python-upscaler/rrdbnet.py at main · YgorSecco/python-upscaler · GitHub

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"""Real-ESRGAN generator (RRDBNet), matching the official RealESRGAN_x4plus weights.
Self-contained (no basicsr/realesrgan deps) so the tool stays lean and robust."""
import torch
from torch import nn
from torch.nn import functional as F


class ResidualDenseBlock(nn.Module):
    def __init__(self, nf=64, gc=32):
        super().__init__()
        self.conv1 = nn.Conv2d(nf, gc, 3, 1, 1)
        self.conv2 = nn.Conv2d(nf + gc, gc, 3, 1, 1)
        self.conv3 = nn.Conv2d(nf + 2 * gc, gc, 3, 1, 1)
        self.conv4 = nn.Conv2d(nf + 3 * gc, gc, 3, 1, 1)
        self.conv5 = nn.Conv2d(nf + 4 * gc, nf, 3, 1, 1)
        self.lrelu = nn.LeakyReLU(0.2, inplace=True)

    def forward(self, x):
        x1 = self.lrelu(self.conv1(x))
        x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
        x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
        x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
        x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
        return x5 * 0.2 + x


class RRDB(nn.Module):
    def __init__(self, nf, gc=32):
        super().__init__()
        self.rdb1 = ResidualDenseBlock(nf, gc)
        self.rdb2 = ResidualDenseBlock(nf, gc)
        self.rdb3 = ResidualDenseBlock(nf, gc)

    def forward(self, x):
        out = self.rdb3(self.rdb2(self.rdb1(x)))
        return out * 0.2 + x


class RRDBNet(nn.Module):
    def __init__(self, in_ch=3, out_ch=3, nf=64, nb=23, gc=32):
        super().__init__()
        self.conv_first = nn.Conv2d(in_ch, nf, 3, 1, 1)
        self.body = nn.Sequential(*[RRDB(nf, gc) for _ in range(nb)])
        self.conv_body = nn.Conv2d(nf, nf, 3, 1, 1)
        self.conv_up1 = nn.Conv2d(nf, nf, 3, 1, 1)
        self.conv_up2 = nn.Conv2d(nf, nf, 3, 1, 1)
        self.conv_hr = nn.Conv2d(nf, nf, 3, 1, 1)
        self.conv_last = nn.Conv2d(nf, out_ch, 3, 1, 1)
        self.lrelu = nn.LeakyReLU(0.2, inplace=True)

    def forward(self, x):
        feat = self.conv_first(x)
        feat = feat + self.conv_body(self.body(feat))
        feat = self.lrelu(self.conv_up1(F.interpolate(feat, scale_factor=2, mode="nearest")))
        feat = self.lrelu(self.conv_up2(F.interpolate(feat, scale_factor=2, mode="nearest")))
        return self.conv_last(self.lrelu(self.conv_hr(feat)))