packages = find:
+python_requires = >=3.6
+install_requires =
+    numpy
+    requests
+    torch
+    tqdm
+
+[options.package_data]
+* = *.pth

vsrealesrgan/__init__.py

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+import numpy as np
+import os
+import torch
+import vapoursynth as vs
+from .utils import RealESRGANer
+
+
+def RealESRGAN(clip: vs.VideoNode, scale: int=2, tile: int=0, tile_pad: int=10, pre_pad: int=0, half: bool=False, device_type: str='cuda', device_index: int=0) -> vs.VideoNode:
+    '''
+    Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data
+
+    Parameters:
+        clip: Clip to process. Only planar format with float sample type of 32 bit depth is supported.
+
+        scale: Upsample scale factor of the network. Must be 2 or 4.
+
+        tile: Tile size, 0 for no tile.
+
+        tile_pad: Tile padding.
+
+        pre_pad: Pre padding size at each border.
+
+        half: Use half precision.
+        
+        device_type: Device type on which the tensor is allocated. Must be 'cuda' or 'cpu'.
+        
+        device_index: Device ordinal for the device type.
+    '''
+    if not isinstance(clip, vs.VideoNode):
+        raise vs.Error('RealESRGAN: this is not a clip')
+
+    if clip.format.id != vs.RGBS:
+        raise vs.Error('RealESRGAN: only RGBS format is supported')
+
+    if scale not in [2, 4]:
+        raise vs.Error('RealESRGAN: scale must be 2 or 4')
+
+    device_type = device_type.lower()
+
+    if device_type not in ['cuda', 'cpu']:
+        raise vs.Error("RealESRGAN: device_type must be 'cuda' or 'cpu'")
+
+    if device_type == 'cuda' and not torch.cuda.is_available():
+        raise vs.Error('RealESRGAN: CUDA is not available')
+
+    device = torch.device(device_type, device_index)
+    if device_type == 'cuda':
+        torch.backends.cudnn.enabled = True
+        torch.backends.cudnn.benchmark = True
+
+    model_path = os.path.join(os.path.dirname(__file__), f'RealESRGAN_x{scale}plus.pth')
+
+    upsampler = RealESRGANer(device, scale, model_path, tile, tile_pad, pre_pad, half)
+
+    new_clip = clip.std.BlankClip(width=clip.width * scale, height=clip.height * scale)
+
+    def realesrgan(n: int, f: vs.VideoFrame) -> vs.VideoFrame:
+        img = frame_to_tensor(f[0])
+        img = upsampler.enhance(img)
+        return tensor_to_frame(img, f[1])
+
+    return new_clip.std.ModifyFrame(clips=[clip, new_clip], selector=realesrgan)
+
+
+def frame_to_tensor(f: vs.VideoFrame) -> torch.Tensor:
+    arr = np.stack([np.asarray(f.get_read_array(plane)) for plane in range(f.format.num_planes)])
+    return torch.from_numpy(arr).unsqueeze(0)
+
+
+def tensor_to_frame(t: torch.Tensor, f: vs.VideoFrame) -> vs.VideoFrame:
+    arr = t.data.squeeze().cpu().numpy()
+    fout = f.copy()
+    for plane in range(fout.format.num_planes):
+        np.copyto(np.asarray(fout.get_write_array(plane)), arr[plane, :, :])
+    return fout

vsrealesrgan/__main__.py

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+import os.path
+import requests
+from tqdm import tqdm
+
+def download_model(url: str) -> None:
+    filename = url.split('/')[-1]
+    r = requests.get(url, stream=True)
+    with open(os.path.join(os.path.dirname(__file__), filename), 'wb') as f:
+        with tqdm(unit='B', unit_scale=True, unit_divisor=1024, miniters=1, desc=filename, total=int(r.headers.get('content-length', 0))) as pbar:
+            for chunk in r.iter_content(chunk_size=4096):
+                f.write(chunk)
+                pbar.update(len(chunk))
+
+if __name__ == '__main__':
+    download_model('https://github.com/HolyWu/vs-realesrgan/releases/download/model/RealESRGAN_x2plus.pth')
+    download_model('https://github.com/HolyWu/vs-realesrgan/releases/download/model/RealESRGAN_x4plus.pth')