mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2025-08-04 03:10:21 +00:00
Holy $hit.
Yep. Fix gfpgan_model_arch requirement(s). Add Upscaler base class, move from images. Add a lot of methods to Upscaler. Re-work all the child upscalers to be proper classes. Add BSRGAN scaler. Add ldsr_model_arch class, removing the dependency for another repo that just uses regular latent-diffusion stuff. Add one universal method that will always find and load new upscaler models without having to add new "setup_model" calls. Still need to add command line params, but that could probably be automated. Add a "self.scale" property to all Upscalers so the scalers themselves can do "things" in response to the requested upscaling size. Ensure LDSR doesn't get stuck in a longer loop of "upscale/downscale/upscale" as we try to reach the target upscale size. Add typehints for IDE sanity. PEP-8 improvements. Moar.
This commit is contained in:
d8ahazard
@@ -1,64 +1,135 @@
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import os
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import sys
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import traceback
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from collections import namedtuple
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import numpy as np
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from PIL import Image
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from basicsr.utils.download_util import load_file_from_url
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from realesrgan import RealESRGANer
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import modules.images
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from modules.upscaler import Upscaler, UpscalerData
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from modules.paths import models_path
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from modules.shared import cmd_opts, opts
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model_dir = "RealESRGAN"
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model_path = os.path.join(models_path, model_dir)
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cmd_dir = None
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RealesrganModelInfo = namedtuple("RealesrganModelInfo", ["name", "location", "model", "netscale"])
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realesrgan_models = []
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have_realesrgan = False
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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.model_path = os.path.join(models_path, self.name)
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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
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from realesrgan import RealESRGANer
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from realesrgan.archs.srvgg_arch import SRVGGNetCompact
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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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for scaler in scalers:
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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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print("Error importing Real-ESRGAN:", file=sys.stderr)
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print(traceback.format_exc(), file=sys.stderr)
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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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info = self.load_model(path)
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if not os.path.exists(info.data_path):
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print("Unable to load RealESRGAN model: %s" % info.name)
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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.data_path,
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model=info.model(),
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half=not cmd_opts.no_half,
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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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try:
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info = None
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for scaler in self.scalers:
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if scaler.data_path == path:
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info = scaler
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if info is None:
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print(f"Unable to find model info: {path}")
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return None
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model_file = load_file_from_url(url=info.data_path, model_dir=self.model_path, progress=True)
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info.data_path = model_file
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return info
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except Exception as e:
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print(f"Error making Real-ESRGAN models list: {e}", file=sys.stderr)
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print(traceback.format_exc(), file=sys.stderr)
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return None
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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():
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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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RealesrganModelInfo(
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name="Real-ESRGAN General x4x3",
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location="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth",
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netscale=4,
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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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UpscalerData(
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name="R-ESRGAN General 4xV3",
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path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3"
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".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,
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act_type='prelu')
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),
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RealesrganModelInfo(
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name="Real-ESRGAN General WDN x4x3",
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location="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth",
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netscale=4,
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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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UpscalerData(
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name="R-ESRGAN General WDN 4xV3",
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path="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,
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act_type='prelu')
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),
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RealesrganModelInfo(
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name="Real-ESRGAN AnimeVideo",
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location="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth",
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netscale=4,
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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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UpscalerData(
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name="R-ESRGAN AnimeVideo",
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path="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,
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act_type='prelu')
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),
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RealesrganModelInfo(
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name="Real-ESRGAN 4x plus",
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location="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",
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netscale=4,
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UpscalerData(
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name="R-ESRGAN 4x+",
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path="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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RealesrganModelInfo(
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name="Real-ESRGAN 4x plus anime 6B",
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location="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth",
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netscale=4,
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UpscalerData(
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name="R-ESRGAN 4x+ Anime6B",
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path="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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RealesrganModelInfo(
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name="Real-ESRGAN 2x plus",
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location="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth",
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netscale=2,
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UpscalerData(
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name="R-ESRGAN 2x+",
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path="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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@@ -66,69 +137,3 @@ def get_realesrgan_models():
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except Exception as e:
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print("Error making Real-ESRGAN models list:", file=sys.stderr)
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print(traceback.format_exc(), file=sys.stderr)
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class UpscalerRealESRGAN(modules.images.Upscaler):
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def __init__(self, upscaling, model_index):
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self.upscaling = upscaling
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self.model_index = model_index
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self.name = realesrgan_models[model_index].name
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def do_upscale(self, img):
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return upscale_with_realesrgan(img, self.upscaling, self.model_index)
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def setup_model(dirname):
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global model_path
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if not os.path.exists(model_path):
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os.makedirs(model_path)
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global realesrgan_models
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global have_realesrgan
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if model_path != dirname:
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model_path = dirname
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try:
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from basicsr.archs.rrdbnet_arch import RRDBNet
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from realesrgan import RealESRGANer
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from realesrgan.archs.srvgg_arch import SRVGGNetCompact
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realesrgan_models = get_realesrgan_models()
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have_realesrgan = True
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for i, model in enumerate(realesrgan_models):
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if model.name in opts.realesrgan_enabled_models:
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modules.shared.sd_upscalers.append(UpscalerRealESRGAN(model.netscale, i))
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except Exception:
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print("Error importing Real-ESRGAN:", file=sys.stderr)
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print(traceback.format_exc(), file=sys.stderr)
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realesrgan_models = [RealesrganModelInfo('None', '', 0, None)]
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have_realesrgan = False
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def upscale_with_realesrgan(image, RealESRGAN_upscaling, RealESRGAN_model_index):
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if not have_realesrgan:
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return image
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info = realesrgan_models[RealESRGAN_model_index]
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model = info.model()
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model_file = load_file_from_url(url=info.location, model_dir=model_path, progress=True)
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if not os.path.exists(model_file):
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print("Unable to load RealESRGAN model: %s" % info.name)
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return image
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upsampler = RealESRGANer(
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scale=info.netscale,
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model_path=info.location,
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model=model,
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half=not cmd_opts.no_half,
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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(image), outscale=RealESRGAN_upscaling)[0]
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image = Image.fromarray(upsampled)
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return image
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