68 lines
2.0 KiB
Python
68 lines
2.0 KiB
Python
from basicsr.archs.rrdbnet_arch import RRDBNet
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from basicsr.utils.download_util import load_file_from_url
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from gfpgan import GFPGANer
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from os import path
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from PIL import Image
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from realesrgan import RealESRGANer
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import numpy as np
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denoise_strength = 0.5
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gfpgan_url = 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth'
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resrgan_url = [
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'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth']
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fp32 = True
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model_name = 'RealESRGAN_x4plus'
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netscale = 4
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outscale = 4
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pre_pad = 0
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tile = 0
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tile_pad = 10
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def upscale_resrgan(source_image: Image) -> Image:
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model_path = path.join('weights', model_name + '.pth')
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if not path.isfile(model_path):
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ROOT_DIR = path.dirname(path.abspath(__file__))
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for url in resrgan_url:
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model_path = load_file_from_url(
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url=url, model_dir=path.join(ROOT_DIR, 'weights'), progress=True, file_name=None)
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model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64,
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num_block=23, num_grow_ch=32, scale=4)
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dni_weight = None
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if model_name == 'realesr-general-x4v3' and denoise_strength != 1:
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wdn_model_path = model_path.replace(
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'realesr-general-x4v3', 'realesr-general-wdn-x4v3')
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model_path = [model_path, wdn_model_path]
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dni_weight = [denoise_strength, 1 - denoise_strength]
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upsampler = RealESRGANer(
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scale=netscale,
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model_path=model_path,
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dni_weight=dni_weight,
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model=model,
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tile=tile,
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tile_pad=tile_pad,
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pre_pad=pre_pad,
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half=fp32)
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image = np.array(source_image)
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output, _ = upsampler.enhance(image, outscale=outscale)
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return upscale_gfpgan(image, upsampler)
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def upscale_gfpgan(image, upsampler) -> Image:
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face_enhancer = GFPGANer(
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model_path=gfpgan_url,
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upscale=outscale,
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arch='clean',
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channel_multiplier=2,
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bg_upsampler=upsampler)
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_, _, output = face_enhancer.enhance(image, has_aligned=False, only_center_face=False, paste_back=True)
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return output
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