133 lines
5.6 KiB
Python
133 lines
5.6 KiB
Python
import os
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import numpy as np
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from PIL import Image
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from realesrgan import RealESRGANer
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from modules.upscaler import Upscaler, UpscalerData
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from modules.shared import cmd_opts, opts
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from modules import modelloader, errors
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class UpscalerRealESRGAN(Upscaler):
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def __init__(self, path):
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self.name = "RealESRGAN"
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self.user_path = path
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super().__init__()
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try:
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from basicsr.archs.rrdbnet_arch import RRDBNet # noqa: F401
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from realesrgan import RealESRGANer # noqa: F401
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from realesrgan.archs.srvgg_arch import SRVGGNetCompact # noqa: F401
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self.enable = True
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self.scalers = []
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scalers = self.load_models(path)
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local_model_paths = self.find_models(ext_filter=[".pth"])
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for scaler in scalers:
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if scaler.local_data_path.startswith("http"):
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filename = modelloader.friendly_name(scaler.local_data_path)
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local_model_candidates = [local_model for local_model in local_model_paths if local_model.endswith(f"{filename}.pth")]
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if local_model_candidates:
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scaler.local_data_path = local_model_candidates[0]
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if scaler.name in opts.realesrgan_enabled_models:
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self.scalers.append(scaler)
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except Exception:
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errors.report("Error importing Real-ESRGAN", exc_info=True)
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self.enable = False
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self.scalers = []
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def do_upscale(self, img, path):
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if not self.enable:
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return img
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try:
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info = self.load_model(path)
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except Exception:
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errors.report(f"Unable to load RealESRGAN model {path}", exc_info=True)
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return img
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upsampler = RealESRGANer(
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scale=info.scale,
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model_path=info.local_data_path,
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model=info.model(),
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half=not cmd_opts.no_half and not cmd_opts.upcast_sampling,
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tile=opts.ESRGAN_tile,
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tile_pad=opts.ESRGAN_tile_overlap,
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)
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upsampled = upsampler.enhance(np.array(img), outscale=info.scale)[0]
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image = Image.fromarray(upsampled)
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return image
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def load_model(self, path):
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for scaler in self.scalers:
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if scaler.data_path == path:
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if scaler.local_data_path.startswith("http"):
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scaler.local_data_path = modelloader.load_file_from_url(
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scaler.data_path,
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model_dir=self.model_download_path,
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)
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if not os.path.exists(scaler.local_data_path):
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raise FileNotFoundError(f"RealESRGAN data missing: {scaler.local_data_path}")
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return scaler
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raise ValueError(f"Unable to find model info: {path}")
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def load_models(self, _):
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return get_realesrgan_models(self)
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def get_realesrgan_models(scaler):
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try:
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from basicsr.archs.rrdbnet_arch import RRDBNet
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from realesrgan.archs.srvgg_arch import SRVGGNetCompact
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models = [
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UpscalerData(
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name="R-ESRGAN General 4xV3",
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path="https://ghproxy.com/https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth",
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scale=4,
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upscaler=scaler,
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model=lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
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),
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UpscalerData(
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name="R-ESRGAN General WDN 4xV3",
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path="https://ghproxy.com/https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth",
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scale=4,
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upscaler=scaler,
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model=lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
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),
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UpscalerData(
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name="R-ESRGAN AnimeVideo",
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path="https://ghproxy.com/https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth",
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scale=4,
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upscaler=scaler,
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model=lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu')
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),
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UpscalerData(
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name="R-ESRGAN 4x+",
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path="https://ghproxy.com/https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",
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scale=4,
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upscaler=scaler,
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model=lambda: 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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),
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UpscalerData(
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name="R-ESRGAN 4x+ Anime6B",
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path="https://ghproxy.com/https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth",
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scale=4,
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upscaler=scaler,
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model=lambda: 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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),
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UpscalerData(
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name="R-ESRGAN 2x+",
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path="https://ghproxy.com/https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth",
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scale=2,
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upscaler=scaler,
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model=lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
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),
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]
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return models
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except Exception:
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errors.report("Error making Real-ESRGAN models list", exc_info=True)
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