Add Replicate demo (#428)
* add cog.yaml * add cog predict * add cog predict * update cog predict * update cog predict * add alpha png * update cog predict * update cog predict * update cog predict * update readme * fix codespell
This commit is contained in:
Xintao
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🔥 **AnimeVideo-v3 model (动漫视频小模型)**. Please see [[*anime video models*](docs/anime_video_model.md)] and [[*comparisons*](docs/anime_comparisons.md)]<br>
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🔥 **RealESRGAN_x4plus_anime_6B** for anime images **(动漫插图模型)**. Please see [[*anime_model*](docs/anime_model.md)]
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1. You can try in our website: [ARC Demo](https://arc.tencent.com/en/ai-demos/imgRestore) (now only support RealESRGAN_x4plus_anime_6B)
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<!-- 1. You can try in our website: [ARC Demo](https://arc.tencent.com/en/ai-demos/imgRestore) (now only support RealESRGAN_x4plus_anime_6B) -->
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1. :boom: **Add** online demo: [](https://replicate.com/xinntao/realesrgan).
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1. [Colab Demo](https://colab.research.google.com/drive/1k2Zod6kSHEvraybHl50Lys0LerhyTMCo?usp=sharing) for Real-ESRGAN **|** [Colab Demo](https://colab.research.google.com/drive/1yNl9ORUxxlL4N0keJa2SEPB61imPQd1B?usp=sharing) for Real-ESRGAN (**anime videos**)
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1. Portable [Windows](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesrgan-ncnn-vulkan-20220424-windows.zip) / [Linux](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesrgan-ncnn-vulkan-20220424-ubuntu.zip) / [MacOS](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesrgan-ncnn-vulkan-20220424-macos.zip) **executable files for Intel/AMD/Nvidia GPU**. You can find more information [here](#portable-executable-files-ncnn). The ncnn implementation is in [Real-ESRGAN-ncnn-vulkan](https://github.com/xinntao/Real-ESRGAN-ncnn-vulkan)
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1. You can watch enhanced animations in [Tencent Video](https://v.qq.com/s/topic/v_child/render/fC4iyCAM.html). 欢迎观看[腾讯视频动漫修复](https://v.qq.com/s/topic/v_child/render/fC4iyCAM.html)
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22
cog.yaml
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22
cog.yaml
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# This file is used for constructing replicate env
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image: "r8.im/tencentarc/realesrgan"
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build:
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gpu: true
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python_version: "3.8"
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system_packages:
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- "libgl1-mesa-glx"
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- "libglib2.0-0"
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python_packages:
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- "torch==1.7.1"
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- "torchvision==0.8.2"
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- "numpy==1.21.1"
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- "lmdb==1.2.1"
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- "opencv-python==4.5.3.56"
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- "PyYAML==5.4.1"
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- "tqdm==4.62.2"
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- "yapf==0.31.0"
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- "basicsr==1.4.2"
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- "facexlib==0.2.5"
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predict: "cog_predict.py:Predictor"
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150
cog_predict.py
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cog_predict.py
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# flake8: noqa
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# This file is used for deploying replicate models
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# running: cog predict -i img=@inputs/00017_gray.png -i version='General - v3' -i scale=2 -i face_enhance=True -i tile=0
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# push: cog push r8.im/xinntao/realesrgan
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import os
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os.system('pip install gfpgan')
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os.system('python setup.py develop')
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import cv2
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import shutil
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import tempfile
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import torch
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from basicsr.archs.rrdbnet_arch import RRDBNet
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from basicsr.archs.srvgg_arch import SRVGGNetCompact
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from realesrgan.utils import RealESRGANer
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try:
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from cog import BasePredictor, Input, Path
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from gfpgan import GFPGANer
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except Exception:
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print('please install cog and realesrgan package')
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class Predictor(BasePredictor):
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def setup(self):
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os.makedirs('output', exist_ok=True)
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# download weights
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if not os.path.exists('realesrgan/weights/realesr-general-x4v3.pth'):
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os.system(
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'wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth -P ./realesrgan/weights'
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)
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if not os.path.exists('realesrgan/weights/GFPGANv1.4.pth'):
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os.system(
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'wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth -P ./realesrgan/weights'
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)
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if not os.path.exists('realesrgan/weights/RealESRGAN_x4plus.pth'):
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os.system(
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'wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P ./realesrgan/weights'
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)
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if not os.path.exists('realesrgan/weights/RealESRGAN_x4plus_anime_6B.pth'):
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os.system(
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'wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth -P ./realesrgan/weights'
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)
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if not os.path.exists('realesrgan/weights/realesr-animevideov3.pth'):
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os.system(
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'wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth -P ./realesrgan/weights'
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)
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def choose_model(self, scale, version, tile=0):
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half = True if torch.cuda.is_available() else False
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if version == 'General - RealESRGANplus':
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model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
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model_path = 'realesrgan/weights/RealESRGAN_x4plus.pth'
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self.upsampler = RealESRGANer(
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scale=4, model_path=model_path, model=model, tile=tile, tile_pad=10, pre_pad=0, half=half)
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elif version == 'General - v3':
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model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
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model_path = 'realesrgan/weights/realesr-general-x4v3.pth'
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self.upsampler = RealESRGANer(
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scale=4, model_path=model_path, model=model, tile=tile, tile_pad=10, pre_pad=0, half=half)
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elif version == 'Anime - anime6B':
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model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
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model_path = 'realesrgan/weights/RealESRGAN_x4plus_anime_6B.pth'
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self.upsampler = RealESRGANer(
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scale=4, model_path=model_path, model=model, tile=tile, tile_pad=10, pre_pad=0, half=half)
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elif version == 'AnimeVideo - v3':
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model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu')
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model_path = 'realesrgan/weights/realesr-animevideov3.pth'
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self.upsampler = RealESRGANer(
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scale=4, model_path=model_path, model=model, tile=tile, tile_pad=10, pre_pad=0, half=half)
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self.face_enhancer = GFPGANer(
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model_path='realesrgan/weights/GFPGANv1.4.pth',
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upscale=scale,
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arch='clean',
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channel_multiplier=2,
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bg_upsampler=self.upsampler)
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def predict(
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self,
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img: Path = Input(description='Input'),
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version: str = Input(
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description='RealESRGAN version. Please see [Readme] below for more descriptions',
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choices=['General - RealESRGANplus', 'General - v3', 'Anime - anime6B', 'AnimeVideo - v3'],
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default='General - v3'),
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scale: float = Input(description='Rescaling factor', default=2),
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face_enhance: bool = Input(
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description='Enhance faces with GFPGAN. Note that it does not work for anime images/vidoes', default=False),
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tile: int = Input(
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description=
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'Tile size. Default is 0, that is no tile. When encountering the out-of-GPU-memory issue, please specify it, e.g., 400 or 200',
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default=0)
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) -> Path:
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if tile <= 100 or tile is None:
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tile = 0
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print(f'img: {img}. version: {version}. scale: {scale}. face_enhance: {face_enhance}. tile: {tile}.')
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try:
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extension = os.path.splitext(os.path.basename(str(img)))[1]
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img = cv2.imread(str(img), cv2.IMREAD_UNCHANGED)
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if len(img.shape) == 3 and img.shape[2] == 4:
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img_mode = 'RGBA'
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elif len(img.shape) == 2:
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img_mode = None
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img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
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else:
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img_mode = None
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h, w = img.shape[0:2]
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if h < 300:
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img = cv2.resize(img, (w * 2, h * 2), interpolation=cv2.INTER_LANCZOS4)
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self.choose_model(scale, version, tile)
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try:
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if face_enhance:
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_, _, output = self.face_enhancer.enhance(
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img, has_aligned=False, only_center_face=False, paste_back=True)
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else:
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output, _ = self.upsampler.enhance(img, outscale=scale)
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except RuntimeError as error:
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print('Error', error)
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print('If you encounter CUDA out of memory, try to set "tile" to a smaller size, e.g., 400.')
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if img_mode == 'RGBA': # RGBA images should be saved in png format
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extension = 'png'
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# save_path = f'output/out.{extension}'
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# cv2.imwrite(save_path, output)
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out_path = Path(tempfile.mkdtemp()) / f'out.{extension}'
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cv2.imwrite(str(out_path), output)
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except Exception as error:
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print('global exception: ', error)
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finally:
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clean_folder('output')
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return out_path
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def clean_folder(folder):
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for filename in os.listdir(folder):
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file_path = os.path.join(folder, filename)
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try:
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if os.path.isfile(file_path) or os.path.islink(file_path):
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os.unlink(file_path)
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elif os.path.isdir(file_path):
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shutil.rmtree(file_path)
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except Exception as e:
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print(f'Failed to delete {file_path}. Reason: {e}')
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@@ -166,7 +166,7 @@ You can finetune Real-ESRGAN on your own dataset. Typically, the fine-tuning pro
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### Generate degraded images on the fly
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Only high-resolution images are required. The low-quality images are generated with the degradation process described in Real-ESRGAN during trainig.
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Only high-resolution images are required. The low-quality images are generated with the degradation process described in Real-ESRGAN during training.
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**1. Prepare dataset**
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BIN
inputs/00017_gray.png
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BIN
inputs/00017_gray.png
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Binary file not shown.
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After Width: | Height: | Size: 68 KiB |
BIN
inputs/children-alpha.png
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inputs/children-alpha.png
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After Width: | Height: | Size: 268 KiB |
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basicsr>=1.3.3.11
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facexlib>=0.2.0.3
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gfpgan>=0.2.1
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basicsr>=1.4.2
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facexlib>=0.2.5
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gfpgan>=1.3.5
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numpy
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opencv-python
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Pillow
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