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import os
import numpy as np
from PIL import Image
from basicsr.archs.rrdbnet_arch import RRDBNet
from modules.postprocess.realesrgan_model_arch import SRVGGNetCompact
from modules.upscaler import Upscaler
from modules.shared import opts, device, log
from modules import devices
class UpscalerRealESRGAN(Upscaler):
def __init__(self, dirname):
self.name = "RealESRGAN"
self.user_path = dirname
super().__init__()
self.scalers = self.find_scalers()
self.models = {}
for scaler in self.scalers:
if scaler.name == 'RealESRGAN 2x+':
scaler.model = lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
scaler.scale = 2
elif scaler.name == 'RealESRGAN 4x+ Anime6B':
scaler.model=lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
elif scaler.name == 'RealESRGAN 4x General V3':
scaler.model = lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
elif scaler.name == 'RealESRGAN 4x General WDN V3':
scaler.model = lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
elif scaler.name == 'RealESRGAN AnimeVideo V3':
scaler.model=lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu')
elif scaler.name == 'RealESRGAN 4x+':
scaler.model = lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
else:
log.error(f"Upscaler unrecognized model: type={self.name} model={scaler.name}")
def load_model(self, path): # pylint: disable=unused-argument
pass
def do_upscale(self, img, selected_model):
if not self.enable:
return img
try:
from modules.postprocess.realesrgan_model_arch import RealESRGANer
except Exception:
log.error("Error importing Real-ESRGAN:")
return img
info = self.find_model(selected_model)
if info is None or not os.path.exists(info.local_data_path):
return img
if self.models.get(info.local_data_path, None) is not None:
log.debug(f"Upscaler cached: type={self.name} model={info.local_data_path}")
upsampler=self.models[info.local_data_path]
else:
upsampler = RealESRGANer(
name=info.name,
scale=info.scale,
model_path=info.local_data_path,
model=info.model(),
half=not opts.no_half and not opts.upcast_sampling,
tile=opts.upscaler_tile_size,
tile_pad=opts.upscaler_tile_overlap,
device=device,
)
self.models[info.local_data_path] = upsampler
upsampled = upsampler.enhance(np.array(img), outscale=info.scale)[0]
if opts.upscaler_unload and info.local_data_path in self.models:
del self.models[info.local_data_path]
log.debug(f"Upscaler unloaded: type={self.name} model={selected_model}")
devices.torch_gc(force=True)
image = Image.fromarray(upsampled)
return image
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