16 files changed, 1009 insertions, 639 deletions
diff --git a/modules/bsrgan_model.py b/modules/bsrgan_model.py
new file mode 100644
index 00000000..77141545
--- /dev/null
+++ b/modules/bsrgan_model.py
@@ -0,0 +1,79 @@
+import os.path
+import sys
+import traceback
+
+import PIL.Image
+import numpy as np
+import torch
+from basicsr.utils.download_util import load_file_from_url
+
+import modules.upscaler
+from modules import shared, modelloader
+from modules.bsrgan_model_arch import RRDBNet
+from modules.paths import models_path
+
+
+class UpscalerBSRGAN(modules.upscaler.Upscaler):
+    def __init__(self, dirname):
+        self.name = "BSRGAN"
+        self.model_path = os.path.join(models_path, self.name)
+        self.model_name = "BSRGAN 4x"
+        self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/BSRGAN.pth"
+        self.user_path = dirname
+        super().__init__()
+        model_paths = self.find_models(ext_filter=[".pt", ".pth"])
+        scalers = []
+        if len(model_paths) == 0:
+            scaler_data = modules.upscaler.UpscalerData(self.model_name, self.model_url, self, 4)
+            scalers.append(scaler_data)
+        for file in model_paths:
+            if "http" in file:
+                name = self.model_name
+            else:
+                name = modelloader.friendly_name(file)
+            try:
+                scaler_data = modules.upscaler.UpscalerData(name, file, self, 4)
+                scalers.append(scaler_data)
+            except Exception:
+                print(f"Error loading BSRGAN model: {file}", file=sys.stderr)
+                print(traceback.format_exc(), file=sys.stderr)
+        self.scalers = scalers
+
+    def do_upscale(self, img: PIL.Image, selected_file):
+        torch.cuda.empty_cache()
+        model = self.load_model(selected_file)
+        if model is None:
+            return img
+        model.to(shared.device)
+        torch.cuda.empty_cache()
+        img = np.array(img)
+        img = img[:, :, ::-1]
+        img = np.moveaxis(img, 2, 0) / 255
+        img = torch.from_numpy(img).float()
+        img = img.unsqueeze(0).to(shared.device)
+        with torch.no_grad():
+            output = model(img)
+        output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
+        output = 255. * np.moveaxis(output, 0, 2)
+        output = output.astype(np.uint8)
+        output = output[:, :, ::-1]
+        torch.cuda.empty_cache()
+        return PIL.Image.fromarray(output, 'RGB')
+
+    def load_model(self, path: str):
+        if "http" in path:
+            filename = load_file_from_url(url=self.model_url, model_dir=self.model_path, file_name="%s.pth" % self.name,
+                                          progress=True)
+        else:
+            filename = path
+        if not os.path.exists(filename) or filename is None:
+            print("Unable to load %s from %s" % (self.model_dir, filename))
+            return None
+        print("Loading %s from %s" % (self.model_dir, filename))
+        model = RRDBNet(in_nc=3, out_nc=3, nf=64, nb=23, gc=32, sf=2)  # define network
+        model.load_state_dict(torch.load(filename), strict=True)
+        model.eval()
+        for k, v in model.named_parameters():
+            v.requires_grad = False
+        return model
+
diff --git a/modules/bsrgan_model_arch.py b/modules/bsrgan_model_arch.py
new file mode 100644
index 00000000..d72647db
--- /dev/null
+++ b/modules/bsrgan_model_arch.py
@@ -0,0 +1,103 @@
+import functools
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.nn.init as init
+
+
+def initialize_weights(net_l, scale=1):
+    if not isinstance(net_l, list):
+        net_l = [net_l]
+    for net in net_l:
+        for m in net.modules():
+            if isinstance(m, nn.Conv2d):
+                init.kaiming_normal_(m.weight, a=0, mode='fan_in')
+                m.weight.data *= scale  # for residual block
+                if m.bias is not None:
+                    m.bias.data.zero_()
+            elif isinstance(m, nn.Linear):
+                init.kaiming_normal_(m.weight, a=0, mode='fan_in')
+                m.weight.data *= scale
+                if m.bias is not None:
+                    m.bias.data.zero_()
+            elif isinstance(m, nn.BatchNorm2d):
+                init.constant_(m.weight, 1)
+                init.constant_(m.bias.data, 0.0)
+
+
+def make_layer(block, n_layers):
+    layers = []
+    for _ in range(n_layers):
+        layers.append(block())
+    return nn.Sequential(*layers)
+
+
+class ResidualDenseBlock_5C(nn.Module):
+    def __init__(self, nf=64, gc=32, bias=True):
+        super(ResidualDenseBlock_5C, self).__init__()
+        # gc: growth channel, i.e. intermediate channels
+        self.conv1 = nn.Conv2d(nf, gc, 3, 1, 1, bias=bias)
+        self.conv2 = nn.Conv2d(nf + gc, gc, 3, 1, 1, bias=bias)
+        self.conv3 = nn.Conv2d(nf + 2 * gc, gc, 3, 1, 1, bias=bias)
+        self.conv4 = nn.Conv2d(nf + 3 * gc, gc, 3, 1, 1, bias=bias)
+        self.conv5 = nn.Conv2d(nf + 4 * gc, nf, 3, 1, 1, bias=bias)
+        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
+
+        # initialization
+        initialize_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1)
+
+    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):
+    '''Residual in Residual Dense Block'''
+
+    def __init__(self, nf, gc=32):
+        super(RRDB, self).__init__()
+        self.RDB1 = ResidualDenseBlock_5C(nf, gc)
+        self.RDB2 = ResidualDenseBlock_5C(nf, gc)
+        self.RDB3 = ResidualDenseBlock_5C(nf, gc)
+
+    def forward(self, x):
+        out = self.RDB1(x)
+        out = self.RDB2(out)
+        out = self.RDB3(out)
+        return out * 0.2 + x
+
+
+class RRDBNet(nn.Module):
+    def __init__(self, in_nc=3, out_nc=3, nf=64, nb=23, gc=32, sf=4):
+        super(RRDBNet, self).__init__()
+        RRDB_block_f = functools.partial(RRDB, nf=nf, gc=gc)
+        self.sf = sf
+        print([in_nc, out_nc, nf, nb, gc, sf])
+
+        self.conv_first = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
+        self.RRDB_trunk = make_layer(RRDB_block_f, nb)
+        self.trunk_conv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
+        #### upsampling
+        self.upconv1 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
+        if self.sf==4:
+            self.upconv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
+        self.HRconv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
+        self.conv_last = nn.Conv2d(nf, out_nc, 3, 1, 1, bias=True)
+
+        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
+
+    def forward(self, x):
+        fea = self.conv_first(x)
+        trunk = self.trunk_conv(self.RRDB_trunk(fea))
+        fea = fea + trunk
+
+        fea = self.lrelu(self.upconv1(F.interpolate(fea, scale_factor=2, mode='nearest')))
+        if self.sf==4:
+            fea = self.lrelu(self.upconv2(F.interpolate(fea, scale_factor=2, mode='nearest')))
+        out = self.conv_last(self.lrelu(self.HRconv(fea)))
+
+        return out
+\ No newline at end of file
diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py
index 5e10c49c..ce841aa4 100644
--- a/modules/esrgan_model.py
+++ b/modules/esrgan_model.py
@@ -1,6 +1,4 @@
 import os
-import sys
-import traceback
 
 import numpy as np
 import torch
@@ -8,93 +6,119 @@ from PIL import Image
 from basicsr.utils.download_util import load_file_from_url
 
 import modules.esrgam_model_arch as arch
-import modules.images
-from modules import shared
-from modules import shared, modelloader
+from modules import shared, modelloader, images
 from modules.devices import has_mps
 from modules.paths import models_path
+from modules.upscaler import Upscaler, UpscalerData
 from modules.shared import opts
 
-model_dir = "ESRGAN"
-model_path = os.path.join(models_path, model_dir)
-model_url = "https://drive.google.com/u/0/uc?id=1TPrz5QKd8DHHt1k8SRtm6tMiPjz_Qene&export=download"
-model_name = "ESRGAN_x4"
-
-
-def load_model(path: str, name: str):
-    global model_path
-    global model_url
-    global model_dir
-    global model_name
-    if "http" in path:
-        filename = load_file_from_url(url=model_url, model_dir=model_path, file_name="%s.pth" % model_name, progress=True)
-    else:
-        filename = path
-    if not os.path.exists(filename) or filename is None:
-        print("Unable to load %s from %s" % (model_dir, filename))
-        return None
-    print("Loading %s from %s" % (model_dir, filename))
-    # this code is adapted from https://github.com/xinntao/ESRGAN
-    pretrained_net = torch.load(filename, map_location='cpu' if has_mps else None)
-    crt_model = arch.RRDBNet(3, 3, 64, 23, gc=32)
-
-    if 'conv_first.weight' in pretrained_net:
-        crt_model.load_state_dict(pretrained_net)
-        return crt_model
 
-    if 'model.0.weight' not in pretrained_net:
-        is_realesrgan = "params_ema" in pretrained_net and 'body.0.rdb1.conv1.weight' in pretrained_net["params_ema"]
-        if is_realesrgan:
-            raise Exception("The file is a RealESRGAN model, it can't be used as a ESRGAN model.")
-        else:
-            raise Exception("The file is not a ESRGAN model.")
+class UpscalerESRGAN(Upscaler):
+    def __init__(self, dirname):
+        self.name = "ESRGAN"
+        self.model_url = "https://drive.google.com/u/0/uc?id=1TPrz5QKd8DHHt1k8SRtm6tMiPjz_Qene&export=download"
+        self.model_name = "ESRGAN 4x"
+        self.scalers = []
+        self.user_path = dirname
+        self.model_path = os.path.join(models_path, self.name)
+        super().__init__()
+        model_paths = self.find_models(ext_filter=[".pt", ".pth"])
+        scalers = []
+        if len(model_paths) == 0:
+            scaler_data = UpscalerData(self.model_name, self.model_url, self, 4)
+            scalers.append(scaler_data)
+        for file in model_paths:
+            print(f"File: {file}")
+            if "http" in file:
+                name = self.model_name
+            else:
+                name = modelloader.friendly_name(file)
+
+            scaler_data = UpscalerData(name, file, self, 4)
+            print(f"ESRGAN: Adding scaler {name}")
+            self.scalers.append(scaler_data)
+
+    def do_upscale(self, img, selected_model):
+        model = self.load_model(selected_model)
+        if model is None:
+            return img
+        model.to(shared.device)
+        img = esrgan_upscale(model, img)
+        return img
 
-    crt_net = crt_model.state_dict()
-    load_net_clean = {}
-    for k, v in pretrained_net.items():
-        if k.startswith('module.'):
-            load_net_clean[k[7:]] = v
+    def load_model(self, path: str):
+        if "http" in path:
+            filename = load_file_from_url(url=self.model_url, model_dir=self.model_path,
+                                          file_name="%s.pth" % self.model_name,
+                                          progress=True)
         else:
-            load_net_clean[k] = v
-    pretrained_net = load_net_clean
-
-    tbd = []
-    for k, v in crt_net.items():
-        tbd.append(k)
-
-    # directly copy
-    for k, v in crt_net.items():
-        if k in pretrained_net and pretrained_net[k].size() == v.size():
-            crt_net[k] = pretrained_net[k]
-            tbd.remove(k)
-
-    crt_net['conv_first.weight'] = pretrained_net['model.0.weight']
-    crt_net['conv_first.bias'] = pretrained_net['model.0.bias']
-
-    for k in tbd.copy():
-        if 'RDB' in k:
-            ori_k = k.replace('RRDB_trunk.', 'model.1.sub.')
-            if '.weight' in k:
-                ori_k = ori_k.replace('.weight', '.0.weight')
-            elif '.bias' in k:
-                ori_k = ori_k.replace('.bias', '.0.bias')
-            crt_net[k] = pretrained_net[ori_k]
-            tbd.remove(k)
-
-    crt_net['trunk_conv.weight'] = pretrained_net['model.1.sub.23.weight']
-    crt_net['trunk_conv.bias'] = pretrained_net['model.1.sub.23.bias']
-    crt_net['upconv1.weight'] = pretrained_net['model.3.weight']
-    crt_net['upconv1.bias'] = pretrained_net['model.3.bias']
-    crt_net['upconv2.weight'] = pretrained_net['model.6.weight']
-    crt_net['upconv2.bias'] = pretrained_net['model.6.bias']
-    crt_net['HRconv.weight'] = pretrained_net['model.8.weight']
-    crt_net['HRconv.bias'] = pretrained_net['model.8.bias']
-    crt_net['conv_last.weight'] = pretrained_net['model.10.weight']
-    crt_net['conv_last.bias'] = pretrained_net['model.10.bias']
-
-    crt_model.load_state_dict(crt_net)
-    crt_model.eval()
-    return crt_model
+            filename = path
+        if not os.path.exists(filename) or filename is None:
+            print("Unable to load %s from %s" % (self.model_path, filename))
+            return None
+        # this code is adapted from https://github.com/xinntao/ESRGAN
+        pretrained_net = torch.load(filename, map_location='cpu' if has_mps else None)
+        crt_model = arch.RRDBNet(3, 3, 64, 23, gc=32)
+
+        if 'conv_first.weight' in pretrained_net:
+            crt_model.load_state_dict(pretrained_net)
+            return crt_model
+
+        if 'model.0.weight' not in pretrained_net:
+            is_realesrgan = "params_ema" in pretrained_net and 'body.0.rdb1.conv1.weight' in pretrained_net[
+                "params_ema"]
+            if is_realesrgan:
+                raise Exception("The file is a RealESRGAN model, it can't be used as a ESRGAN model.")
+            else:
+                raise Exception("The file is not a ESRGAN model.")
+
+        crt_net = crt_model.state_dict()
+        load_net_clean = {}
+        for k, v in pretrained_net.items():
+            if k.startswith('module.'):
+                load_net_clean[k[7:]] = v
+            else:
+                load_net_clean[k] = v
+        pretrained_net = load_net_clean
+
+        tbd = []
+        for k, v in crt_net.items():
+            tbd.append(k)
+
+        # directly copy
+        for k, v in crt_net.items():
+            if k in pretrained_net and pretrained_net[k].size() == v.size():
+                crt_net[k] = pretrained_net[k]
+                tbd.remove(k)
+
+        crt_net['conv_first.weight'] = pretrained_net['model.0.weight']
+        crt_net['conv_first.bias'] = pretrained_net['model.0.bias']
+
+        for k in tbd.copy():
+            if 'RDB' in k:
+                ori_k = k.replace('RRDB_trunk.', 'model.1.sub.')
+                if '.weight' in k:
+                    ori_k = ori_k.replace('.weight', '.0.weight')
+                elif '.bias' in k:
+                    ori_k = ori_k.replace('.bias', '.0.bias')
+                crt_net[k] = pretrained_net[ori_k]
+                tbd.remove(k)
+
+        crt_net['trunk_conv.weight'] = pretrained_net['model.1.sub.23.weight']
+        crt_net['trunk_conv.bias'] = pretrained_net['model.1.sub.23.bias']
+        crt_net['upconv1.weight'] = pretrained_net['model.3.weight']
+        crt_net['upconv1.bias'] = pretrained_net['model.3.bias']
+        crt_net['upconv2.weight'] = pretrained_net['model.6.weight']
+        crt_net['upconv2.bias'] = pretrained_net['model.6.bias']
+        crt_net['HRconv.weight'] = pretrained_net['model.8.weight']
+        crt_net['HRconv.bias'] = pretrained_net['model.8.bias']
+        crt_net['conv_last.weight'] = pretrained_net['model.10.weight']
+        crt_net['conv_last.bias'] = pretrained_net['model.10.bias']
+
+        crt_model.load_state_dict(crt_net)
+        crt_model.eval()
+        return crt_model
+
 
 def upscale_without_tiling(model, img):
     img = np.array(img)
@@ -115,7 +139,7 @@ def esrgan_upscale(model, img):
     if opts.ESRGAN_tile == 0:
         return upscale_without_tiling(model, img)
 
-    grid = modules.images.split_grid(img, opts.ESRGAN_tile, opts.ESRGAN_tile, opts.ESRGAN_tile_overlap)
+    grid = images.split_grid(img, opts.ESRGAN_tile, opts.ESRGAN_tile, opts.ESRGAN_tile_overlap)
     newtiles = []
     scale_factor = 1
 
@@ -130,38 +154,7 @@ def esrgan_upscale(model, img):
             newrow.append([x * scale_factor, w * scale_factor, output])
         newtiles.append([y * scale_factor, h * scale_factor, newrow])
 
-    newgrid = modules.images.Grid(newtiles, grid.tile_w * scale_factor, grid.tile_h * scale_factor, grid.image_w * scale_factor, grid.image_h * scale_factor, grid.overlap * scale_factor)
-    output = modules.images.combine_grid(newgrid)
+    newgrid = images.Grid(newtiles, grid.tile_w * scale_factor, grid.tile_h * scale_factor,
+                                  grid.image_w * scale_factor, grid.image_h * scale_factor, grid.overlap * scale_factor)
+    output = images.combine_grid(newgrid)
     return output
-
-
-class UpscalerESRGAN(modules.images.Upscaler):
-    def __init__(self, filename, title):
-        self.name = title
-        self.filename = filename
-
-    def do_upscale(self, img):
-        model = load_model(self.filename, self.name)
-        if model is None:
-            return img
-        model.to(shared.device)
-        img = esrgan_upscale(model, img)
-        return img
-
-
-def setup_model(dirname):
-    global model_path
-    global model_name
-    if not os.path.exists(model_path):
-        os.makedirs(model_path)
-
-    model_paths = modelloader.load_models(model_path, command_path=dirname, ext_filter=[".pt", ".pth"])
-    if len(model_paths) == 0:
-        modules.shared.sd_upscalers.append(UpscalerESRGAN(model_url, model_name))
-    for file in model_paths:
-        name = modelloader.friendly_name(file)
-        try:
-            modules.shared.sd_upscalers.append(UpscalerESRGAN(file, name))
-        except Exception:
-            print(f"Error loading ESRGAN model: {file}", file=sys.stderr)
-            print(traceback.format_exc(), file=sys.stderr)
diff --git a/modules/extras.py b/modules/extras.py
index af6e631f..d7d0fa54 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -66,29 +66,28 @@ def run_extras(extras_mode, image, image_folder, gfpgan_visibility, codeformer_v
             info += f"CodeFormer w: {round(codeformer_weight, 2)}, CodeFormer visibility:{round(codeformer_visibility, 2)}\n"
             image = res
 
-        if upscaling_resize != 1.0:
-            def upscale(image, scaler_index, resize):
-                small = image.crop((image.width // 2, image.height // 2, image.width // 2 + 10, image.height // 2 + 10))
-                pixels = tuple(np.array(small).flatten().tolist())
-                key = (resize, scaler_index, image.width, image.height, gfpgan_visibility, codeformer_visibility, codeformer_weight) + pixels
+        def upscale(image, scaler_index, resize):
+            small = image.crop((image.width // 2, image.height // 2, image.width // 2 + 10, image.height // 2 + 10))
+            pixels = tuple(np.array(small).flatten().tolist())
+            key = (resize, scaler_index, image.width, image.height, gfpgan_visibility, codeformer_visibility, codeformer_weight) + pixels
 
-                c = cached_images.get(key)
-                if c is None:
-                    upscaler = shared.sd_upscalers[scaler_index]
-                    c = upscaler.upscale(image, image.width * resize, image.height * resize)
-                    cached_images[key] = c
+            c = cached_images.get(key)
+            if c is None:
+                upscaler = shared.sd_upscalers[scaler_index]
+                c = upscaler.scaler.upscale(image, resize, upscaler.data_path)
+                cached_images[key] = c
 
-                return c
+            return c
 
-            info += f"Upscale: {round(upscaling_resize, 3)}, model:{shared.sd_upscalers[extras_upscaler_1].name}\n"
-            res = upscale(image, extras_upscaler_1, upscaling_resize)
+        info += f"Upscale: {round(upscaling_resize, 3)}, model:{shared.sd_upscalers[extras_upscaler_1].name}\n"
+        res = upscale(image, extras_upscaler_1, upscaling_resize)
 
-            if extras_upscaler_2 != 0 and extras_upscaler_2_visibility > 0:
-                res2 = upscale(image, extras_upscaler_2, upscaling_resize)
-                info += f"Upscale: {round(upscaling_resize, 3)}, visibility: {round(extras_upscaler_2_visibility, 3)}, model:{shared.sd_upscalers[extras_upscaler_2].name}\n"
-                res = Image.blend(res, res2, extras_upscaler_2_visibility)
+        if extras_upscaler_2 != 0 and extras_upscaler_2_visibility > 0:
+            res2 = upscale(image, extras_upscaler_2, upscaling_resize)
+            info += f"Upscale: {round(upscaling_resize, 3)}, visibility: {round(extras_upscaler_2_visibility, 3)}, model:{shared.sd_upscalers[extras_upscaler_2].name}\n"
+            res = Image.blend(res, res2, extras_upscaler_2_visibility)
 
-            image = res
+        image = res
 
         while len(cached_images) > 2:
             del cached_images[next(iter(cached_images.keys()))]
diff --git a/modules/gfpgan_model.py b/modules/gfpgan_model.py
index ffb6960d..2bf8a1ee 100644
--- a/modules/gfpgan_model.py
+++ b/modules/gfpgan_model.py
@@ -1,24 +1,23 @@
 import os
 import sys
 import traceback
-from glob import glob
 
-from modules import shared, devices
-from modules.shared import cmd_opts
-from modules.paths import script_path
+import facexlib
+import gfpgan
+
 import modules.face_restoration
 from modules import shared, devices, modelloader
 from modules.paths import models_path
 
 model_dir = "GFPGAN"
-cmd_dir = None
+user_path = None
 model_path = os.path.join(models_path, model_dir)
 model_url = "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth"
-
+have_gfpgan = False
 loaded_gfpgan_model = None
 
 
-def gfpgan():
+def gfpgann():
     global loaded_gfpgan_model
     global model_path
     if loaded_gfpgan_model is not None:
@@ -28,14 +27,16 @@ def gfpgan():
     if gfpgan_constructor is None:
         return None
 
-    models = modelloader.load_models(model_path, model_url, cmd_dir)
-    if len(models) != 0:
+    models = modelloader.load_models(model_path, model_url, user_path, ext_filter="GFPGAN")
+    if len(models) == 1 and "http" in models[0]:
+        model_file = models[0]
+    elif len(models) != 0:
         latest_file = max(models, key=os.path.getctime)
         model_file = latest_file
     else:
         print("Unable to load gfpgan model!")
         return None
-    model = gfpgan_constructor(model_path=model_file, model_dir=model_path, upscale=1, arch='clean', channel_multiplier=2,
+    model = gfpgan_constructor(model_path=model_file, upscale=1, arch='clean', channel_multiplier=2,
                                bg_upsampler=None)
     model.gfpgan.to(shared.device)
     loaded_gfpgan_model = model
@@ -44,11 +45,12 @@ def gfpgan():
 
 
 def gfpgan_fix_faces(np_image):
-    model = gfpgan()
+    model = gfpgann()
     if model is None:
         return np_image
     np_image_bgr = np_image[:, :, ::-1]
-    cropped_faces, restored_faces, gfpgan_output_bgr = model.enhance(np_image_bgr, has_aligned=False, only_center_face=False, paste_back=True)
+    cropped_faces, restored_faces, gfpgan_output_bgr = model.enhance(np_image_bgr, has_aligned=False,
+                                                                     only_center_face=False, paste_back=True)
     np_image = gfpgan_output_bgr[:, :, ::-1]
 
     if shared.opts.face_restoration_unload:
@@ -57,7 +59,6 @@ def gfpgan_fix_faces(np_image):
     return np_image
 
 
-have_gfpgan = False
 gfpgan_constructor = None
 
 
@@ -67,14 +68,33 @@ def setup_model(dirname):
         os.makedirs(model_path)
 
     try:
-        from modules.gfpgan_model_arch import GFPGANerr
-        global cmd_dir
+        from gfpgan import GFPGANer
+        from facexlib import detection, parsing
+        global user_path
         global have_gfpgan
         global gfpgan_constructor
 
-        cmd_dir = dirname
+        load_file_from_url_orig = gfpgan.utils.load_file_from_url
+        facex_load_file_from_url_orig = facexlib.detection.load_file_from_url
+        facex_load_file_from_url_orig2 = facexlib.parsing.load_file_from_url
+
+        def my_load_file_from_url(**kwargs):
+            print("Setting model_dir to " + model_path)
+            return load_file_from_url_orig(**dict(kwargs, model_dir=model_path))
+
+        def facex_load_file_from_url(**kwargs):
+            return facex_load_file_from_url_orig(**dict(kwargs, save_dir=model_path, model_dir=None))
+
+        def facex_load_file_from_url2(**kwargs):
+            return facex_load_file_from_url_orig2(**dict(kwargs, save_dir=model_path, model_dir=None))
+
+        gfpgan.utils.load_file_from_url = my_load_file_from_url
+        facexlib.detection.load_file_from_url = facex_load_file_from_url
+        facexlib.parsing.load_file_from_url = facex_load_file_from_url2
+        user_path = dirname
+        print("Have gfpgan should be true?")
         have_gfpgan = True
-        gfpgan_constructor = GFPGANerr
+        gfpgan_constructor = GFPGANer
 
         class FaceRestorerGFPGAN(modules.face_restoration.FaceRestoration):
             def name(self):
@@ -82,7 +102,9 @@ def setup_model(dirname):
 
             def restore(self, np_image):
                 np_image_bgr = np_image[:, :, ::-1]
-                cropped_faces, restored_faces, gfpgan_output_bgr = gfpgan().enhance(np_image_bgr, has_aligned=False, only_center_face=False, paste_back=True)
+                cropped_faces, restored_faces, gfpgan_output_bgr = gfpgann().enhance(np_image_bgr, has_aligned=False,
+                                                                                     only_center_face=False,
+                                                                                     paste_back=True)
                 np_image = gfpgan_output_bgr[:, :, ::-1]
 
                 return np_image
diff --git a/modules/gfpgan_model_arch.py b/modules/gfpgan_model_arch.py
deleted file mode 100644
index d81cea96..00000000
--- a/modules/gfpgan_model_arch.py
+++ /dev/null
@@ -1,150 +0,0 @@
-# GFPGAN likes to download stuff "wherever", and we're trying to fix that, so this is a copy of the original...
-
-import cv2
-import os
-import torch
-from basicsr.utils import img2tensor, tensor2img
-from basicsr.utils.download_util import load_file_from_url
-from facexlib.utils.face_restoration_helper import FaceRestoreHelper
-from torchvision.transforms.functional import normalize
-
-from gfpgan.archs.gfpgan_bilinear_arch import GFPGANBilinear
-from gfpgan.archs.gfpganv1_arch import GFPGANv1
-from gfpgan.archs.gfpganv1_clean_arch import GFPGANv1Clean
-
-ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-
-
-class GFPGANerr():
-    """Helper for restoration with GFPGAN.
-
-    It will detect and crop faces, and then resize the faces to 512x512.
-    GFPGAN is used to restored the resized faces.
-    The background is upsampled with the bg_upsampler.
-    Finally, the faces will be pasted back to the upsample background image.
-
-    Args:
-        model_path (str): The path to the GFPGAN model. It can be urls (will first download it automatically).
-        upscale (float): The upscale of the final output. Default: 2.
-        arch (str): The GFPGAN architecture. Option: clean | original. Default: clean.
-        channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2.
-        bg_upsampler (nn.Module): The upsampler for the background. Default: None.
-    """
-
-    def __init__(self, model_path, model_dir, upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=None, device=None):
-        self.upscale = upscale
-        self.bg_upsampler = bg_upsampler
-
-        # initialize model
-        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') if device is None else device
-        # initialize the GFP-GAN
-        if arch == 'clean':
-            self.gfpgan = GFPGANv1Clean(
-                out_size=512,
-                num_style_feat=512,
-                channel_multiplier=channel_multiplier,
-                decoder_load_path=None,
-                fix_decoder=False,
-                num_mlp=8,
-                input_is_latent=True,
-                different_w=True,
-                narrow=1,
-                sft_half=True)
-        elif arch == 'bilinear':
-            self.gfpgan = GFPGANBilinear(
-                out_size=512,
-                num_style_feat=512,
-                channel_multiplier=channel_multiplier,
-                decoder_load_path=None,
-                fix_decoder=False,
-                num_mlp=8,
-                input_is_latent=True,
-                different_w=True,
-                narrow=1,
-                sft_half=True)
-        elif arch == 'original':
-            self.gfpgan = GFPGANv1(
-                out_size=512,
-                num_style_feat=512,
-                channel_multiplier=channel_multiplier,
-                decoder_load_path=None,
-                fix_decoder=True,
-                num_mlp=8,
-                input_is_latent=True,
-                different_w=True,
-                narrow=1,
-                sft_half=True)
-        elif arch == 'RestoreFormer':
-            from gfpgan.archs.restoreformer_arch import RestoreFormer
-            self.gfpgan = RestoreFormer()
-        # initialize face helper
-        self.face_helper = FaceRestoreHelper(
-            upscale,
-            face_size=512,
-            crop_ratio=(1, 1),
-            det_model='retinaface_resnet50',
-            save_ext='png',
-            use_parse=True,
-            device=self.device,
-            model_rootpath=model_dir)
-
-        if model_path.startswith('https://'):
-            model_path = load_file_from_url(
-                url=model_path, model_dir=model_dir, progress=True, file_name=None)
-        loadnet = torch.load(model_path)
-        if 'params_ema' in loadnet:
-            keyname = 'params_ema'
-        else:
-            keyname = 'params'
-        self.gfpgan.load_state_dict(loadnet[keyname], strict=True)
-        self.gfpgan.eval()
-        self.gfpgan = self.gfpgan.to(self.device)
-
-    @torch.no_grad()
-    def enhance(self, img, has_aligned=False, only_center_face=False, paste_back=True, weight=0.5):
-        self.face_helper.clean_all()
-
-        if has_aligned:  # the inputs are already aligned
-            img = cv2.resize(img, (512, 512))
-            self.face_helper.cropped_faces = [img]
-        else:
-            self.face_helper.read_image(img)
-            # get face landmarks for each face
-            self.face_helper.get_face_landmarks_5(only_center_face=only_center_face, eye_dist_threshold=5)
-            # eye_dist_threshold=5: skip faces whose eye distance is smaller than 5 pixels
-            # TODO: even with eye_dist_threshold, it will still introduce wrong detections and restorations.
-            # align and warp each face
-            self.face_helper.align_warp_face()
-
-        # face restoration
-        for cropped_face in self.face_helper.cropped_faces:
-            # prepare data
-            cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True)
-            normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
-            cropped_face_t = cropped_face_t.unsqueeze(0).to(self.device)
-
-            try:
-                output = self.gfpgan(cropped_face_t, return_rgb=False, weight=weight)[0]
-                # convert to image
-                restored_face = tensor2img(output.squeeze(0), rgb2bgr=True, min_max=(-1, 1))
-            except RuntimeError as error:
-                print(f'\tFailed inference for GFPGAN: {error}.')
-                restored_face = cropped_face
-
-            restored_face = restored_face.astype('uint8')
-            self.face_helper.add_restored_face(restored_face)
-
-        if not has_aligned and paste_back:
-            # upsample the background
-            if self.bg_upsampler is not None:
-                # Now only support RealESRGAN for upsampling background
-                bg_img = self.bg_upsampler.enhance(img, outscale=self.upscale)[0]
-            else:
-                bg_img = None
-
-            self.face_helper.get_inverse_affine(None)
-            # paste each restored face to the input image
-            restored_img = self.face_helper.paste_faces_to_input_image(upsample_img=bg_img)
-            return self.face_helper.cropped_faces, self.face_helper.restored_faces, restored_img
-        else:
-            return self.face_helper.cropped_faces, self.face_helper.restored_faces, None
diff --git a/modules/images.py b/modules/images.py
index 9458bf8d..a6538dbe 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -11,7 +11,6 @@ from PIL import Image, ImageFont, ImageDraw, PngImagePlugin
 from fonts.ttf import Roboto
 import string
 
-import modules.shared
 from modules import sd_samplers, shared
 from modules.shared import opts, cmd_opts
 
@@ -52,8 +51,8 @@ def split_grid(image, tile_w=512, tile_h=512, overlap=64):
     cols = math.ceil((w - overlap) / non_overlap_width)
     rows = math.ceil((h - overlap) / non_overlap_height)
 
-    dx = (w - tile_w) / (cols-1) if cols > 1 else 0
-    dy = (h - tile_h) / (rows-1) if rows > 1 else 0
+    dx = (w - tile_w) / (cols - 1) if cols > 1 else 0
+    dy = (h - tile_h) / (rows - 1) if rows > 1 else 0