mirror of
https://github.com/TencentARC/GFPGAN.git
synced 2025-05-15 23:00:12 -07:00
update replicate (#248)
* update util * update predict * update predict * update predict * update predict * update predict * update predict * update predict * update predict * merge replicate update
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Xintao
GitHub
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3e27784b1b
commit
7272e45887
@ -80,7 +80,8 @@ class GFPGANer():
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det_model='retinaface_resnet50',
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det_model='retinaface_resnet50',
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save_ext='png',
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save_ext='png',
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use_parse=True,
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use_parse=True,
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device=self.device)
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device=self.device,
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model_rootpath='gfpgan/weights')
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if model_path.startswith('https://'):
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if model_path.startswith('https://'):
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model_path = load_file_from_url(
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model_path = load_file_from_url(
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81
predict.py
81
predict.py
@ -1,5 +1,9 @@
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# flake8: noqa
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# flake8: noqa
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# This file is used for deploying replicate models
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# This file is used for deploying replicate models
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# running: cog predict -i img=@inputs/whole_imgs/10045.png -i version='v1.4' -i scale=2
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# push: cog push r8.im/tencentarc/gfpgan
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# push (backup): cog push r8.im/xinntao/gfpgan
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import os
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import os
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os.system('python setup.py develop')
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os.system('python setup.py develop')
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@ -10,6 +14,7 @@ import shutil
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import tempfile
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import tempfile
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import torch
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import torch
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from basicsr.archs.srvgg_arch import SRVGGNetCompact
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from basicsr.archs.srvgg_arch import SRVGGNetCompact
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from pathlib import Path
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from gfpgan import GFPGANer
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from gfpgan import GFPGANer
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@ -24,33 +29,46 @@ class Predictor(BasePredictor):
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def setup(self):
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def setup(self):
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# download weights
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# download weights
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if not os.path.exists('realesr-general-x4v3.pth'):
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if not os.path.exists('gfpgan/weights/realesr-general-x4v3.pth'):
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os.system(
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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 .')
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'wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth -P ./gfpgan/weights'
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if not os.path.exists('GFPGANv1.2.pth'):
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)
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os.system('wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.2.pth -P .')
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if not os.path.exists('gfpgan/weights/GFPGANv1.2.pth'):
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if not os.path.exists('GFPGANv1.3.pth'):
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os.system(
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os.system('wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P .')
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'wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.2.pth -P ./gfpgan/weights')
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if not os.path.exists('gfpgan/weights/GFPGANv1.3.pth'):
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os.system(
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'wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P ./gfpgan/weights')
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if not os.path.exists('gfpgan/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 ./gfpgan/weights')
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# background enhancer with RealESRGAN
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# background enhancer with RealESRGAN
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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 = 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 = 'realesr-general-x4v3.pth'
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model_path = 'gfpgan/weights/realesr-general-x4v3.pth'
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half = True if torch.cuda.is_available() else False
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half = True if torch.cuda.is_available() else False
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upsampler = RealESRGANer(scale=4, model_path=model_path, model=model, tile=0, tile_pad=10, pre_pad=0, half=half)
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self.upsampler = RealESRGANer(
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scale=4, model_path=model_path, model=model, tile=0, tile_pad=10, pre_pad=0, half=half)
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# Use GFPGAN for face enhancement
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# Use GFPGAN for face enhancement
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self.face_enhancer_v3 = GFPGANer(
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self.face_enhancer = GFPGANer(
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model_path='GFPGANv1.3.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
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model_path='gfpgan/weights/GFPGANv1.4.pth',
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self.face_enhancer_v2 = GFPGANer(
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upscale=2,
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model_path='GFPGANv1.2.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
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arch='clean',
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os.makedirs('output', exist_ok=True)
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channel_multiplier=2,
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bg_upsampler=self.upsampler)
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self.current_version = 'v1.4'
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def predict(
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def predict(
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self,
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self,
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img: Path = Input(description='Input'),
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img: Path = Input(description='Input'),
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version: str = Input(description='GFPGAN version', choices=['v1.2', 'v1.3'], default='v1.3'),
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version: str = Input(
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description='GFPGAN version. v1.3: better quality. v1.4: more details and better identity.',
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choices=['v1.2', 'v1.3', 'v1.4'],
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default='v1.4'),
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scale: float = Input(description='Rescaling factor', default=2)
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scale: float = Input(description='Rescaling factor', default=2)
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) -> Path:
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) -> Path:
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print(img, version, scale)
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try:
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try:
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img = cv2.imread(str(img), cv2.IMREAD_UNCHANGED)
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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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if len(img.shape) == 3 and img.shape[2] == 4:
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@ -62,12 +80,35 @@ class Predictor(BasePredictor):
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if h < 300:
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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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img = cv2.resize(img, (w * 2, h * 2), interpolation=cv2.INTER_LANCZOS4)
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if version == 'v1.2':
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if self.current_version != version:
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face_enhancer = self.face_enhancer_v2
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if version == 'v1.2':
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else:
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self.face_enhancer = GFPGANer(
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face_enhancer = self.face_enhancer_v3
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model_path='gfpgan/weights/GFPGANv1.2.pth',
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upscale=2,
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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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self.current_version = 'v1.2'
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elif version == 'v1.3':
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self.face_enhancer = GFPGANer(
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model_path='gfpgan/weights/GFPGANv1.3.pth',
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upscale=2,
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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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self.current_version = 'v1.3'
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elif version == 'v1.4':
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self.face_enhancer = GFPGANer(
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model_path='gfpgan/weights/GFPGANv1.4.pth',
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upscale=2,
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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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self.current_version = 'v1.4'
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try:
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try:
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_, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
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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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except RuntimeError as error:
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except RuntimeError as error:
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print('Error', error)
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print('Error', error)
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else:
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else:
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@ -86,7 +127,7 @@ class Predictor(BasePredictor):
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extension = 'jpg'
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extension = 'jpg'
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save_path = f'output/out.{extension}'
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save_path = f'output/out.{extension}'
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cv2.imwrite(save_path, output)
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cv2.imwrite(save_path, output)
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out_path = os.path.join(tempfile.mkdtemp(), 'output.png')
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out_path = Path(tempfile.mkdtemp()) / 'output.png'
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cv2.imwrite(str(out_path), output)
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cv2.imwrite(str(out_path), output)
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except Exception as error:
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except Exception as error:
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print('global exception', error)
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print('global exception', error)
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