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import os
import torch
from basicsr.utils.download_util import load_file_from_url
from basicsr.archs.rrdbnet_arch import RRDBNet
from basicsr.archs.srvgg_arch import SRVGGNetCompact
from gfpgan.utils import GFPGANer
from realesrgan.utils import RealESRGANer
from config import *
from srcnn import SRCNN
def get_upsampler(model_name, device=None):
if model_name == "RealESRGAN_x4plus": # x4 RRDBNet model
model = RRDBNet(
num_in_ch=3,
num_out_ch=3,
num_feat=64,
num_block=23,
num_grow_ch=32,
scale=4,
)
netscale = 4
file_url = [
"https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth"
]
elif model_name == "RealESRNet_x4plus": # x4 RRDBNet model
model = RRDBNet(
num_in_ch=3,
num_out_ch=3,
num_feat=64,
num_block=23,
num_grow_ch=32,
scale=4,
)
netscale = 4
file_url = [
"https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth"
]
elif model_name == "RealESRGAN_x4plus_anime_6B": # x4 RRDBNet model with 6 blocks
model = RRDBNet(
num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4
)
netscale = 4
file_url = [
"https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth"
]
elif model_name == "RealESRGAN_x2plus": # x2 RRDBNet model
model = RRDBNet(
num_in_ch=3,
num_out_ch=3,
num_feat=64,
num_block=23,
num_grow_ch=32,
scale=2,
)
netscale = 2
file_url = [
"https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth"
]
elif model_name == "realesr-animevideov3": # x4 VGG-style model (XS size)
model = SRVGGNetCompact(
num_in_ch=3,
num_out_ch=3,
num_feat=64,
num_conv=16,
upscale=4,
act_type="prelu",
)
netscale = 4
file_url = [
"https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth"
]
elif model_name == "realesr-general-x4v3": # x4 VGG-style model (S size)
model = SRVGGNetCompact(
num_in_ch=3,
num_out_ch=3,
num_feat=64,
num_conv=32,
upscale=4,
act_type="prelu",
)
netscale = 4
file_url = [
"https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth",
"https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth",
]
elif model_name == "srcnn":
model = SRCNN(device=device)
model_path = os.path.join(ROOT_DIR, WEIGHT_DIR, model_name + ".pth")
model.load_state_dict(torch.load(model_path, map_location=torch.device("cpu")))
if device:
model.to(device)
return model
else:
raise ValueError(f"Wrong model version {model_name}.")
model_path = os.path.join(ROOT_DIR, WEIGHT_DIR, model_name + ".pth")
if not os.path.exists(model_path):
print(f"Downloading weights for model {model_name}")
for url in file_url:
# model_path will be updated
model_path = load_file_from_url(
url=url,
model_dir=os.path.join(ROOT_DIR, WEIGHT_DIR),
progress=True,
file_name=None,
)
if model_name != "realesr-general-x4v3":
dni_weight = None
else:
dni_weight = [0.5, 0.5]
wdn_model_path = model_path.replace(
"realesr-general-x4v3", "realesr-general-wdn-x4v3"
)
model_path = [model_path, wdn_model_path]
half = "cuda" in str(device)
return RealESRGANer(
scale=netscale,
model_path=model_path,
dni_weight=dni_weight,
model=model,
half=half,
device=device,
)
def get_face_enhancer(model_name, upscale=2, bg_upsampler=None, device=None):
if model_name == "GFPGANv1.3":
arch = "clean"
channel_multiplier = 2
file_url = "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth"
elif model_name == "GFPGANv1.4":
arch = "clean"
channel_multiplier = 2
file_url = "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth"
elif model_name == "RestoreFormer":
arch = "RestoreFormer"
channel_multiplier = 2
file_url = "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/RestoreFormer.pth"
else:
raise ValueError(f"Wrong model version {model_name}.")
model_path = os.path.join(ROOT_DIR, WEIGHT_DIR, model_name + ".pth")
if not os.path.exists(model_path):
print(f"Downloading weights for model {model_name}")
model_path = load_file_from_url(
url=file_url,
model_dir=os.path.join(ROOT_DIR, WEIGHT_DIR),
progress=True,
file_name=None,
)
return GFPGANer(
model_path=model_path,
upscale=upscale,
arch=arch,
channel_multiplier=channel_multiplier,
bg_upsampler=bg_upsampler,
device=device,
)
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