Merge pull request #1109 from d8ahazard/ModelLoader

Model Loader, Fixes

18 files changed, 2145 insertions, 1385 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/codeformer_model.py b/modules/codeformer_model.py
index 8fbdea24..efd881eb 100644
--- a/modules/codeformer_model.py
+++ b/modules/codeformer_model.py
@@ -5,31 +5,31 @@ import traceback
 import cv2

 import torch

 

-from modules import shared, devices

-from modules.paths import script_path

-import modules.shared

 import modules.face_restoration

-from importlib import reload

+import modules.shared

+from modules import shared, devices, modelloader

+from modules.paths import script_path, models_path

 

-# codeformer people made a choice to include modified basicsr librry to their projectwhich makes

-# it utterly impossiblr to use it alongside with other libraries that also use basicsr, like GFPGAN.

+# codeformer people made a choice to include modified basicsr library to their project which makes

+# it utterly impossible to use it alongside with other libraries that also use basicsr, like GFPGAN.

 # I am making a choice to include some files from codeformer to work around this issue.

-

-pretrain_model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth'

+model_dir = "Codeformer"

+model_path = os.path.join(models_path, model_dir)

+model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth'

 

 have_codeformer = False

 codeformer = None

 

-def setup_codeformer():

+

+def setup_model(dirname):

+    global model_path

+    if not os.path.exists(model_path):

+        os.makedirs(model_path)

+

     path = modules.paths.paths.get("CodeFormer", None)

     if path is None:

         return

 

-

-    # both GFPGAN and CodeFormer use bascisr, one has it installed from pip the other uses its own

-    #stored_sys_path = sys.path

-    #sys.path = [path] + sys.path

-

     try:

         from torchvision.transforms.functional import normalize

         from modules.codeformer.codeformer_arch import CodeFormer

@@ -44,18 +44,23 @@ def setup_codeformer():
             def name(self):

                 return "CodeFormer"

 

-            def __init__(self):

+            def __init__(self, dirname):

                 self.net = None

                 self.face_helper = None

+                self.cmd_dir = dirname

 

             def create_models(self):

 

                 if self.net is not None and self.face_helper is not None:

                     self.net.to(devices.device_codeformer)

                     return self.net, self.face_helper

-

+                model_paths = modelloader.load_models(model_path, model_url, self.cmd_dir)

+                if len(model_paths) != 0:

+                    ckpt_path = model_paths[0]

+                else:

+                    print("Unable to load codeformer model.")

+                    return None, None

                 net = net_class(dim_embd=512, codebook_size=1024, n_head=8, n_layers=9, connect_list=['32', '64', '128', '256']).to(devices.device_codeformer)

-                ckpt_path = load_file_from_url(url=pretrain_model_url, model_dir=os.path.join(path, 'weights/CodeFormer'), progress=True)

                 checkpoint = torch.load(ckpt_path)['params_ema']

                 net.load_state_dict(checkpoint)

                 net.eval()

@@ -74,6 +79,9 @@ def setup_codeformer():
                 original_resolution = np_image.shape[0:2]

 

                 self.create_models()

+                if self.net is None or self.face_helper is None:

+                    return np_image

+

                 self.face_helper.clean_all()

                 self.face_helper.read_image(np_image)

                 self.face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5)

@@ -114,7 +122,7 @@ def setup_codeformer():
         have_codeformer = True

 

         global codeformer

-        codeformer = FaceRestorerCodeFormer()

+        codeformer = FaceRestorerCodeFormer(dirname)

         shared.face_restorers.append(codeformer)

 

     except Exception:

diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py
index 7f3baf31..ce841aa4 100644
--- a/modules/esrgan_model.py
+++ b/modules/esrgan_model.py
@@ -1,80 +1,124 @@
 import os

-import sys

-import traceback

 

 import numpy as np

 import torch

 from PIL import Image

+from basicsr.utils.download_util import load_file_from_url

 

 import modules.esrgam_model_arch as arch

-from modules import shared

-from modules.shared import opts

+from modules import shared, modelloader, images

 from modules.devices import has_mps

-import modules.images

-

+from modules.paths import models_path

+from modules.upscaler import Upscaler, UpscalerData

+from modules.shared import opts

 

-def load_model(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

+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

 

-    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.")

+    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:

-            raise Exception("The file is not a ESRGAN 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

 

-    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)

@@ -95,7 +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

 

@@ -110,32 +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.model = load_model(filename)

-

-    def do_upscale(self, img):

-        model = self.model.to(shared.device)

-        img = esrgan_upscale(model, img)

-        return img

-

-

-def load_models(dirname):

-    for file in os.listdir(dirname):

-        path = os.path.join(dirname, file)

-        model_name, extension = os.path.splitext(file)

-

-        if extension != '.pt' and extension != '.pth':

-            continue

-

-        try:

-            modules.shared.sd_upscalers.append(UpscalerESRGAN(path, model_name))

-        except Exception:

-            print(f"Error loading ESRGAN model: {path}", file=sys.stderr)

-            print(traceback.format_exc(), file=sys.stderr)

diff --git a/modules/extras.py b/modules/extras.py
index c2543fcf..1d4e9fa8 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -40,6 +40,8 @@ def run_extras(extras_mode, image, image_folder, gfpgan_visibility, codeformer_v
 

     outputs = []

     for image, image_name in zip(imageArr, imageNameArr):

+        if image is None:

+            return outputs, "Please select an input image.", ''

         existing_pnginfo = image.info or {}

 

         image = image.convert("RGB")

@@ -65,29 +67,28 @@ 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 44c5dc6c..2bf8a1ee 100644
--- a/modules/gfpgan_model.py
+++ b/modules/gfpgan_model.py
@@ -1,39 +1,25 @@
 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 modules.face_restoration

-

-

-def gfpgan_model_path():

-    from modules.shared import cmd_opts

-

-    filemask = 'GFPGAN*.pth'

-

-    if cmd_opts.gfpgan_model is not None:

-        return cmd_opts.gfpgan_model

-

-    places = [script_path, '.', os.path.join(cmd_opts.gfpgan_dir, 'experiments/pretrained_models')]

-

-    filename = None

-    for place in places:

-        filename = next(iter(glob(os.path.join(place, filemask))), None)

-        if filename is not None:

-            break

-

-    return filename

+import facexlib

+import gfpgan

 

+import modules.face_restoration

+from modules import shared, devices, modelloader

+from modules.paths import models_path

 

+model_dir = "GFPGAN"

+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:

         loaded_gfpgan_model.gfpgan.to(shared.device)

         return loaded_gfpgan_model

@@ -41,7 +27,17 @@ def gfpgan():
     if gfpgan_constructor is None:

         return None

 

-    model = gfpgan_constructor(model_path=gfpgan_model_path() or 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth', upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None)

+    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, upscale=1, arch='clean', channel_multiplier=2,

+                               bg_upsampler=None)

     model.gfpgan.to(shared.device)

     loaded_gfpgan_model = model

 

@@ -49,10 +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:

@@ -61,21 +59,41 @@ def gfpgan_fix_faces(np_image):
     return np_image

 

 

-have_gfpgan = False

 gfpgan_constructor = None

 

-def setup_gfpgan():

-    try:

-        gfpgan_model_path()

 

-        if os.path.exists(cmd_opts.gfpgan_dir):

-            sys.path.append(os.path.abspath(cmd_opts.gfpgan_dir))

-        from gfpgan import GFPGANer

+def setup_model(dirname):

+    global model_path

+    if not os.path.exists(model_path):

+        os.makedirs(model_path)

 

+    try:

+        from gfpgan import GFPGANer

+        from facexlib import detection, parsing

+        global user_path

         global have_gfpgan

-        have_gfpgan = True

-

         global gfpgan_constructor

+

+        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 = GFPGANer

 

         class FaceRestorerGFPGAN(modules.face_restoration.FaceRestoration):

@@ -84,7 +102,9 @@ def setup_gfpgan():
 

             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/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

 

     grid = Grid([], tile_w, tile_h, w, h, overlap)

     for row in range(rows):

@@ -67,7 +66,7 @@ def split_grid(image, tile_w=512, tile_h=512, overlap=64):
         for col in range(cols):

             x = int(col * dx)

 

-            if x+tile_w >= w:

+            if x + tile_w >= w:

                 x = w - tile_w

 

             tile = image.crop((x, y, x + tile_w, y + tile_h))

@@ -85,8 +84,10 @@ def combine_grid(grid):
         r = r.astype(np.uint8)

         return Image.fromarray(r, 'L')

 

-    mask_w = make_mask_image(np.arange(grid.overlap, dtype=np.float32).reshape((1, grid.overlap)).repeat(grid.tile_h, axis=0))

-    mask_h = make_mask_image(np.arange(grid.overlap, dtype=np.float32).reshape((grid.overlap, 1)).repeat(grid.image_w, axis=1))

+    mask_w = make_mask_image(

+        np.arange(grid.overlap, dtype=np.float32).reshape((1, grid.overlap)).repeat(grid.tile_h, axis=0))

+    mask_h = make_mask_image(

+        np.arange(grid.overlap, dtype=np.float32).reshape((grid.overlap, 1)).repeat(grid.image_w, axis=1))

 

     combined_image = Image.new("RGB", (grid.image_w, grid.image_h))

     for y, h, row in grid.tiles:

@@ -129,10 +130,12 @@ def draw_grid_annotations(im, width, height, hor_texts, ver_texts):
 

     def draw_texts(drawing, draw_x, draw_y, lines):

         for i, line in enumerate(lines):

-            drawing.multiline_text((draw_x, draw_y + line.size[1] / 2), line.text, font=fnt, fill=color_active if line.is_active else color_inactive, anchor="mm", align="center")

+            drawing.multiline_text((draw_x, draw_y + line.size[1] / 2), line.text, font=fnt,

+                                   fill=color_active if line.is_active else color_inactive, anchor="mm", align="center")

 

             if not line.is_active:

-                drawing.line((draw_x - line.size[0]//2, draw_y + line.size[1]//2, draw_x + line.size[0]//2, draw_y + line.size[1]//2), fill=color_inactive, width=4)

+                drawing.line((draw_x - line.size[0] // 2, draw_y + line.size[1] // 2, draw_x + line.size[0] // 2,

+                              draw_y + line.size[1] // 2), fill=color_inactive, width=4)

 

             draw_y += line.size[1] + line_spacing

 

@@ -171,7 +174,8 @@ def draw_grid_annotations(im, width, height, hor_texts, ver_texts):
             line.size = (bbox[2] - bbox[0], bbox[3] - bbox[1])

 

     hor_text_heights = [sum([line.size[1] + line_spacing for line in lines]) - line_spacing for lines in hor_texts]

-    ver_text_heights = [sum([line.size[1] + line_spacing for line in lines]) - line_spacing * len(lines) for lines in ver_texts]

+    ver_text_heights = [sum([line.size[1] + line_spacing for line in lines]) - line_spacing * len(lines) for lines in

+                        ver_texts]

 

     pad_top = max(hor_text_heights) + line_spacing * 2

 

@@ -202,8 +206,10 @@ def draw_prompt_matrix(im, width, height, all_prompts):
     prompts_horiz = prompts[:boundary]

     prompts_vert = prompts[boundary:]

 

-    hor_texts = [[GridAnnotation(x, is_active=pos & (1 << i) != 0) for i, x in enumerate(prompts_horiz)] for pos in range(1 << len(prompts_horiz))]

-    ver_texts = [[GridAnnotation(x, is_active=pos & (1 << i) != 0) for i, x in enumerate(prompts_vert)] for pos in range(1 << len(prompts_vert))]

+    hor_texts = [[GridAnnotation(x, is_active=pos & (1 << i) != 0) for i, x in enumerate(prompts_horiz)] for pos in

+                 range(1 << len(prompts_horiz))]

+    ver_texts = [[GridAnnotation(x, is_active=pos & (1 << i) != 0) for i, x in enumerate(prompts_vert)] for pos in

+                 range(1 << len(prompts_vert))]

 

     return draw_grid_annotations(im, width, height, hor_texts, ver_texts)

 

@@ -214,7 +220,8 @@ def resize_image(resize_mode, im, width, height):
             return im.resize((w, h), resample=LANCZOS)

 

         upscaler = [x for x in shared.sd_upscalers if x.name == opts.upscaler_for_img2img][0]

-        return upscaler.upscale(im, w, h)

+        scale = w / im.width

+        return upscaler.scaler.upscale(im, scale)

 

     if resize_mode == 0:

         res = resize(im, width, height)

@@ -244,11 +251,13 @@ def resize_image(resize_mode, im, width, height):
         if ratio < src_ratio:

             fill_height = height // 2 - src_h // 2

             res.paste(resized.resize((width, fill_height), box=(0, 0, width, 0)), box=(0, 0))

-            res.paste(resized.resize((width, fill_height), box=(0, resized.height, width, resized.height)), box=(0, fill_height + src_h))

+            res.paste(resized.resize((width, fill_height), box=(0, resized.height, width, resized.height)),

+                      box=(0, fill_height + src_h))

         elif ratio > src_ratio:

             fill_width = width // 2 - src_w // 2

             res.paste(resized.resize((fill_width, height), box=(0, 0, 0, height)), box=(0, 0))

-            res.paste(resized.resize((fill_width, height), box=(resized.width, 0, resized.width, height)), box=(fill_width + src_w, 0))

+            res.paste(resized.resize((fill_width, height), box=(resized.width, 0, resized.width, height)),

+                      box=(fill_width + src_w, 0))

 

     return res

 

@@ -256,7 +265,7 @@ def resize_image(resize_mode, im, width, height):
 invalid_filename_chars = '<>:"/\\|?*\n'

 invalid_filename_prefix = ' '

 invalid_filename_postfix = ' .'

-re_nonletters = re.compile(r'[\s'+string.punctuation+']+')

+re_nonletters = re.compile(r'[\s' + string.punctuation + ']+')

 max_filename_part_length = 128

 

 

@@ -283,7 +292,8 @@ def apply_filename_pattern(x, p, seed, prompt):
             words = [x for x in re_nonletters.split(prompt or "") if len(x) > 0]

             if len(words) == 0:

                 words = ["empty"]

-            x = x.replace("[prompt_words]", sanitize_filename_part(" ".join(words[0:max_prompt_words]), replace_spaces=False))

+            x = x.replace("[prompt_words]",

+                          sanitize_filename_part(" ".join(words[0:max_prompt_words]), replace_spaces=False))

 

     if p is not None:

         x = x.replace("[steps]", str(p.steps))

@@ -291,7 +301,8 @@ def apply_filename_pattern(x, p, seed, prompt):
         x = x.replace("[width]", str(p.width))

         x = x.replace("[height]", str(p.height))

         x = x.replace("[styles]", sanitize_filename_part(", ".join(p.styles), replace_spaces=False))

-        x = x.replace("[sampler]", sanitize_filename_part(sd_samplers.samplers[p.sampler_index].name, replace_spaces=False))

+        x = x.replace("[sampler]",

+                      sanitize_filename_part(sd_samplers.samplers[p.sampler_index].name, replace_spaces=False))

 

     x = x.replace("[model_hash]", shared.sd_model.sd_model_hash)

     x = x.replace("[date]", datetime.date.today().isoformat())

@@ -303,6 +314,7 @@ def apply_filename_pattern(x, p, seed, prompt):
 

     return x

 

+

 def get_next_sequence_number(path, basename):

     """

     Determines and returns the next sequence number to use when saving an image in the specified directory.

@@ -316,7 +328,8 @@ def get_next_sequence_number(path, basename):
     prefix_length = len(basename)

     for p in os.listdir(path):

         if p.startswith(basename):

-            l = os.path.splitext(p[prefix_length:])[0].split('-') #splits the filename (removing the basename first if one is defined, so the sequence number is always the first element)

+            l = os.path.splitext(p[prefix_length:])[0].split(

+                '-')  # splits the filename (removing the basename first if one is defined, so the sequence number is always the first element)

             try:

                 result = max(int(l[0]), result)

             except ValueError:

@@ -324,7 +337,10 @@ def get_next_sequence_number(path, basename):
 

     return result + 1

 

-def save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False, no_prompt=False, grid=False, pnginfo_section_name='parameters', p=None, existing_info=None, forced_filename=None, suffix=""):

+

+def save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False,

+               no_prompt=False, grid=False, pnginfo_section_name='parameters', p=None, existing_info=None,

+               forced_filename=None, suffix=""):

     if short_filename or prompt is None or seed is None:

         file_decoration = ""

     elif opts.save_to_dirs:

@@ -361,7 +377,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
         fullfn = "a.png"

         fullfn_without_extension = "a"

         for i in range(500):

-            fn = f"{basecount+i:05}" if basename == '' else f"{basename}-{basecount+i:04}"

+            fn = f"{basecount + i:05}" if basename == '' else f"{basename}-{basecount + i:04}"

             fullfn = os.path.join(path, f"{fn}{file_decoration}.{extension}")

             fullfn_without_extension = os.path.join(path, f"{fn}{file_decoration}")

             if not os.path.exists(fullfn):

@@ -403,31 +419,3 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
             file.write(info + "\n")

 

 

-class Upscaler:

-    name = "Lanczos"

-

-    def do_upscale(self, img):

-        return img

-

-    def upscale(self, img, w, h):

-        for i in range(3):

-            if img.width >= w and img.height >= h:

-                break

-

-            img = self.do_upscale(img)

-

-        if img.width != w or img.height != h:

-            img = img.resize((int(w), int(h)), resample=LANCZOS)

-

-        return img

-

-

-class UpscalerNone(Upscaler):

-    name = "None"

-

-    def upscale(self, img, w, h):

-        return img

-

-

-modules.shared.sd_upscalers.append(UpscalerNone())

-modules.shared.sd_upscalers.append(Upscaler())

diff --git a/modules/ldsr_model.py b/modules/ldsr_model.py
index 95e84659..969d1a0d 100644
--- a/modules/ldsr_model.py
+++ b/modules/ldsr_model.py
@@ -1,67 +1,45 @@
 import os
 import sys
 import traceback
-from collections import namedtuple
 
 from basicsr.utils.download_util import load_file_from_url
 
-import modules.images
+from modules.upscaler import Upscaler, UpscalerData
+from modules.ldsr_model_arch import LDSR
 from modules import shared
-from modules.paths import script_path
+from modules.paths import models_path
 
-LDSRModelInfo = namedtuple("LDSRModelInfo", ["name", "location", "model", "netscale"])
 
-ldsr_models = []
-have_ldsr = False
-LDSR_obj = None
-
-
-class UpscalerLDSR(modules.images.Upscaler):
-    def __init__(self, steps):
-        self.steps = steps
+class UpscalerLDSR(Upscaler):
+    def __init__(self, user_path):
         self.name = "LDSR"
-
-    def do_upscale(self, img):
-        return upscale_with_ldsr(img)
-
-
-def add_lsdr():
-    modules.shared.sd_upscalers.append(UpscalerLDSR(100))
-
-
-def setup_ldsr():
-    path = modules.paths.paths.get("LDSR", None)
-    if path is None:
-        return
-    global have_ldsr
-    global LDSR_obj
-    try:
-        from LDSR import LDSR
-        model_url = "https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1"
-        yaml_url = "https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1"
-        repo_path = 'latent-diffusion/experiments/pretrained_models/'
-        model_path = load_file_from_url(url=model_url, model_dir=os.path.join("repositories", repo_path),
-                                        progress=True, file_name="model.chkpt")
-        yaml_path = load_file_from_url(url=yaml_url, model_dir=os.path.join("repositories", repo_path),
-                                       progress=True, file_name="project.yaml")
-        have_ldsr = True
-        LDSR_obj = LDSR(model_path, yaml_path)
-
-
-    except Exception:
-        print("Error importing LDSR:", file=sys.stderr)
-        print(traceback.format_exc(), file=sys.stderr)
-        have_ldsr = False
-
-
-def upscale_with_ldsr(image):
-    setup_ldsr()
-    if not have_ldsr or LDSR_obj is None:
-        return image
-
-    ddim_steps = shared.opts.ldsr_steps
-    pre_scale = shared.opts.ldsr_pre_down
-    post_scale = shared.opts.ldsr_post_down
-
-    image = LDSR_obj.super_resolution(image, ddim_steps, pre_scale, post_scale)
-    return image
+        self.model_path = os.path.join(models_path, self.name)
+        self.user_path = user_path
+        self.model_url = "https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1"
+        self.yaml_url = "https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1"
+        super().__init__()
+        scaler_data = UpscalerData("LDSR", None, self)
+        self.scalers = [scaler_data]
+
+    def load_model(self, path: str):
+        model = load_file_from_url(url=self.model_url, model_dir=self.model_path,
+                                   file_name="model.pth", progress=True)
+        yaml = load_file_from_url(url=self.model_url, model_dir=self.model_path,
+                                  file_name="project.yaml", progress=True)
+
+        try:
+            return LDSR(model, yaml)
+
+        except Exception:
+            print("Error importing LDSR:", file=sys.stderr)
+            print(traceback.format_exc(), file=sys.stderr)
+        return None
+
+    def do_upscale(self, img, path):
+        ldsr = self.load_model(path)
+        if ldsr is None:
+            print("NO LDSR!")
+            return img
+        ddim_steps = shared.opts.ldsr_steps
+        pre_scale = shared.opts.ldsr_pre_down
+        return ldsr.super_resolution(img, ddim_steps, self.scale)
diff --git a/modules/ldsr_model_arch.py b/modules/ldsr_model_arch.py
new file mode 100644
index 00000000..f8f3c3d3
--- /dev/null
+++ b/modules/ldsr_model_arch.py
@@ -0,0 +1,225 @@
+import gc
+import time
+import warnings
+
+import numpy as np
+import torch
+import torchvision
+from PIL import Image
+from einops import rearrange, repeat
+from omegaconf import OmegaConf
+
+from ldm.models.diffusion.ddim import DDIMSampler
+from ldm.util import instantiate_from_config, ismap
+
+warnings.filterwarnings("ignore", category=UserWarning)
+
+
+# Create LDSR Class
+class LDSR:
+    def load_model_from_config(self, half_attention):
+        print(f"Loading model from {self.modelPath}")
+        pl_sd = torch.load(self.modelPath, map_location="cpu")
+        sd = pl_sd["state_dict"]
+        config = OmegaConf.load(self.yamlPath)
+        model = instantiate_from_config(config.model)
+        model.load_state_dict(sd, strict=False)
+        model.cuda()
+        if half_attention:
+            model = model.half()
+
+        model.eval()
+        return {"model": model}
+
+    def __init__(self, model_path, yaml_path):
+        self.modelPath = model_path
+        self.yamlPath = yaml_path
+
+    @staticmethod
+    def run(model, selected_path, custom_steps, eta):
+        example = get_cond(selected_path)
+
+        n_runs = 1
+        guider = None
+        ckwargs = None
+        ddim_use_x0_pred = False
+        temperature = 1.
+        eta = eta
+        custom_shape = None
+
+        height, width = example["image"].shape[1:3]
+        split_input = height >= 128 and width >= 128
+
+        if split_input:
+            ks = 128
+            stride = 64
+            vqf = 4  #
+            model.split_input_params = {"ks": (ks, ks), "stride": (stride, stride),
+                                        "vqf": vqf,
+                                        "patch_distributed_vq": True,
+                                        "tie_braker": False,
+                                        "clip_max_weight": 0.5,
+                                        "clip_min_weight": 0.01,
+                                        "clip_max_tie_weight": 0.5,
+                                        "clip_min_tie_weight": 0.01}
+        else:
+            if hasattr(model, "split_input_params"):
+                delattr(model, "split_input_params")
+
+        x_t = None
+        logs = None
+        for n in range(n_runs):
+            if custom_shape is not None:
+                x_t = torch.randn(1, custom_shape[1], custom_shape[2], custom_shape[3]).to(model.device)
+                x_t = repeat(x_t, '1 c h w -> b c h w', b=custom_shape[0])
+
+            logs = make_convolutional_sample(example, model,
+                                             custom_steps=custom_steps,
+                                             eta=eta, quantize_x0=False,
+                                             custom_shape=custom_shape,
+                                             temperature=temperature, noise_dropout=0.,
+                                             corrector=guider, corrector_kwargs=ckwargs, x_T=x_t,
+                                             ddim_use_x0_pred=ddim_use_x0_pred
+                                             )
+        return logs
+
+    def super_resolution(self, image, steps=100, target_scale=2, half_attention=False):
+        model = self.load_model_from_config(half_attention)
+
+        # Run settings
+        diffusion_steps = int(steps)
+        eta = 1.0
+
+        down_sample_method = 'Lanczos'
+
+        gc.collect()
+        torch.cuda.empty_cache()
+
+        im_og = image
+        width_og, height_og = im_og.size
+        # If we can adjust the max upscale size, then the 4 below should be our variable
+        print("Foo")
+        down_sample_rate = target_scale / 4
+        print(f"Downsample rate is {down_sample_rate}")
+        wd = width_og * down_sample_rate
+        hd = height_og * down_sample_rate
+        width_downsampled_pre = int(wd)
+        height_downsampled_pre = int(hd)
+
+        if down_sample_rate != 1:
+            print(
+                f'Downsampling from [{width_og}, {height_og}] to [{width_downsampled_pre}, {height_downsampled_pre}]')
+            im_og = im_og.resize((width_downsampled_pre, height_downsampled_pre), Image.LANCZOS)
+        else:
+            print(f"Down sample rate is 1 from {target_scale} / 4")
+        logs = self.run(model["model"], im_og, diffusion_steps, eta)
+
+        sample = logs["sample"]
+        sample = sample.detach().cpu()
+        sample = torch.clamp(sample, -1., 1.)
+        sample = (sample + 1.) / 2. * 255
+        sample = sample.numpy().astype(np.uint8)
+        sample = np.transpose(sample, (0, 2, 3, 1))
+        a = Image.fromarray(sample[0])
+
+        del model
+        gc.collect()
+        torch.cuda.empty_cache()
+        print(f'Processing finished!')
+        return a
+
+
+def get_cond(selected_path):
+    example = dict()
+    up_f = 4
+    c = selected_path.convert('RGB')
+    c = torch.unsqueeze(torchvision.transforms.ToTensor()(c), 0)
+    c_up = torchvision.transforms.functional.resize(c, size=[up_f * c.shape[2], up_f * c.shape[3]],
+                                                    antialias=True)
+    c_up = rearrange(c_up, '1 c h w -> 1 h w c')
+    c = rearrange(c, '1 c h w -> 1 h w c')
+    c = 2. * c - 1.
+
+    c = c.to(torch.device("cuda"))
+    example["LR_image"] = c
+    example["image"] = c_up
+
+    return example
+
+
+@torch.no_grad()
+def convsample_ddim(model, cond, steps, shape, eta=1.0, callback=None, normals_sequence=None,
+                    mask=None, x0=None, quantize_x0=False, temperature=1., score_corrector=None,
+                    corrector_kwargs=None, x_t=None
+                    ):
+    ddim = DDIMSampler(model)
+    bs = shape[0]
+    shape = shape[1:]
+    print(f"Sampling with eta = {eta}; steps: {steps}")
+    samples, intermediates = ddim.sample(steps, batch_size=bs, shape=shape, conditioning=cond, callback=callback,
+                                         normals_sequence=normals_sequence, quantize_x0=quantize_x0, eta=eta,
+                                         mask=mask, x0=x0, temperature=temperature, verbose=False,
+                                         score_corrector=score_corrector,
+                                         corrector_kwargs=corrector_kwargs, x_t=x_t)
+
+    return samples, intermediates
+
+
+@torch.no_grad()
+def make_convolutional_sample(batch, model, custom_steps=None, eta=1.0, quantize_x0=False, custom_shape=None, temperature=1., noise_dropout=0., corrector=None,
+                              corrector_kwargs=None, x_T=None, ddim_use_x0_pred=False):
+    log = dict()
+
+    z, c, x, xrec, xc = model.get_input(batch, model.first_stage_key,
+                                        return_first_stage_outputs=True,
+                                        force_c_encode=not (hasattr(model, 'split_input_params')
+                                                            and model.cond_stage_key == 'coordinates_bbox'),
+                                        return_original_cond=True)
+
+    if custom_shape is not None:
+        z = torch.randn(custom_shape)
+        print(f"Generating {custom_shape[0]} samples of shape {custom_shape[1:]}")
+
+    z0 = None
+
+    log["input"] = x
+    log["reconstruction"] = xrec
+
+    if ismap(xc):
+        log["original_conditioning"] = model.to_rgb(xc)
+        if hasattr(model, 'cond_stage_key'):
+            log[model.cond_stage_key] = model.to_rgb(xc)
+
+    else:
+        log["original_conditioning"] = xc if xc is not None else torch.zeros_like(x)
+        if model.cond_stage_model:
+            log[model.cond_stage_key] = xc if xc is not None else torch.zeros_like(x)
+            if model.cond_stage_key == 'class_label':
+                log[model.cond_stage_key] = xc[model.cond_stage_key]
+
+    with model.ema_scope("Plotting"):
+        t0 = time.time()
+
+        sample, intermediates = convsample_ddim(model, c, steps=custom_steps, shape=z.shape,
+                                                eta=eta,
+                                                quantize_x0=quantize_x0, mask=None, x0=z0,
+                                                temperature=temperature, score_corrector=corrector, corrector_kwargs=corrector_kwargs,
+                                                x_t=x_T)
+        t1 = time.time()
+
+        if ddim_use_x0_pred:
+            sample = intermediates['pred_x0'][-1]
+
+    x_sample = model.decode_first_stage(sample)
+
+    try:
+        x_sample_noquant = model.decode_first_stage(sample, force_not_quantize=True)
+        log["sample_noquant"] = x_sample_noquant
+        log["sample_diff"] = torch.abs(x_sample_noquant - x_sample)
+    except:
+        pass
+
+    log["sample"] = x_sample
+    log["time"] = t1 - t0
+
+    return log
diff --git a/modules/modelloader.py b/modules/modelloader.py
new file mode 100644
index 00000000..51b3ecd5
--- /dev/null
+++ b/modules/modelloader.py
@@ -0,0 +1,133 @@
+import os
+import shutil
+import importlib
+from urllib.parse import urlparse
+
+from basicsr.utils.download_util import load_file_from_url
+
+from modules import shared
+from modules.upscaler import Upscaler
+from modules.paths import script_path, models_path
+
+
+def load_models(model_path: str, model_url: str = None, command_path: str = None, ext_filter=None, download_name=None) -> list:
+    """
+    A one-and done loader to try finding the desired models in specified directories.
+
+    @param download_name: Specify to download from model_url immediately.
+    @param model_url: If no other models are found, this will be downloaded on upscale.
+    @param model_path: The location to store/find models in.
+    @param command_path: A command-line argument to search for models in first.
+    @param ext_filter: An optional list of filename extensions to filter by
+    @return: A list of paths containing the desired model(s)
+    """
+    output = []
+
+    if ext_filter is None:
+        ext_filter = []
+    try:
+        places = []
+        if command_path is not None and command_path != model_path:
+            pretrained_path = os.path.join(command_path, 'experiments/pretrained_models')
+            if os.path.exists(pretrained_path):
+                print(f"Appending path: {pretrained_path}")
+                places.append(pretrained_path)
+            elif os.path.exists(command_path):
+                places.append(command_path)
+        places.append(model_path)
+        for place in places:
+            if os.path.exists(place):
+                for file in os.listdir(place):
+                    full_path = os.path.join(place, file)
+                    if os.path.isdir(full_path):
+                        continue
+                    if len(ext_filter) != 0:
+                        model_name, extension = os.path.splitext(file)
+                        if extension not in ext_filter:
+                            continue
+                    if file not in output:
+                        output.append(full_path)
+        if model_url is not None and len(output) == 0:
+            if download_name is not None:
+                dl = load_file_from_url(model_url, model_path, True, download_name)
+                output.append(dl)
+            else:
+                output.append(model_url)
+    except:
+        pass
+    return output
+
+
+def friendly_name(file: str):
+    if "http" in file:
+        file = urlparse(file).path
+
+    file = os.path.basename(file)
+    model_name, extension = os.path.splitext(file)
+    model_name = model_name.replace("_", " ").title()
+    return model_name
+
+
+def cleanup_models():
+    # This code could probably be more efficient if we used a tuple list or something to store the src/destinations
+    # and then enumerate that, but this works for now. In the future, it'd be nice to just have every "model" scaler
+    # somehow auto-register and just do these things...
+    root_path = script_path
+    src_path = models_path
+    dest_path = os.path.join(models_path, "Stable-diffusion")
+    move_files(src_path, dest_path, ".ckpt")
+    src_path = os.path.join(root_path, "ESRGAN")
+    dest_path = os.path.join(models_path, "ESRGAN")
+    move_files(src_path, dest_path)
+    src_path = os.path.join(root_path, "gfpgan")
+    dest_path = os.path.join(models_path, "GFPGAN")
+    move_files(src_path, dest_path)
+    src_path = os.path.join(root_path, "SwinIR")
+    dest_path = os.path.join(models_path, "SwinIR")
+    move_files(src_path, dest_path)
+    src_path = os.path.join(root_path, "repositories/latent-diffusion/experiments/pretrained_models/")
+    dest_path = os.path.join(models_path, "LDSR")
+    move_files(src_path, dest_path)
+
+
+def move_files(src_path: str, dest_path: str, ext_filter: str = None):
+    try:
+        if not os.path.exists(dest_path):
+            os.makedirs(dest_path)
+        if os.path.exists(src_path):
+            for file in os.listdir(src_path):
+                fullpath = os.path.join(src_path, file)
+                if os.path.isfile(fullpath):
+                    if ext_filter is not None:
+                        if ext_filter not in file:
+                            continue
+                    print(f"Moving {file} from {src_path} to {dest_path}.")
+                    try:
+                        shutil.move(fullpath, dest_path)
+                    except:
+                        pass
+            if len(os.listdir(src_path)) == 0:
+                print(f"Removing empty folder: {src_path}")
+                shutil.rmtree(src_path, True)
+    except:
+        pass
+
+
+def load_upscalers():
+    datas = []
+    for cls in Upscaler.__subclasses__():
+        name = cls.__name__
+        module_name = cls.__module__
+        module = importlib.import_module(module_name)
+        class_ = getattr(module, name)
+        cmd_name = f"{name.lower().replace('upscaler', '')}-models-path"
+        opt_string = None
+        try:
+            opt_string = shared.opts.__getattr__(cmd_name)
+        except:
+            pass
+        scaler = class_(opt_string)
+        for child in scaler.scalers:
+            datas.append(child)
+
+    shared.sd_upscalers = datas
diff --git a/modules/paths.py b/modules/paths.py
index 484f04ca..ceb80417 100644
--- a/modules/paths.py
+++ b/modules/paths.py
@@ -3,9 +3,10 @@ import os
 import sys

 

 script_path = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))

+models_path = os.path.join(script_path, "models")

 sys.path.insert(0, script_path)

 

-# search for directory of stable diffsuion in following palces

+# search for directory of stable diffusion in following places

 sd_path = None

 possible_sd_paths = [os.path.join(script_path, 'repositories/stable-diffusion'), '.', os.path.dirname(script_path)]

 for possible_sd_path in possible_sd_paths:

diff --git a/modules/realesrgan_model.py b/modules/realesrgan_model.py
index c32d6c4c..0a2eb896 100644
--- a/modules/realesrgan_model.py
+++ b/modules/realesrgan_model.py
@@ -1,119 +1,139 @@
+import os

 import sys

 import traceback

-from collections import namedtuple

 

 import numpy as np

 from PIL import Image

+from basicsr.utils.download_util import load_file_from_url

 from realesrgan import RealESRGANer

 

-import modules.images

+from modules.upscaler import Upscaler, UpscalerData

+from modules.paths import models_path

 from modules.shared import cmd_opts, opts

 

-RealesrganModelInfo = namedtuple("RealesrganModelInfo", ["name", "location", "model", "netscale"])

-realesrgan_models = []

-have_realesrgan = False

 

+class UpscalerRealESRGAN(Upscaler):

+    def __init__(self, path):

+        self.name = "RealESRGAN"

+        self.model_path = os.path.join(models_path, self.name)

+        self.user_path = path

+        super().__init__()

+        try:

+            from basicsr.archs.rrdbnet_arch import RRDBNet

+            from realesrgan import RealESRGANer

+            from realesrgan.archs.srvgg_arch import SRVGGNetCompact

+            self.enable = True

+            self.scalers = []

+            scalers = self.load_models(path)

+            for scaler in scalers:

+                if scaler.name in opts.realesrgan_enabled_models:

+                    self.scalers.append(scaler)

+

+        except Exception:

+            print("Error importing Real-ESRGAN:", file=sys.stderr)

+            print(traceback.format_exc(), file=sys.stderr)

+            self.enable = False

+            self.scalers = []

+

+    def do_upscale(self, img, path):

+        if not self.enable:

+            return img

+

+        info = self.load_model(path)

+        if not os.path.exists(info.data_path):

+            print("Unable to load RealESRGAN model: %s" % info.name)

+            return img

+

+        upsampler = RealESRGANer(

+            scale=info.scale,

+            model_path=info.data_path,

+            model=info.model(),

+            half=not cmd_opts.no_half,

+            tile=opts.ESRGAN_tile,

+            tile_pad=opts.ESRGAN_tile_overlap,

+        )

+

+        upsampled = upsampler.enhance(np.array(img), outscale=info.scale)[0]

+

+        image = Image.fromarray(upsampled)

+        return image

+

+    def load_model(self, path):

+        try:

+            info = None

+            for scaler in self.scalers:

+                if scaler.data_path == path:

+                    info = scaler

+

+            if info is None:

+                print(f"Unable to find model info: {path}")

+                return None

+

+            model_file = load_file_from_url(url=info.data_path, model_dir=self.model_path, progress=True)

+            info.data_path = model_file

+            return info

+        except Exception as e:

+            print(f"Error making Real-ESRGAN models list: {e}", file=sys.stderr)

+            print(traceback.format_exc(), file=sys.stderr)

+        return None

 

-def get_realesrgan_models():

+    def load_models(self, _):

+        return get_realesrgan_models(self)

+

+

+def get_realesrgan_models(scaler):

     try:

         from basicsr.archs.rrdbnet_arch import RRDBNet

-        from realesrgan import RealESRGANer

         from realesrgan.archs.srvgg_arch import SRVGGNetCompact

         models = [

-            RealesrganModelInfo(

-                name="Real-ESRGAN General x4x3",

-                location="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth",

-                netscale=4,

-                model=lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')

+            UpscalerData(

+                name="R-ESRGAN General 4xV3",

+                path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3"

+                     ".pth",

+                scale=4,

+                upscaler=scaler,

+                model=lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4,

+                                              act_type='prelu')

             ),

-            RealesrganModelInfo(

-                name="Real-ESRGAN General WDN x4x3",

-                location="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth",

-                netscale=4,

-                model=lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')

+            UpscalerData(

+                name="R-ESRGAN General WDN 4xV3",

+                path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth",

+                scale=4,

+                upscaler=scaler,

+                model=lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4,

+                                              act_type='prelu')

             ),

-            RealesrganModelInfo(

-                name="Real-ESRGAN AnimeVideo",

-                location="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth",

-                netscale=4,

-                model=lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu')

+            UpscalerData(

+                name="R-ESRGAN AnimeVideo",

+                path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth",

+                scale=4,

+                upscaler=scaler,

+                model=lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4,

+                                              act_type='prelu')

             ),

-            RealesrganModelInfo(

-                name="Real-ESRGAN 4x plus",

-                location="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",

-                netscale=4,

+            UpscalerData(

+                name="R-ESRGAN 4x+",

+                path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",

+                scale=4,

+                upscaler=scaler,

                 model=lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)

             ),

-            RealesrganModelInfo(

-                name="Real-ESRGAN 4x plus anime 6B",

-                location="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth",

-                netscale=4,

+            UpscalerData(

+                name="R-ESRGAN 4x+ Anime6B",

+                path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth",

+                scale=4,

+                upscaler=scaler,

                 model=lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)

             ),

-            RealesrganModelInfo(

-                name="Real-ESRGAN 2x plus",

-                location="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth",

-                netscale=2,

+            UpscalerData(

+                name="R-ESRGAN 2x+",

+                path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth",

+                scale=2,

+                upscaler=scaler,

                 model=lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)

             ),

         ]

         return models

     except Exception as e:

-        print("Error makeing Real-ESRGAN midels list:", file=sys.stderr)

+        print("Error making Real-ESRGAN models list:", file=sys.stderr)

         print(traceback.format_exc(), file=sys.stderr)

-

-

-class UpscalerRealESRGAN(modules.images.Upscaler):

-    def __init__(self, upscaling, model_index):

-        self.upscaling = upscaling

-        self.model_index = model_index

-        self.name = realesrgan_models[model_index].name

-

-    def do_upscale(self, img):

-        return upscale_with_realesrgan(img, self.upscaling, self.model_index)

-

-

-def setup_realesrgan():

-    global realesrgan_models

-    global have_realesrgan

-

-    try:

-        from basicsr.archs.rrdbnet_arch import RRDBNet

-        from realesrgan import RealESRGANer

-        from realesrgan.archs.srvgg_arch import SRVGGNetCompact

-

-        realesrgan_models = get_realesrgan_models()

-        have_realesrgan = True

-

-        for i, model in enumerate(realesrgan_models):

-            if model.name in opts.realesrgan_enabled_models:

-                modules.shared.sd_upscalers.append(UpscalerRealESRGAN(model.netscale, i))

-

-    except Exception:

-        print("Error importing Real-ESRGAN:", file=sys.stderr)

-        print(traceback.format_exc(), file=sys.stderr)

-

-        realesrgan_models = [RealesrganModelInfo('None', '', 0, None)]

-        have_realesrgan = False

-

-

-def upscale_with_realesrgan(image, RealESRGAN_upscaling, RealESRGAN_model_index):

-    if not have_realesrgan:

-        return image

-

-    info = realesrgan_models[RealESRGAN_model_index]

-

-    model = info.model()

-    upsampler = RealESRGANer(

-        scale=info.netscale,

-        model_path=info.location,

-        model=model,

-        half=not cmd_opts.no_half,

-        tile=opts.ESRGAN_tile,

-        tile_pad=opts.ESRGAN_tile_overlap,

-    )

-

-    upsampled = upsampler.enhance(np.array(image), outscale=RealESRGAN_upscaling)[0]

-

-    image = Image.fromarray(upsampled)

-    return image

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 0e7ed905..3f3f6b7c 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -8,7 +8,14 @@ from omegaconf import OmegaConf
 

 from ldm.util import instantiate_from_config

 

-from modules import shared

+from modules import shared, modelloader

+from modules.paths import models_path

+

+model_dir = "Stable-diffusion"

+model_path = os.path.join(models_path, model_dir)

+model_name = "sd-v1-4.ckpt"

+model_url = "https://drive.yerf.org/wl/?id=EBfTrmcCCUAGaQBXVIj5lJmEhjoP1tgl&mode=grid&download=1"

+user_dir = None

 

 CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name'])

 checkpoints_list = {}

@@ -23,14 +30,47 @@ except Exception:
     pass

 

 

+def modeltitle(path, h):

+    abspath = os.path.abspath(path)

+

+    if abspath.startswith(model_dir):

+        name = abspath.replace(model_dir, '')

+    else:

+        name = os.path.basename(path)

+

+    if name.startswith("\\") or name.startswith("/"):

+        name = name[1:]

+

+    return f'{name} [{h}]'

+

+

+def setup_model(dirname):

+    global model_path

+    global model_name

+    global model_url

+    global user_dir

+    global model_list

+    user_dir = dirname

+    if not os.path.exists(model_path):

+        os.makedirs(model_path)

+    checkpoints_list.clear()

+    list_models()

+

+

 def checkpoint_tiles():

     return sorted([x.title for x in checkpoints_list.values()])

 

 

 def list_models():

+    global model_path

+    global model_url

+    global model_name

+    global user_dir

     checkpoints_list.clear()

-

-    model_dir = os.path.abspath(shared.cmd_opts.ckpt_dir)

+    model_list = modelloader.load_models(model_path=model_path,model_url=model_url,command_path= user_dir,

+                                         ext_filter=[".ckpt"], download_name=model_name)

+    print(f"Model list: {model_list}")

+    model_dir = os.path.abspath(model_path)

 

     def modeltitle(path, h):

         abspath = os.path.abspath(path)

@@ -53,13 +93,11 @@ def list_models():
         title, model_name = modeltitle(cmd_ckpt, h)

         checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, model_name)

     elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file:

-        print(f"Checkpoint in --ckpt argument not found: {cmd_ckpt}", file=sys.stderr)

-

-    if os.path.exists(model_dir):

-        for filename in glob.glob(model_dir + '/**/*.ckpt', recursive=True):

-            h = model_hash(filename)

-            title, model_name = modeltitle(filename, h)

-            checkpoints_list[title] = CheckpointInfo(filename, title, h, model_name)

+        print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr)

+    for filename in model_list:

+        h = model_hash(filename)

+        title = modeltitle(filename, h)

+        checkpoints_list[title] = CheckpointInfo(filename, title, h, model_name)

 

 def get_closet_checkpoint_match(searchString):

     applicable = sorted([info for info in checkpoints_list.values() if searchString in info.title], key = lambda x:len(x.title))

@@ -69,6 +107,7 @@ def get_closet_checkpoint_match(searchString):
 

 def model_hash(filename):

     try:

+        print(f"Opening: {filename}")

         with open(filename, "rb") as file:

             import hashlib

             m = hashlib.sha256()

@@ -89,7 +128,7 @@ def select_checkpoint():
     if len(checkpoints_list) == 0:

         print(f"No checkpoints found. When searching for checkpoints, looked at:", file=sys.stderr)

         print(f" - file {os.path.abspath(shared.cmd_opts.ckpt)}", file=sys.stderr)

-        print(f" - directory {os.path.abspath(shared.cmd_opts.ckpt_dir)}", file=sys.stderr)

+        print(f" - directory {os.path.abspath(shared.cmd_opts.stablediffusion_models_path)}", file=sys.stderr)

         print(f"Can't run without a checkpoint. Find and place a .ckpt file into any of those locations. The program will exit.", file=sys.stderr)

         exit(1)

 

diff --git a/modules/shared.py b/modules/shared.py
index f88c2b02..69002158 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -1,26 +1,28 @@
-import sys

 import argparse

+import datetime

 import json

 import os

+import sys

+

 import gradio as gr

 import tqdm

-import datetime

 

 import modules.artists

-from modules.paths import script_path, sd_path

-from modules.devices import get_optimal_device

-import modules.styles

 import modules.interrogate

 import modules.memmon

 import modules.sd_models

+import modules.styles

+from modules.devices import get_optimal_device

+from modules.paths import script_path, sd_path

 

 sd_model_file = os.path.join(script_path, 'model.ckpt')

 default_sd_model_file = sd_model_file

-

+model_path = os.path.join(script_path, 'models')

 parser = argparse.ArgumentParser()

 parser.add_argument("--config", type=str, default=os.path.join(sd_path, "configs/stable-diffusion/v1-inference.yaml"), help="path to config which constructs model",)

 parser.add_argument("--ckpt", type=str, default=sd_model_file, help="path to checkpoint of stable diffusion model; this checkpoint will be added to the list of checkpoints and loaded by default if you don't have a checkpoint selected in settings",)

-parser.add_argument("--ckpt-dir", type=str, default=os.path.join(script_path, 'models'), help="path to directory with stable diffusion checkpoints",)

+# This should be deprecated, but we'll leave it for a few iterations

+parser.add_argument("--ckpt-dir", type=str, default=None, help="Path to directory with stable diffusion checkpoints (Deprecated, use '--stablediffusion-models-path'", )

 parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN'))

 parser.add_argument("--gfpgan-model", type=str, help="GFPGAN model file name", default=None)

 parser.add_argument("--no-half", action='store_true', help="do not switch the model to 16-bit floats")

@@ -34,8 +36,14 @@ parser.add_argument("--always-batch-cond-uncond", action='store_true', help="dis
 parser.add_argument("--unload-gfpgan", action='store_true', help="does not do anything.")

 parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")

 parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site (doesn't work for me but you might have better luck)")

-parser.add_argument("--esrgan-models-path", type=str, help="path to directory with ESRGAN models", default=os.path.join(script_path, 'ESRGAN'))

-parser.add_argument("--swinir-models-path", type=str, help="path to directory with SwinIR models", default=os.path.join(script_path, 'SwinIR'))

+parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(model_path, 'Codeformer'))

+parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model file(s).", default=os.path.join(model_path, 'GFPGAN'))

+parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(model_path, 'ESRGAN'))

+parser.add_argument("--bsrgan-models-path", type=str, help="Path to directory with BSRGAN model file(s).", default=os.path.join(model_path, 'BSRGAN'))

+parser.add_argument("--realesrgan-models-path", type=str, help="Path to directory with RealESRGAN model file(s).", default=os.path.join(model_path, 'RealESRGAN'))

+parser.add_argument("--stablediffusion-models-path", type=str, help="Path to directory with Stable-diffusion checkpoints.", default=os.path.join(model_path, 'SwinIR'))

+parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(model_path, 'SwinIR'))

+parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(model_path, 'LDSR'))

 parser.add_argument("--opt-split-attention", action='store_true', help="force-enables cross-attention layer optimization. By default, it's on for torch.cuda and off for other torch devices.")

 parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")

 parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")

@@ -53,7 +61,10 @@ parser.add_argument("--autolaunch", action='store_true', help="open the webui UR
 parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False)

 

 cmd_opts = parser.parse_args()

-

+if cmd_opts.ckpt_dir is not None:

+    print("The 'ckpt-dir' arg is deprecated in favor of the 'stablediffusion-models-path' argument and will be "

+          "removed in a future release. Please use the new option if you wish to use a custom checkpoint directory.")

+    cmd_opts.__setattr__("stablediffusion-models-path", cmd_opts.ckpt_dir)

 device = get_optimal_device()

 

 batch_cond_uncond = cmd_opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram)

@@ -61,6 +72,7 @@ parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram
 

 config_filename = cmd_opts.ui_settings_file

 

+

 class State:

     interrupted = False

     job = ""

@@ -95,13 +107,13 @@ prompt_styles = modules.styles.StyleDatabase(styles_filename)
 interrogator = modules.interrogate.InterrogateModels("interrogate")

 

 face_restorers = []

-

-modules.sd_models.list_models()

+# This was moved to webui.py with the other model "setup" calls.

+# modules.sd_models.list_models()

 

 

 def realesrgan_models_names():

     import modules.realesrgan_model

-    return [x.name for x in modules.realesrgan_model.get_realesrgan_models()]

+    return [x.name for x in modules.realesrgan_model.get_realesrgan_models(None)]

 

 

 class OptionInfo:

@@ -167,13 +179,10 @@ options_templates.update(options_section(('saving-to-dirs', "Saving to a directo
 options_templates.update(options_section(('upscaling', "Upscaling"), {

     "ESRGAN_tile": OptionInfo(192, "Tile size for ESRGAN upscalers. 0 = no tiling.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),

     "ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),

-    "realesrgan_enabled_models": OptionInfo(["Real-ESRGAN 4x plus", "Real-ESRGAN 4x plus anime 6B"], "Select which RealESRGAN models to show in the web UI. (Requires restart)", gr.CheckboxGroup, lambda: {"choices": realesrgan_models_names()}),

+    "realesrgan_enabled_models": OptionInfo(["R-ESRGAN x4+", "R-ESRGAN x4+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI. (Requires restart)", gr.CheckboxGroup, lambda: {"choices": realesrgan_models_names()}),

     "SWIN_tile": OptionInfo(192, "Tile size for all SwinIR.", gr.Slider, {"minimum": 16, "maximum": 512, "step": 16}),

     "SWIN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for SwinIR. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),

     "ldsr_steps": OptionInfo(100, "LDSR processing steps. Lower = faster", gr.Slider, {"minimum": 1, "maximum": 200, "step": 1}),

-    "ldsr_pre_down": OptionInfo(1, "LDSR Pre-process down-sample scale. 1 = no down-sampling, 4 = 1/4 scale.", gr.Slider, {"minimum": 1, "maximum": 4, "step": 1}),

-    "ldsr_post_down": OptionInfo(1, "LDSR Post-process down-sample scale. 1 = no down-sampling, 4 = 1/4 scale.", gr.Slider, {"minimum": 1, "maximum": 4, "step": 1}),

-

     "upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Radio, lambda: {"choices": [x.name for x in sd_upscalers]}),

 }))

 

@@ -192,7 +201,7 @@ options_templates.update(options_section(('system', "System"), {
 options_templates.update(options_section(('sd', "Stable Diffusion"), {

     "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Radio, lambda: {"choices": modules.sd_models.checkpoint_tiles()}),

     "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),

-    "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),    

+    "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),

     "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),

     "enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply."),

     "enable_emphasis": OptionInfo(True, "Eemphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention"),

diff --git a/modules/swinir.py b/modules/swinir.py
deleted file mode 100644
index 8c534495..00000000
--- a/modules/swinir.py
+++ /dev/null
@@ -1,123 +0,0 @@
-import sys

-import traceback

-import cv2

-import os

-import contextlib

-import numpy as np

-from PIL import Image

-import torch

-import modules.images

-from modules.shared import cmd_opts, opts, device

-from modules.swinir_arch import SwinIR as net

-

-precision_scope = (

-    torch.autocast if cmd_opts.precision == "autocast" else contextlib.nullcontext

-)

-

-

-def load_model(filename, scale=4):

-    model = net(

-        upscale=scale,

-        in_chans=3,

-        img_size=64,

-        window_size=8,

-        img_range=1.0,

-        depths=[6, 6, 6, 6, 6, 6, 6, 6, 6],

-        embed_dim=240,

-        num_heads=[8, 8, 8, 8, 8, 8, 8, 8, 8],

-        mlp_ratio=2,

-        upsampler="nearest+conv",

-        resi_connection="3conv",

-    )

-

-    pretrained_model = torch.load(filename)

-    model.load_state_dict(pretrained_model["params_ema"], strict=True)

-    if not cmd_opts.no_half:

-        model = model.half()

-    return model

-

-

-def load_models(dirname):

-    for file in os.listdir(dirname):

-        path = os.path.join(dirname, file)

-        model_name, extension = os.path.splitext(file)

-

-        if extension != ".pt" and extension != ".pth":

-            continue

-

-        try:

-            modules.shared.sd_upscalers.append(UpscalerSwin(path, model_name))

-        except Exception:

-            print(f"Error loading SwinIR model: {path}", file=sys.stderr)

-            print(traceback.format_exc(), file=sys.stderr)

-

-

-def upscale(

-    img,

-    model,

-    tile=opts.SWIN_tile,

-    tile_overlap=opts.SWIN_tile_overlap,

-    window_size=8,

-    scale=4,

-):

-    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(device)

-    with torch.no_grad(), precision_scope("cuda"):

-        _, _, h_old, w_old = img.size()

-        h_pad = (h_old // window_size + 1) * window_size - h_old

-        w_pad = (w_old // window_size + 1) * window_size - w_old

-        img = torch.cat([img, torch.flip(img, [2])], 2)[:, :, : h_old + h_pad, :]

-        img = torch.cat([img, torch.flip(img, [3])], 3)[:, :, :, : w_old + w_pad]

-        output = inference(img, model, tile, tile_overlap, window_size, scale)

-        output = output[..., : h_old * scale, : w_old * scale]

-        output = output.data.squeeze().float().cpu().clamp_(0, 1).numpy()

-        if output.ndim == 3:

-            output = np.transpose(

-                output[[2, 1, 0], :, :], (1, 2, 0)

-            )  # CHW-RGB to HCW-BGR

-        output = (output * 255.0).round().astype(np.uint8)  # float32 to uint8

-        return Image.fromarray(output, "RGB")

-

-

-def inference(img, model, tile, tile_overlap, window_size, scale):

-    # test the image tile by tile

-    b, c, h, w = img.size()

-    tile = min(tile, h, w)

-    assert tile % window_size == 0, "tile size should be a multiple of window_size"

-    sf = scale

-

-    stride = tile - tile_overlap

-    h_idx_list = list(range(0, h - tile, stride)) + [h - tile]

-    w_idx_list = list(range(0, w - tile, stride)) + [w - tile]

-    E = torch.zeros(b, c, h * sf, w * sf, dtype=torch.half, device=device).type_as(img)

-    W = torch.zeros_like(E, dtype=torch.half, device=device)

-

-    for h_idx in h_idx_list:

-        for w_idx in w_idx_list:

-            in_patch = img[..., h_idx : h_idx + tile, w_idx : w_idx + tile]

-            out_patch = model(in_patch)

-            out_patch_mask = torch.ones_like(out_patch)

-

-            E[

-                ..., h_idx * sf : (h_idx + tile) * sf, w_idx * sf : (w_idx + tile) * sf

-            ].add_(out_patch)

-            W[

-                ..., h_idx * sf : (h_idx + tile) * sf, w_idx * sf : (w_idx + tile) * sf

-            ].add_(out_patch_mask)

-    output = E.div_(W)

-

-    return output

-

-

-class UpscalerSwin(modules.images.Upscaler):

-    def __init__(self, filename, title):

-        self.name = title

-        self.model = load_model(filename)

-

-    def do_upscale(self, img):

-        model = self.model.to(device)

-        img = upscale(img, model)

-        return img

diff --git a/modules/swinir_model.py b/modules/swinir_model.py
new file mode 100644
index 00000000..41fda5a7
--- /dev/null
+++ b/modules/swinir_model.py
@@ -0,0 +1,139 @@
+import contextlib
+import os
+
+import numpy as np
+import torch
+from PIL import Image
+from basicsr.utils.download_util import load_file_from_url
+
+from modules import modelloader
+from modules.paths import models_path
+from modules.shared import cmd_opts, opts, device
+from modules.swinir_model_arch import SwinIR as net
+from modules.upscaler import Upscaler, UpscalerData
+
+precision_scope = (
+    torch.autocast if cmd_opts.precision == "autocast" else contextlib.nullcontext
+)
+
+
+class UpscalerSwinIR(Upscaler):
+    def __init__(self, dirname):
+        self.name = "SwinIR"
+        self.model_url = "https://github.com/JingyunLiang/SwinIR/releases/download/v0.0" \
+                         "/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR" \
+                         "-L_x4_GAN.pth "
+        self.model_name = "SwinIR 4x"
+        self.model_path = os.path.join(models_path, self.name)
+        self.user_path = dirname
+        super().__init__()
+        scalers = []
+        model_files = self.find_models(ext_filter=[".pt", ".pth"])
+        for model in model_files:
+            if "http" in model:
+                name = self.model_name
+            else:
+                name = modelloader.friendly_name(model)
+            model_data = UpscalerData(name, model, self)
+            scalers.append(model_data)
+        self.scalers = scalers
+
+    def do_upscale(self, img, model_file):
+        model = self.load_model(model_file)
+        if model is None:
+            return img
+        model = model.to(device)
+        img = upscale(img, model)
+        try:
+            torch.cuda.empty_cache()
+        except:
+            pass
+        return img
+
+    def load_model(self, path, scale=4):
+        if "http" in path:
+            dl_name = "%s%s" % (self.model_name.replace(" ", "_"), ".pth")
+            filename = load_file_from_url(url=path, model_dir=self.model_path, file_name=dl_name, progress=True)
+        else:
+            filename = path
+        if filename is None or not os.path.exists(filename):
+            return None
+        model = net(
+            upscale=scale,
+            in_chans=3,
+            img_size=64,
+            window_size=8,
+            img_range=1.0,
+            depths=[6, 6, 6, 6, 6, 6, 6, 6, 6],
+            embed_dim=240,
+            num_heads=[8, 8, 8, 8, 8, 8, 8, 8, 8],
+            mlp_ratio=2,
+            upsampler="nearest+conv",
+            resi_connection="3conv",
+        )
+
+        pretrained_model = torch.load(filename)
+        model.load_state_dict(pretrained_model["params_ema"], strict=True)
+        if not cmd_opts.no_half:
+            model = model.half()
+        return model
+
+
+def upscale(
+        img,
+        model,
+        tile=opts.SWIN_tile,
+        tile_overlap=opts.SWIN_tile_overlap,
+        window_size=8,
+        scale=4,
+):
+    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(device)
+    with torch.no_grad(), precision_scope("cuda"):
+        _, _, h_old, w_old = img.size()
+        h_pad = (h_old // window_size + 1) * window_size - h_old
+        w_pad = (w_old // window_size + 1) * window_size - w_old
+        img = torch.cat([img, torch.flip(img, [2])], 2)[:, :, : h_old + h_pad, :]
+        img = torch.cat([img, torch.flip(img, [3])], 3)[:, :, :, : w_old + w_pad]
+        output = inference(img, model, tile, tile_overlap, window_size, scale)
+        output = output[..., : h_old * scale, : w_old * scale]
+        output = output.data.squeeze().float().cpu().clamp_(0, 1).numpy()
+        if output.ndim == 3:
+            output = np.transpose(
+                output[[2, 1, 0], :, :], (1, 2, 0)
+            )  # CHW-RGB to HCW-BGR
+        output = (output * 255.0).round().astype(np.uint8)  # float32 to uint8
+        return Image.fromarray(output, "RGB")
+
+
+def inference(img, model, tile, tile_overlap, window_size, scale):
+    # test the image tile by tile
+    b, c, h, w = img.size()
+    tile = min(tile, h, w)
+    assert tile % window_size == 0, "tile size should be a multiple of window_size"
+    sf = scale
+
+    stride = tile - tile_overlap
+    h_idx_list = list(range(0, h - tile, stride)) + [h - tile]
+    w_idx_list = list(range(0, w - tile, stride)) + [w - tile]
+    E = torch.zeros(b, c, h * sf, w * sf, dtype=torch.half, device=device).type_as(img)
+    W = torch.zeros_like(E, dtype=torch.half, device=device)
+
+    for h_idx in h_idx_list:
+        for w_idx in w_idx_list:
+            in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile]
+            out_patch = model(in_patch)
+            out_patch_mask = torch.ones_like(out_patch)
+
+            E[
+            ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
+            ].add_(out_patch)
+            W[
+            ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
+            ].add_(out_patch_mask)
+    output = E.div_(W)
+
+    return output
diff --git a/modules/swinir_arch.py b/modules/swinir_model_arch.py
index a5eb9a36..461fb354 100644
--- a/modules/swinir_arch.py
+++ b/modules/swinir_model_arch.py
@@ -1,867 +1,867 @@
-# -----------------------------------------------------------------------------------

-# SwinIR: Image Restoration Using Swin Transformer, https://arxiv.org/abs/2108.10257

-# Originally Written by Ze Liu, Modified by Jingyun Liang.

-# -----------------------------------------------------------------------------------

-

-import math

-import torch

-import torch.nn as nn

-import torch.nn.functional as F

-import torch.utils.checkpoint as checkpoint

-from timm.models.layers import DropPath, to_2tuple, trunc_normal_

-

-

-class Mlp(nn.Module):

-    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):

-        super().__init__()

-        out_features = out_features or in_features

-        hidden_features = hidden_features or in_features

-        self.fc1 = nn.Linear(in_features, hidden_features)

-        self.act = act_layer()

-        self.fc2 = nn.Linear(hidden_features, out_features)

-        self.drop = nn.Dropout(drop)

-

-    def forward(self, x):

-        x = self.fc1(x)

-        x = self.act(x)

-        x = self.drop(x)

-        x = self.fc2(x)

-        x = self.drop(x)

-        return x

-

-

-def window_partition(x, window_size):

-    """

-    Args:

-        x: (B, H, W, C)

-        window_size (int): window size

-

-    Returns:

-        windows: (num_windows*B, window_size, window_size, C)

-    """

-    B, H, W, C = x.shape

-    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)

-    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)

-    return windows

-

-

-def window_reverse(windows, window_size, H, W):

-    """

-    Args:

-        windows: (num_windows*B, window_size, window_size, C)

-        window_size (int): Window size

-        H (int): Height of image

-        W (int): Width of image

-

-    Returns:

-        x: (B, H, W, C)

-    """

-    B = int(windows.shape[0] / (H * W / window_size / window_size))

-    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)

-    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)

-    return x

-

-

-class WindowAttention(nn.Module):

-    r""" Window based multi-head self attention (W-MSA) module with relative position bias.

-    It supports both of shifted and non-shifted window.

-

-    Args:

-        dim (int): Number of input channels.

-        window_size (tuple[int]): The height and width of the window.

-        num_heads (int): Number of attention heads.

-        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True

-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set

-        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0

-        proj_drop (float, optional): Dropout ratio of output. Default: 0.0

-    """

-

-    def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):

-

-        super().__init__()

-        self.dim = dim

-        self.window_size = window_size  # Wh, Ww

-        self.num_heads = num_heads

-        head_dim = dim // num_heads

-        self.scale = qk_scale or head_dim ** -0.5

-

-        # define a parameter table of relative position bias

-        self.relative_position_bias_table = nn.Parameter(

-            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads))  # 2*Wh-1 * 2*Ww-1, nH

-

-        # get pair-wise relative position index for each token inside the window

-        coords_h = torch.arange(self.window_size[0])

-        coords_w = torch.arange(self.window_size[1])

-        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww

-        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww

-        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww

-        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2

-        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0

-        relative_coords[:, :, 1] += self.window_size[1] - 1

-        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1

-        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww

-        self.register_buffer("relative_position_index", relative_position_index)

-

-        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)

-        self.attn_drop = nn.Dropout(attn_drop)

-        self.proj = nn.Linear(dim, dim)

-

-        self.proj_drop = nn.Dropout(proj_drop)

-

-        trunc_normal_(self.relative_position_bias_table, std=.02)

-        self.softmax = nn.Softmax(dim=-1)

-

-    def forward(self, x, mask=None):

-        """

-        Args:

-            x: input features with shape of (num_windows*B, N, C)

-            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None

-        """

-        B_, N, C = x.shape

-        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)

-        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)

-

-        q = q * self.scale

-        attn = (q @ k.transpose(-2, -1))

-

-        relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(

-            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH

-        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww

-        attn = attn + relative_position_bias.unsqueeze(0)

-

-        if mask is not None:

-            nW = mask.shape[0]

-            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)

-            attn = attn.view(-1, self.num_heads, N, N)

-            attn = self.softmax(attn)

-        else:

-            attn = self.softmax(attn)

-

-        attn = self.attn_drop(attn)

-

-        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)

-        x = self.proj(x)

-        x = self.proj_drop(x)

-        return x

-

-    def extra_repr(self) -> str:

-        return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'

-

-    def flops(self, N):

-        # calculate flops for 1 window with token length of N

-        flops = 0

-        # qkv = self.qkv(x)

-        flops += N * self.dim * 3 * self.dim

-        # attn = (q @ k.transpose(-2, -1))

-        flops += self.num_heads * N * (self.dim // self.num_heads) * N

-        #  x = (attn @ v)

-        flops += self.num_heads * N * N * (self.dim // self.num_heads)

-        # x = self.proj(x)

-        flops += N * self.dim * self.dim

-        return flops

-

-

-class SwinTransformerBlock(nn.Module):

-    r""" Swin Transformer Block.

-

-    Args:

-        dim (int): Number of input channels.

-        input_resolution (tuple[int]): Input resulotion.

-        num_heads (int): Number of attention heads.

-        window_size (int): Window size.

-        shift_size (int): Shift size for SW-MSA.

-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.

-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True

-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.

-        drop (float, optional): Dropout rate. Default: 0.0

-        attn_drop (float, optional): Attention dropout rate. Default: 0.0

-        drop_path (float, optional): Stochastic depth rate. Default: 0.0

-        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU

-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm

-    """

-

-    def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,

-                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,

-                 act_layer=nn.GELU, norm_layer=nn.LayerNorm):

-        super().__init__()

-        self.dim = dim

-        self.input_resolution = input_resolution

-        self.num_heads = num_heads

-        self.window_size = window_size

-        self.shift_size = shift_size

-        self.mlp_ratio = mlp_ratio

-        if min(self.input_resolution) <= self.window_size:

-            # if window size is larger than input resolution, we don't partition windows

-            self.shift_size = 0

-            self.window_size = min(self.input_resolution)

-        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"

-

-        self.norm1 = norm_layer(dim)

-        self.attn = WindowAttention(

-            dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,

-            qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)

-

-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()

-        self.norm2 = norm_layer(dim)

-        mlp_hidden_dim = int(dim * mlp_ratio)

-        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)

-

-        if self.shift_size > 0:

-            attn_mask = self.calculate_mask(self.input_resolution)

-        else:

-            attn_mask = None

-

-        self.register_buffer("attn_mask", attn_mask)

-

-    def calculate_mask(self, x_size):

-        # calculate attention mask for SW-MSA

-        H, W = x_size

-        img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1

-        h_slices = (slice(0, -self.window_size),

-                    slice(-self.window_size, -self.shift_size),

-                    slice(-self.shift_size, None))

-        w_slices = (slice(0, -self.window_size),

-                    slice(-self.window_size, -self.shift_size),

-                    slice(-self.shift_size, None))

-        cnt = 0

-        for h in h_slices:

-            for w in w_slices:

-                img_mask[:, h, w, :] = cnt

-                cnt += 1

-

-        mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1

-        mask_windows = mask_windows.view(-1, self.window_size * self.window_size)

-        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)

-        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))

-

-        return attn_mask

-

-    def forward(self, x, x_size):

-        H, W = x_size

-        B, L, C = x.shape

-        # assert L == H * W, "input feature has wrong size"

-

-        shortcut = x

-        x = self.norm1(x)

-        x = x.view(B, H, W, C)

-

-        # cyclic shift

-        if self.shift_size > 0:

-            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))

-        else:

-            shifted_x = x

-

-        # partition windows

-        x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C

-        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C

-

-        # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size

-        if self.input_resolution == x_size:

-            attn_windows = self.attn(x_windows, mask=self.attn_mask)  # nW*B, window_size*window_size, C

-        else:

-            attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device))

-

-        # merge windows

-        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)

-        shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C

-

-        # reverse cyclic shift

-        if self.shift_size > 0:

-            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))

-        else:

-            x = shifted_x

-        x = x.view(B, H * W, C)

-

-        # FFN

-        x = shortcut + self.drop_path(x)

-        x = x + self.drop_path(self.mlp(self.norm2(x)))

-

-        return x

-

-    def extra_repr(self) -> str:

-        return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \

-               f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"

-

-    def flops(self):

-        flops = 0

-        H, W = self.input_resolution

-        # norm1

-        flops += self.dim * H * W

-        # W-MSA/SW-MSA

-        nW = H * W / self.window_size / self.window_size

-        flops += nW * self.attn.flops(self.window_size * self.window_size)

-        # mlp

-        flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio

-        # norm2

-        flops += self.dim * H * W

-        return flops

-

-

-class PatchMerging(nn.Module):

-    r""" Patch Merging Layer.

-

-    Args:

-        input_resolution (tuple[int]): Resolution of input feature.

-        dim (int): Number of input channels.

-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm

-    """

-

-    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):

-        super().__init__()

-        self.input_resolution = input_resolution

-        self.dim = dim

-        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)

-        self.norm = norm_layer(4 * dim)

-

-    def forward(self, x):

-        """

-        x: B, H*W, C

-        """

-        H, W = self.input_resolution

-        B, L, C = x.shape

-        assert L == H * W, "input feature has wrong size"

-        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."

-

-        x = x.view(B, H, W, C)

-

-        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C

-        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C

-        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C

-        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C

-        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C

-        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C

-

-        x = self.norm(x)

-        x = self.reduction(x)

-

-        return x

-

-    def extra_repr(self) -> str:

-        return f"input_resolution={self.input_resolution}, dim={self.dim}"

-

-    def flops(self):

-        H, W = self.input_resolution

-        flops = H * W * self.dim

-        flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim

-        return flops

-

-

-class BasicLayer(nn.Module):

-    """ A basic Swin Transformer layer for one stage.

-

-    Args:

-        dim (int): Number of input channels.

-        input_resolution (tuple[int]): Input resolution.

-        depth (int): Number of blocks.

-        num_heads (int): Number of attention heads.

-        window_size (int): Local window size.

-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.

-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True

-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.

-        drop (float, optional): Dropout rate. Default: 0.0

-        attn_drop (float, optional): Attention dropout rate. Default: 0.0

-        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0

-        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm

-        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None

-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.

-    """

-

-    def __init__(self, dim, input_resolution, depth, num_heads, window_size,

-                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,

-                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False):

-

-        super().__init__()

-        self.dim = dim

-        self.input_resolution = input_resolution

-        self.depth = depth

-        self.use_checkpoint = use_checkpoint

-

-        # build blocks

-        self.blocks = nn.ModuleList([

-            SwinTransformerBlock(dim=dim, input_resolution=input_resolution,

-                                 num_heads=num_heads, window_size=window_size,

-                                 shift_size=0 if (i % 2 == 0) else window_size // 2,

-                                 mlp_ratio=mlp_ratio,

-                                 qkv_bias=qkv_bias, qk_scale=qk_scale,

-                                 drop=drop, attn_drop=attn_drop,

-                                 drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,

-                                 norm_layer=norm_layer)

-            for i in range(depth)])

-

-        # patch merging layer

-        if downsample is not None:

-            self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)

-        else:

-            self.downsample = None

-

-    def forward(self, x, x_size):

-        for blk in self.blocks:

-            if self.use_checkpoint:

-                x = checkpoint.checkpoint(blk, x, x_size)

-            else:

-                x = blk(x, x_size)

-        if self.downsample is not None:

-            x = self.downsample(x)

-        return x

-

-    def extra_repr(self) -> str:

-        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"

-

-    def flops(self):

-        flops = 0

-        for blk in self.blocks:

-            flops += blk.flops()

-        if self.downsample is not None:

-            flops += self.downsample.flops()

-        return flops

-

-

-class RSTB(nn.Module):

-    """Residual Swin Transformer Block (RSTB).

-

-    Args:

-        dim (int): Number of input channels.

-        input_resolution (tuple[int]): Input resolution.

-        depth (int): Number of blocks.

-        num_heads (int): Number of attention heads.

-        window_size (int): Local window size.

-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.

-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True

-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.

-        drop (float, optional): Dropout rate. Default: 0.0

-        attn_drop (float, optional): Attention dropout rate. Default: 0.0

-        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0

-        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm

-        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None

-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.

-        img_size: Input image size.

-        patch_size: Patch size.

-        resi_connection: The convolutional block before residual connection.

-    """

-

-    def __init__(self, dim, input_resolution, depth, num_heads, window_size,

-                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,

-                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,

-                 img_size=224, patch_size=4, resi_connection='1conv'):

-        super(RSTB, self).__init__()

-

-        self.dim = dim

-        self.input_resolution = input_resolution

-

-        self.residual_group = BasicLayer(dim=dim,

-                                         input_resolution=input_resolution,

-                                         depth=depth,

-                                         num_heads=num_heads,

-                                         window_size=window_size,

-                                         mlp_ratio=mlp_ratio,

-                                         qkv_bias=qkv_bias, qk_scale=qk_scale,

-                                         drop=drop, attn_drop=attn_drop,

-                                         drop_path=drop_path,

-                                         norm_layer=norm_layer,

-                                         downsample=downsample,

-                                         use_checkpoint=use_checkpoint)

-

-        if resi_connection == '1conv':

-            self.conv = nn.Conv2d(dim, dim, 3, 1, 1)

-        elif resi_connection == '3conv':

-            # to save parameters and memory

-            self.conv = nn.Sequential(nn.Conv2d(dim, dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True),

-                                      nn.Conv2d(dim // 4, dim // 4, 1, 1, 0),

-                                      nn.LeakyReLU(negative_slope=0.2, inplace=True),

-                                      nn.Conv2d(dim // 4, dim, 3, 1, 1))

-

-        self.patch_embed = PatchEmbed(

-            img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim,

-            norm_layer=None)

-

-        self.patch_unembed = PatchUnEmbed(

-            img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim,

-            norm_layer=None)

-

-    def forward(self, x, x_size):

-        return self.patch_embed(self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))) + x

-

-    def flops(self):

-        flops = 0

-        flops += self.residual_group.flops()

-        H, W = self.input_resolution

-        flops += H * W * self.dim * self.dim * 9

-        flops += self.patch_embed.flops()

-        flops += self.patch_unembed.flops()

-

-        return flops

-

-

-class PatchEmbed(nn.Module):

-    r""" Image to Patch Embedding

-

-    Args:

-        img_size (int): Image size.  Default: 224.

-        patch_size (int): Patch token size. Default: 4.

-        in_chans (int): Number of input image channels. Default: 3.

-        embed_dim (int): Number of linear projection output channels. Default: 96.

-        norm_layer (nn.Module, optional): Normalization layer. Default: None

-    """

-

-    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):

-        super().__init__()

-        img_size = to_2tuple(img_size)

-        patch_size = to_2tuple(patch_size)

-        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]

-        self.img_size = img_size

-        self.patch_size = patch_size

-        self.patches_resolution = patches_resolution

-        self.num_patches = patches_resolution[0] * patches_resolution[1]

-

-        self.in_chans = in_chans

-        self.embed_dim = embed_dim

-

-        if norm_layer is not None:

-            self.norm = norm_layer(embed_dim)

-        else:

-            self.norm = None

-

-    def forward(self, x):

-        x = x.flatten(2).transpose(1, 2)  # B Ph*Pw C

-        if self.norm is not None:

-            x = self.norm(x)

-        return x

-

-    def flops(self):

-        flops = 0

-        H, W = self.img_size

-        if self.norm is not None:

-            flops += H * W * self.embed_dim

-        return flops

-

-

-class PatchUnEmbed(nn.Module):

-    r""" Image to Patch Unembedding

-

-    Args:

-        img_size (int): Image size.  Default: 224.

-        patch_size (int): Patch token size. Default: 4.

-        in_chans (int): Number of input image channels. Default: 3.

-        embed_dim (int): Number of linear projection output channels. Default: 96.

-        norm_layer (nn.Module, optional): Normalization layer. Default: None

-    """

-

-    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):

-        super().__init__()

-        img_size = to_2tuple(img_size)

-        patch_size = to_2tuple(patch_size)

-        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]

-        self.img_size = img_size

-        self.patch_size = patch_size

-        self.patches_resolution = patches_resolution

-        self.num_patches = patches_resolution[0] * patches_resolution[1]

-

-        self.in_chans = in_chans

-        self.embed_dim = embed_dim

-

-    def forward(self, x, x_size):

-        B, HW, C = x.shape

-        x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1])  # B Ph*Pw C

-        return x

-

-    def flops(self):

-        flops = 0

-        return flops

-

-

-class Upsample(nn.Sequential):

-    """Upsample module.

-

-    Args:

-        scale (int): Scale factor. Supported scales: 2^n and 3.

-        num_feat (int): Channel number of intermediate features.

-    """

-

-    def __init__(self, scale, num_feat):

-        m = []

-        if (scale & (scale - 1)) == 0:  # scale = 2^n

-            for _ in range(int(math.log(scale, 2))):

-                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))

-                m.append(nn.PixelShuffle(2))

-        elif scale == 3:

-            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))

-            m.append(nn.PixelShuffle(3))

-        else:

-            raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')

-        super(Upsample, self).__init__(*m)

-

-

-class UpsampleOneStep(nn.Sequential):

-    """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle)

-       Used in lightweight SR to save parameters.

-

-    Args:

-        scale (int): Scale factor. Supported scales: 2^n and 3.

-        num_feat (int): Channel number of intermediate features.

-

-    """

-

-    def __init__(self, scale, num_feat, num_out_ch, input_resolution=None):

-        self.num_feat = num_feat

-        self.input_resolution = input_resolution

-        m = []

-        m.append(nn.Conv2d(num_feat, (scale ** 2) * num_out_ch, 3, 1, 1))

-        m.append(nn.PixelShuffle(scale))

-        super(UpsampleOneStep, self).__init__(*m)

-

-    def flops(self):

-        H, W = self.input_resolution

-        flops = H * W * self.num_feat * 3 * 9

-        return flops

-

-

-class SwinIR(nn.Module):

-    r""" SwinIR

-        A PyTorch impl of : `SwinIR: Image Restoration Using Swin Transformer`, based on Swin Transformer.

-

-    Args:

-        img_size (int | tuple(int)): Input image size. Default 64

-        patch_size (int | tuple(int)): Patch size. Default: 1

-        in_chans (int): Number of input image channels. Default: 3

-        embed_dim (int): Patch embedding dimension. Default: 96

-        depths (tuple(int)): Depth of each Swin Transformer layer.

-        num_heads (tuple(int)): Number of attention heads in different layers.

-        window_size (int): Window size. Default: 7

-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4

-        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True

-        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None

-        drop_rate (float): Dropout rate. Default: 0

-        attn_drop_rate (float): Attention dropout rate. Default: 0

-        drop_path_rate (float): Stochastic depth rate. Default: 0.1

-        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.

-        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False

-        patch_norm (bool): If True, add normalization after patch embedding. Default: True

-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False

-        upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction

-        img_range: Image range. 1. or 255.

-        upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None

-        resi_connection: The convolutional block before residual connection. '1conv'/'3conv'

-    """

-

-    def __init__(self, img_size=64, patch_size=1, in_chans=3,

-                 embed_dim=96, depths=[6, 6, 6, 6], num_heads=[6, 6, 6, 6],

-                 window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None,

-                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,

-                 norm_layer=nn.LayerNorm, ape=False, patch_norm=True,

-                 use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv',

-                 **kwargs):

-        super(SwinIR, self).__init__()

-        num_in_ch = in_chans

-        num_out_ch = in_chans

-        num_feat = 64

-        self.img_range = img_range

-        if in_chans == 3:

-            rgb_mean = (0.4488, 0.4371, 0.4040)

-            self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)

-        else:

-            self.mean = torch.zeros(1, 1, 1, 1)

-        self.upscale = upscale

-        self.upsampler = upsampler

-        self.window_size = window_size

-

-        #####################################################################################################

-        ################################### 1, shallow feature extraction ###################################

-        self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1)

-

-        #####################################################################################################

-        ################################### 2, deep feature extraction ######################################

-        self.num_layers = len(depths)

-        self.embed_dim = embed_dim

-        self.ape = ape

-        self.patch_norm = patch_norm

-        self.num_features = embed_dim

-        self.mlp_ratio = mlp_ratio

-

-        # split image into non-overlapping patches

-        self.patch_embed = PatchEmbed(

-            img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,

-            norm_layer=norm_layer if self.patch_norm else None)

-        num_patches = self.patch_embed.num_patches

-        patches_resolution = self.patch_embed.patches_resolution

-        self.patches_resolution = patches_resolution

-

-        # merge non-overlapping patches into image

-        self.patch_unembed = PatchUnEmbed(

-            img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,

-            norm_layer=norm_layer if self.patch_norm else None)

-

-        # absolute position embedding

-        if self.ape:

-            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))

-            trunc_normal_(self.absolute_pos_embed, std=.02)

-

-        self.pos_drop = nn.Dropout(p=drop_rate)

-

-        # stochastic depth

-        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule

-

-        # build Residual Swin Transformer blocks (RSTB)

-        self.layers = nn.ModuleList()

-        for i_layer in range(self.num_layers):

-            layer = RSTB(dim=embed_dim,

-                         input_resolution=(patches_resolution[0],

-                                           patches_resolution[1]),

-                         depth=depths[i_layer],

-                         num_heads=num_heads[i_layer],

-                         window_size=window_size,

-                         mlp_ratio=self.mlp_ratio,

-                         qkv_bias=qkv_bias, qk_scale=qk_scale,

-                         drop=drop_rate, attn_drop=attn_drop_rate,

-                         drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],  # no impact on SR results

-                         norm_layer=norm_layer,

-                         downsample=None,

-                         use_checkpoint=use_checkpoint,

-                         img_size=img_size,

-                         patch_size=patch_size,

-                         resi_connection=resi_connection

-

-                         )

-            self.layers.append(layer)

-        self.norm = norm_layer(self.num_features)

-

-        # build the last conv layer in deep feature extraction

-        if resi_connection == '1conv':

-            self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)

-        elif resi_connection == '3conv':

-            # to save parameters and memory

-            self.conv_after_body = nn.Sequential(nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1),

-                                                 nn.LeakyReLU(negative_slope=0.2, inplace=True),

-                                                 nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0),

-                                                 nn.LeakyReLU(negative_slope=0.2, inplace=True),

-                                                 nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1))

-

-        #####################################################################################################

-        ################################ 3, high quality image reconstruction ################################

-        if self.upsampler == 'pixelshuffle':

-            # for classical SR

-            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),

-                                                      nn.LeakyReLU(inplace=True))

-            self.upsample = Upsample(upscale, num_feat)

-            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)

-        elif self.upsampler == 'pixelshuffledirect':

-            # for lightweight SR (to save parameters)

-            self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch,

-                                            (patches_resolution[0], patches_resolution[1]))

-        elif self.upsampler == 'nearest+conv':

-            # for real-world SR (less artifacts)

-            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),

-                                                      nn.LeakyReLU(inplace=True))

-            self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)

-            if self.upscale == 4:

-                self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)

-            self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)

-            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)

-            self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)

-        else:

-            # for image denoising and JPEG compression artifact reduction

-            self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1)

-

-        self.apply(self._init_weights)

-

-    def _init_weights(self, m):

-        if isinstance(m, nn.Linear):

-            trunc_normal_(m.weight, std=.02)

-            if isinstance(m, nn.Linear) and m.bias is not None:

-                nn.init.constant_(m.bias, 0)

-        elif isinstance(m, nn.LayerNorm):

-            nn.init.constant_(m.bias, 0)

-            nn.init.constant_(m.weight, 1.0)

-

-    @torch.jit.ignore

-    def no_weight_decay(self):

-        return {'absolute_pos_embed'}

-

-    @torch.jit.ignore

-    def no_weight_decay_keywords(self):

-        return {'relative_position_bias_table'}

-

-    def check_image_size(self, x):

-        _, _, h, w = x.size()

-        mod_pad_h = (self.window_size - h % self.window_size) % self.window_size

-        mod_pad_w = (self.window_size - w % self.window_size) % self.window_size

-        x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect')

-        return x

-

-    def forward_features(self, x):

-        x_size = (x.shape[2], x.shape[3])

-        x = self.patch_embed(x)

-        if self.ape:

-            x = x + self.absolute_pos_embed

-        x = self.pos_drop(x)

-

-        for layer in self.layers:

-            x = layer(x, x_size)

-

-        x = self.norm(x)  # B L C

-        x = self.patch_unembed(x, x_size)

-

-        return x

-

-    def forward(self, x):

-        H, W = x.shape[2:]

-        x = self.check_image_size(x)

-        

-        self.mean = self.mean.type_as(x)

-        x = (x - self.mean) * self.img_range

-

-        if self.upsampler == 'pixelshuffle':

-            # for classical SR

-            x = self.conv_first(x)

-            x = self.conv_after_body(self.forward_features(x)) + x

-            x = self.conv_before_upsample(x)

-            x = self.conv_last(self.upsample(x))

-        elif self.upsampler == 'pixelshuffledirect':

-            # for lightweight SR

-            x = self.conv_first(x)

-            x = self.conv_after_body(self.forward_features(x)) + x

-            x = self.upsample(x)

-        elif self.upsampler == 'nearest+conv':

-            # for real-world SR

-            x = self.conv_first(x)

-            x = self.conv_after_body(self.forward_features(x)) + x

-            x = self.conv_before_upsample(x)

-            x = self.lrelu(self.conv_up1(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))

-            if self.upscale == 4:

-                x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))

-            x = self.conv_last(self.lrelu(self.conv_hr(x)))

-        else:

-            # for image denoising and JPEG compression artifact reduction

-            x_first = self.conv_first(x)

-            res = self.conv_after_body(self.forward_features(x_first)) + x_first

-            x = x + self.conv_last(res)

-

-        x = x / self.img_range + self.mean

-

-        return x[:, :, :H*self.upscale, :W*self.upscale]

-

-    def flops(self):

-        flops = 0

-        H, W = self.patches_resolution

-        flops += H * W * 3 * self.embed_dim * 9

-        flops += self.patch_embed.flops()

-        for i, layer in enumerate(self.layers):

-            flops += layer.flops()

-        flops += H * W * 3 * self.embed_dim * self.embed_dim

-        flops += self.upsample.flops()

-        return flops

-

-

-if __name__ == '__main__':

-    upscale = 4

-    window_size = 8

-    height = (1024 // upscale // window_size + 1) * window_size

-    width = (720 // upscale // window_size + 1) * window_size

-    model = SwinIR(upscale=2, img_size=(height, width),

-                   window_size=window_size, img_range=1., depths=[6, 6, 6, 6],

-                   embed_dim=60, num_heads=[6, 6, 6, 6], mlp_ratio=2, upsampler='pixelshuffledirect')

-    print(model)

-    print(height, width, model.flops() / 1e9)

-

-    x = torch.randn((1, 3, height, width))

-    x = model(x)

-    print(x.shape)

+# -----------------------------------------------------------------------------------
+# SwinIR: Image Restoration Using Swin Transformer, https://arxiv.org/abs/2108.10257
+# Originally Written by Ze Liu, Modified by Jingyun Liang.
+# -----------------------------------------------------------------------------------
+
+import math
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint as checkpoint
+from timm.models.layers import DropPath, to_2tuple, trunc_normal_
+
+
+class Mlp(nn.Module):
+    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features)
+        self.act = act_layer()
+        self.fc2 = nn.Linear(hidden_features, out_features)
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+
+def window_partition(x, window_size):
+    """
+    Args:
+        x: (B, H, W, C)
+        window_size (int): window size
+
+    Returns:
+        windows: (num_windows*B, window_size, window_size, C)
+    """
+    B, H, W, C = x.shape
+    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
+    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
+    return windows
+
+
+def window_reverse(windows, window_size, H, W):
+    """
+    Args:
+        windows: (num_windows*B, window_size, window_size, C)
+        window_size (int): Window size
+        H (int): Height of image
+        W (int): Width of image
+
+    Returns:
+        x: (B, H, W, C)
+    """
+    B = int(windows.shape[0] / (H * W / window_size / window_size))
+    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
+    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
+    return x
+
+
+class WindowAttention(nn.Module):
+    r""" Window based multi-head self attention (W-MSA) module with relative position bias.
+    It supports both of shifted and non-shifted window.
+
+    Args:
+        dim (int): Number of input channels.
+        window_size (tuple[int]): The height and width of the window.
+        num_heads (int): Number of attention heads.
+        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
+        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
+        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
+    """
+
+    def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
+
+        super().__init__()
+        self.dim = dim
+        self.window_size = window_size  # Wh, Ww
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = qk_scale or head_dim ** -0.5
+
+        # define a parameter table of relative position bias
+        self.relative_position_bias_table = nn.Parameter(
+            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads))  # 2*Wh-1 * 2*Ww-1, nH
+
+        # get pair-wise relative position index for each token inside the window
+        coords_h = torch.arange(self.window_size[0])
+        coords_w = torch.arange(self.window_size[1])
+        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
+        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
+        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
+        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
+        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
+        relative_coords[:, :, 1] += self.window_size[1] - 1
+        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
+        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
+        self.register_buffer("relative_position_index", relative_position_index)
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+
+        self.proj_drop = nn.Dropout(proj_drop)
+
+        trunc_normal_(self.relative_position_bias_table, std=.02)
+        self.softmax = nn.Softmax(dim=-1)
+
+    def forward(self, x, mask=None):
+        """
+        Args:
+            x: input features with shape of (num_windows*B, N, C)
+            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
+        """
+        B_, N, C = x.shape
+        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)
+
+        q = q * self.scale
+        attn = (q @ k.transpose(-2, -1))
+
+        relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
+            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
+        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
+        attn = attn + relative_position_bias.unsqueeze(0)
+
+        if mask is not None:
+            nW = mask.shape[0]
+            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
+            attn = attn.view(-1, self.num_heads, N, N)
+            attn = self.softmax(attn)
+        else:
+            attn = self.softmax(attn)
+
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+    def extra_repr(self) -> str:
+        return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'
+
+    def flops(self, N):
+        # calculate flops for 1 window with token length of N
+        flops = 0
+        # qkv = self.qkv(x)
+        flops += N * self.dim * 3 * self.dim
+        # attn = (q @ k.transpose(-2, -1))
+        flops += self.num_heads * N * (self.dim // self.num_heads) * N
+        #  x = (attn @ v)
+        flops += self.num_heads * N * N * (self.dim // self.num_heads)
+        # x = self.proj(x)
+        flops += N * self.dim * self.dim
+        return flops
+
+
+class SwinTransformerBlock(nn.Module):
+    r""" Swin Transformer Block.
+
+    Args:
+        dim (int): Number of input channels.
+        input_resolution (tuple[int]): Input resulotion.
+        num_heads (int): Number of attention heads.
+        window_size (int): Window size.
+        shift_size (int): Shift size for SW-MSA.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float, optional): Stochastic depth rate. Default: 0.0
+        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
+        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+    """
+
+    def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
+                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
+                 act_layer=nn.GELU, norm_layer=nn.LayerNorm):
+        super().__init__()
+        self.dim = dim
+        self.input_resolution = input_resolution
+        self.num_heads = num_heads
+        self.window_size = window_size
+        self.shift_size = shift_size
+        self.mlp_ratio = mlp_ratio
+        if min(self.input_resolution) <= self.window_size:
+            # if window size is larger than input resolution, we don't partition windows
+            self.shift_size = 0
+            self.window_size = min(self.input_resolution)
+        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
+
+        self.norm1 = norm_layer(dim)
+        self.attn = WindowAttention(
+            dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
+            qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
+
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        self.norm2 = norm_layer(dim)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
+
+        if self.shift_size > 0:
+            attn_mask = self.calculate_mask(self.input_resolution)
+        else:
+            attn_mask = None
+
+        self.register_buffer("attn_mask", attn_mask)
+
+    def calculate_mask(self, x_size):
+        # calculate attention mask for SW-MSA
+        H, W = x_size
+        img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1
+        h_slices = (slice(0, -self.window_size),
+                    slice(-self.window_size, -self.shift_size),
+                    slice(-self.shift_size, None))
+        w_slices = (slice(0, -self.window_size),
+                    slice(-self.window_size, -self.shift_size),
+                    slice(-self.shift_size, None))
+        cnt = 0
+        for h in h_slices:
+            for w in w_slices:
+                img_mask[:, h, w, :] = cnt
+                cnt += 1
+
+        mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1
+        mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
+        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
+        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
+
+        return attn_mask
+
+    def forward(self, x, x_size):
+        H, W = x_size
+        B, L, C = x.shape
+        # assert L == H * W, "input feature has wrong size"
+
+        shortcut = x
+        x = self.norm1(x)
+        x = x.view(B, H, W, C)
+
+        # cyclic shift
+        if self.shift_size > 0:
+            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
+        else:
+            shifted_x = x
+
+        # partition windows
+        x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C
+        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C
+
+        # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size
+        if self.input_resolution == x_size:
+            attn_windows = self.attn(x_windows, mask=self.attn_mask)  # nW*B, window_size*window_size, C
+        else:
+            attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device))
+
+        # merge windows
+        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
+        shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C
+
+        # reverse cyclic shift
+        if self.shift_size > 0:
+            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
+        else:
+            x = shifted_x
+        x = x.view(B, H * W, C)
+
+        # FFN
+        x = shortcut + self.drop_path(x)
+        x = x + self.drop_path(self.mlp(self.norm2(x)))
+
+        return x
+
+    def extra_repr(self) -> str:
+        return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
+               f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
+
+    def flops(self):
+        flops = 0
+        H, W = self.input_resolution
+        # norm1
+        flops += self.dim * H * W
+        # W-MSA/SW-MSA
+        nW = H * W / self.window_size / self.window_size
+        flops += nW * self.attn.flops(self.window_size * self.window_size)
+        # mlp
+        flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
+        # norm2
+        flops += self.dim * H * W
+        return flops
+
+
+class PatchMerging(nn.Module):
+    r""" Patch Merging Layer.
+
+    Args:
+        input_resolution (tuple[int]): Resolution of input feature.
+        dim (int): Number of input channels.
+        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+    """
+
+    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
+        super().__init__()
+        self.input_resolution = input_resolution
+        self.dim = dim
+        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
+        self.norm = norm_layer(4 * dim)
+
+    def forward(self, x):
+        """
+        x: B, H*W, C
+        """
+        H, W = self.input_resolution
+        B, L, C = x.shape
+        assert L == H * W, "input feature has wrong size"
+        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
+
+        x = x.view(B, H, W, C)
+
+        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
+        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
+        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
+        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
+        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
+        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
+
+        x = self.norm(x)
+        x = self.reduction(x)
+
+        return x
+
+    def extra_repr(self) -> str:
+        return f"input_resolution={self.input_resolution}, dim={self.dim}"
+
+    def flops(self):
+        H, W = self.input_resolution
+        flops = H * W * self.dim
+        flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
+        return flops
+
+
+class BasicLayer(nn.Module):
+    """ A basic Swin Transformer layer for one stage.
+
+    Args:
+        dim (int): Number of input channels.
+        input_resolution (tuple[int]): Input resolution.
+        depth (int): Number of blocks.
+        num_heads (int): Number of attention heads.
+        window_size (int): Local window size.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
+        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+    """
+
+    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
+                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
+                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False):
+
+        super().__init__()
+        self.dim = dim
+        self.input_resolution = input_resolution
+        self.depth = depth
+        self.use_checkpoint = use_checkpoint
+
+        # build blocks
+        self.blocks = nn.ModuleList([
+            SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
+                                 num_heads=num_heads, window_size=window_size,
+                                 shift_size=0 if (i % 2 == 0) else window_size // 2,
+                                 mlp_ratio=mlp_ratio,
+                                 qkv_bias=qkv_bias, qk_scale=qk_scale,
+                                 drop=drop, attn_drop=attn_drop,
+                                 drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
+                                 norm_layer=norm_layer)
+            for i in range(depth)])
+
+        # patch merging layer
+        if downsample is not None:
+            self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
+        else:
+            self.downsample = None
+
+    def forward(self, x, x_size):
+        for blk in self.blocks:
+            if self.use_checkpoint:
+                x = checkpoint.checkpoint(blk, x, x_size)
+            else:
+                x = blk(x, x_size)
+        if self.downsample is not None:
+            x = self.downsample(x)
+        return x
+
+    def extra_repr(self) -> str:
+        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
+
+    def flops(self):
+        flops = 0
+        for blk in self.blocks:
+            flops += blk.flops()
+        if self.downsample is not None:
+            flops += self.downsample.flops()
+        return flops
+
+
+class RSTB(nn.Module):
+    """Residual Swin Transformer Block (RSTB).
+
+    Args:
+        dim (int): Number of input channels.
+        input_resolution (tuple[int]): Input resolution.
+        depth (int): Number of blocks.
+        num_heads (int): Number of attention heads.
+        window_size (int): Local window size.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
+        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+        img_size: Input image size.
+        patch_size: Patch size.
+        resi_connection: The convolutional block before residual connection.
+    """
+
+    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
+                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
+                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
+                 img_size=224, patch_size=4, resi_connection='1conv'):
+        super(RSTB, self).__init__()
+
+        self.dim = dim
+        self.input_resolution = input_resolution
+
+        self.residual_group = BasicLayer(dim=dim,
+                                         input_resolution=input_resolution,
+                                         depth=depth,
+                                         num_heads=num_heads,
+                                         window_size=window_size,
+                                         mlp_ratio=mlp_ratio,
+                                         qkv_bias=qkv_bias, qk_scale=qk_scale,
+                                         drop=drop, attn_drop=attn_drop,
+                                         drop_path=drop_path,
+                                         norm_layer=norm_layer,
+                                         downsample=downsample,
+                                         use_checkpoint=use_checkpoint)
+
+        if resi_connection == '1conv':
+            self.conv = nn.Conv2d(dim, dim, 3, 1, 1)
+        elif resi_connection == '3conv':
+            # to save parameters and memory
+            self.conv = nn.Sequential(nn.Conv2d(dim, dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True),
+                                      nn.Conv2d(dim // 4, dim // 4, 1, 1, 0),
+                                      nn.LeakyReLU(negative_slope=0.2, inplace=True),
+                                      nn.Conv2d(dim // 4, dim, 3, 1, 1))
+
+        self.patch_embed = PatchEmbed(
+            img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim,
+            norm_layer=None)
+
+        self.patch_unembed = PatchUnEmbed(
+            img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim,
+            norm_layer=None)
+
+    def forward(self, x, x_size):
+        return self.patch_embed(self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))) + x
+
+    def flops(self):
+        flops = 0
+        flops += self.residual_group.flops()
+        H, W = self.input_resolution
+        flops += H * W * self.dim * self.dim * 9
+        flops += self.patch_embed.flops()
+        flops += self.patch_unembed.flops()
+
+        return flops
+
+
+class PatchEmbed(nn.Module):
+    r""" Image to Patch Embedding
+
+    Args:
+        img_size (int): Image size.  Default: 224.
+        patch_size (int): Patch token size. Default: 4.
+        in_chans (int): Number of input image channels. Default: 3.
+        embed_dim (int): Number of linear projection output channels. Default: 96.
+        norm_layer (nn.Module, optional): Normalization layer. Default: None
+    """
+
+    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
+        super().__init__()
+        img_size = to_2tuple(img_size)
+        patch_size = to_2tuple(patch_size)
+        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
+        self.img_size = img_size
+        self.patch_size = patch_size
+        self.patches_resolution = patches_resolution
+        self.num_patches = patches_resolution[0] * patches_resolution[1]
+
+        self.in_chans = in_chans
+        self.embed_dim = embed_dim
+
+        if norm_layer is not None:
+            self.norm = norm_layer(embed_dim)
+        else:
+            self.norm = None
+
+    def forward(self, x):
+        x = x.flatten(2).transpose(1, 2)  # B Ph*Pw C
+        if self.norm is not None:
+            x = self.norm(x)
+        return x
+
+    def flops(self):
+        flops = 0
+        H, W = self.img_size
+        if self.norm is not None:
+            flops += H * W * self.embed_dim
+        return flops
+
+
+class PatchUnEmbed(nn.Module):
+    r""" Image to Patch Unembedding
+
+    Args:
+        img_size (int): Image size.  Default: 224.
+        patch_size (int): Patch token size. Default: 4.
+        in_chans (int): Number of input image channels. Default: 3.
+        embed_dim (int): Number of linear projection output channels. Default: 96.
+        norm_layer (nn.Module, optional): Normalization layer. Default: None
+    """
+
+    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
+        super().__init__()
+        img_size = to_2tuple(img_size)
+        patch_size = to_2tuple(patch_size)
+        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
+        self.img_size = img_size
+        self.patch_size = patch_size
+        self.patches_resolution = patches_resolution
+        self.num_patches = patches_resolution[0] * patches_resolution[1]
+
+        self.in_chans = in_chans
+        self.embed_dim = embed_dim
+
+    def forward(self, x, x_size):
+        B, HW, C = x.shape
+        x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1])  # B Ph*Pw C
+        return x
+
+    def flops(self):
+        flops = 0
+        return flops
+
+
+class Upsample(nn.Sequential):
+    """Upsample module.
+
+    Args:
+        scale (int): Scale factor. Supported scales: 2^n and 3.
+        num_feat (int): Channel number of intermediate features.
+    """
+
+    def __init__(self, scale, num_feat):
+        m = []
+        if (scale & (scale - 1)) == 0:  # scale = 2^n
+            for _ in range(int(math.log(scale, 2))):
+                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
+                m.append(nn.PixelShuffle(2))
+        elif scale == 3:
+            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
+            m.append(nn.PixelShuffle(3))
+        else:
+            raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
+        super(Upsample, self).__init__(*m)
+
+
+class UpsampleOneStep(nn.Sequential):
+    """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle)
+       Used in lightweight SR to save parameters.
+
+    Args:
+        scale (int): Scale factor. Supported scales: 2^n and 3.
+        num_feat (int): Channel number of intermediate features.
+
+    """
+
+    def __init__(self, scale, num_feat, num_out_ch, input_resolution=None):
+        self.num_feat = num_feat
+        self.input_resolution = input_resolution
+        m = []
+        m.append(nn.Conv2d(num_feat, (scale ** 2) * num_out_ch, 3, 1, 1))
+        m.append(nn.PixelShuffle(scale))
+        super(UpsampleOneStep, self).__init__(*m)
+
+    def flops(self):
+        H, W = self.input_resolution
+        flops = H * W * self.num_feat * 3 * 9
+        return flops
+
+
+class SwinIR(nn.Module):
+    r""" SwinIR
+        A PyTorch impl of : `SwinIR: Image Restoration Using Swin Transformer`, based on Swin Transformer.
+
+    Args:
+        img_size (int | tuple(int)): Input image size. Default 64
+        patch_size (int | tuple(int)): Patch size. Default: 1
+        in_chans (int): Number of input image channels. Default: 3
+        embed_dim (int): Patch embedding dimension. Default: 96
+        depths (tuple(int)): Depth of each Swin Transformer layer.
+        num_heads (tuple(int)): Number of attention heads in different layers.
+        window_size (int): Window size. Default: 7
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
+        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
+        drop_rate (float): Dropout rate. Default: 0
+        attn_drop_rate (float): Attention dropout rate. Default: 0
+        drop_path_rate (float): Stochastic depth rate. Default: 0.1
+        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
+        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
+        patch_norm (bool): If True, add normalization after patch embedding. Default: True
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
+        upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction
+        img_range: Image range. 1. or 255.
+        upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None
+        resi_connection: The convolutional block before residual connection. '1conv'/'3conv'
+    """
+
+    def __init__(self, img_size=64, patch_size=1, in_chans=3,
+                 embed_dim=96, depths=[6, 6, 6, 6], num_heads=[6, 6, 6, 6],
+                 window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None,
+                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
+                 norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
+                 use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv',
+                 **kwargs):
+        super(SwinIR, self).__init__()
+        num_in_ch = in_chans
+        num_out_ch = in_chans
+        num_feat = 64
+        self.img_range = img_range
+        if in_chans == 3:
+            rgb_mean = (0.4488, 0.4371, 0.4040)
+            self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)
+        else:
+            self.mean = torch.zeros(1, 1, 1, 1)
+        self.upscale = upscale
+        self.upsampler = upsampler
+        self.window_size = window_size
+
+        #####################################################################################################
+        ################################### 1, shallow feature extraction ###################################
+        self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1)
+
+        #####################################################################################################
+        ################################### 2, deep feature extraction ######################################
+        self.num_layers = len(depths)
+        self.embed_dim = embed_dim
+        self.ape = ape
+        self.patch_norm = patch_norm
+        self.num_features = embed_dim
+        self.mlp_ratio = mlp_ratio
+
+        # split image into non-overlapping patches
+        self.patch_embed = PatchEmbed(
+            img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
+            norm_layer=norm_layer if self.patch_norm else None)
+        num_patches = self.patch_embed.num_patches
+        patches_resolution = self.patch_embed.patches_resolution
+        self.patches_resolution = patches_resolution
+
+        # merge non-overlapping patches into image
+        self.patch_unembed = PatchUnEmbed(
+            img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
+            norm_layer=norm_layer if self.patch_norm else None)
+
+        # absolute position embedding
+        if self.ape:
+            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
+            trunc_normal_(self.absolute_pos_embed, std=.02)
+
+        self.pos_drop = nn.Dropout(p=drop_rate)
+
+        # stochastic depth
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
+
+        # build Residual Swin Transformer blocks (RSTB)
+        self.layers = nn.ModuleList()
+        for i_layer in range(self.num_layers):
+            layer = RSTB(dim=embed_dim,
+                         input_resolution=(patches_resolution[0],
+                                           patches_resolution[1]),
+                         depth=depths[i_layer],
+                         num_heads=num_heads[i_layer],
+                         window_size=window_size,
+                         mlp_ratio=self.mlp_ratio,
+                         qkv_bias=qkv_bias, qk_scale=qk_scale,
+                         drop=drop_rate, attn_drop=attn_drop_rate,
+                         drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],  # no impact on SR results
+                         norm_layer=norm_layer,
+                         downsample=None,
+                         use_checkpoint=use_checkpoint,
+                         img_size=img_size,
+                         patch_size=patch_size,
+                         resi_connection=resi_connection
+
+                         )
+            self.layers.append(layer)
+        self.norm = norm_layer(self.num_features)
+
+        # build the last conv layer in deep feature extraction
+        if resi_connection == '1conv':
+            self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
+        elif resi_connection == '3conv':
+            # to save parameters and memory
+            self.conv_after_body = nn.Sequential(nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1),
+                                                 nn.LeakyReLU(negative_slope=0.2, inplace=True),
+                                                 nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0),
+                                                 nn.LeakyReLU(negative_slope=0.2, inplace=True),
+                                                 nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1))
+
+        #####################################################################################################
+        ################################ 3, high quality image reconstruction ################################
+        if self.upsampler == 'pixelshuffle':
+            # for classical SR
+            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
+                                                      nn.LeakyReLU(inplace=True))
+            self.upsample = Upsample(upscale, num_feat)
+            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+        elif self.upsampler == 'pixelshuffledirect':
+            # for lightweight SR (to save parameters)
+            self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch,
+                                            (patches_resolution[0], patches_resolution[1]))
+        elif self.upsampler == 'nearest+conv':
+            # for real-world SR (less artifacts)
+            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
+                                                      nn.LeakyReLU(inplace=True))
+            self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+            if self.upscale == 4:
+                self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+            self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+            self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
+        else:
+            # for image denoising and JPEG compression artifact reduction
+            self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1)
+
+        self.apply(self._init_weights)
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+
+    @torch.jit.ignore
+    def no_weight_decay(self):
+        return {'absolute_pos_embed'}
+
+    @torch.jit.ignore
+    def no_weight_decay_keywords(self):
+        return {'relative_position_bias_table'}
+
+    def check_image_size(self, x):
+        _, _, h, w = x.size()
+        mod_pad_h = (self.window_size - h % self.window_size) % self.window_size
+        mod_pad_w = (self.window_size - w % self.window_size) % self.window_size
+        x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect')
+        return x
+
+    def forward_features(self, x):
+        x_size = (x.shape[2], x.shape[3])
+        x = self.patch_embed(x)
+        if self.ape:
+            x = x + self.absolute_pos_embed
+        x = self.pos_drop(x)
+
+        for layer in self.layers:
+            x = layer(x, x_size)
+
+        x = self.norm(x)  # B L C
+        x = self.patch_unembed(x, x_size)
+
+        return x
+
+    def forward(self, x):
+        H, W = x.shape[2:]
+        x = self.check_image_size(x)
+        
+        self.mean = self.mean.type_as(x)
+        x = (x - self.mean) * self.img_range
+
+        if self.upsampler == 'pixelshuffle':
+            # for classical SR
+            x = self.conv_first(x)
+            x = self.conv_after_body(self.forward_features(x)) + x
+            x = self.conv_before_upsample(x)
+            x = self.conv_last(self.upsample(x))
+        elif self.upsampler == 'pixelshuffledirect':
+            # for lightweight SR
+            x = self.conv_first(x)
+            x = self.conv_after_body(self.forward_features(x)) + x
+            x = self.upsample(x)
+        elif self.upsampler == 'nearest+conv':
+            # for real-world SR
+            x = self.conv_first(x)
+            x = self.conv_after_body(self.forward_features(x)) + x
+            x = self.conv_before_upsample(x)
+            x = self.lrelu(self.conv_up1(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
+            if self.upscale == 4:
+                x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
+            x = self.conv_last(self.lrelu(self.conv_hr(x)))
+        else:
+            # for image denoising and JPEG compression artifact reduction
+            x_first = self.conv_first(x)
+            res = self.conv_after_body(self.forward_features(x_first)) + x_first
+            x = x + self.conv_last(res)
+
+        x = x / self.img_range + self.mean
+
+        return x[:, :, :H*self.upscale, :W*self.upscale]
+
+    def flops(self):
+        flops = 0
+        H, W = self.patches_resolution
+        flops += H * W * 3 * self.embed_dim * 9
+        flops += self.patch_embed.flops()
+        for i, layer in enumerate(self.layers):
+            flops += layer.flops()
+        flops += H * W * 3 * self.embed_dim * self.embed_dim
+        flops += self.upsample.flops()
+        return flops
+
+
+if __name__ == '__main__':
+    upscale = 4
+    window_size = 8
+    height = (1024 // upscale // window_size + 1) * window_size
+    width = (720 // upscale // window_size + 1) * window_size
+    model = SwinIR(upscale=2, img_size=(height, width),
+                   window_size=window_size, img_range=1., depths=[6, 6, 6, 6],
+                   embed_dim=60, num_heads=[6, 6, 6, 6], mlp_ratio=2, upsampler='pixelshuffledirect')
+    print(model)
+    print(height, width, model.flops() / 1e9)
+
+    x = torch.randn((1, 3, height, width))
+    x = model(x)
+    print(x.shape)
diff --git a/modules/upscaler.py b/modules/upscaler.py
new file mode 100644
index 00000000..d698282f
--- /dev/null
+++ b/modules/upscaler.py
@@ -0,0 +1,121 @@
+import os
+from abc import abstractmethod
+
+import PIL
+import numpy as np
+import torch
+from PIL import Image
+
+import modules.shared
+from modules import modelloader, shared
+
+LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
+from modules.paths import models_path
+
+
+class Upscaler:
+    name = None
+    model_path = None
+    model_name = None
+    model_url = None
+    enable = True
+    filter = None
+    model = None
+    user_path = None
+    scalers: []
+    tile = True
+
+    def __init__(self, create_dirs=False):
+        self.mod_pad_h = None
+        self.tile_size = modules.shared.opts.ESRGAN_tile
+        self.tile_pad = modules.shared.opts.ESRGAN_tile_overlap
+        self.device = modules.shared.device
+        self.img = None
+        self.output = None
+        self.scale = 1
+        self.half = not modules.shared.cmd_opts.no_half
+        self.pre_pad = 0
+        self.mod_scale = None
+        if self.name is not None and create_dirs:
+            self.model_path = os.path.join(models_path, self.name)
+            if not os.path.exists(self.model_path):
+                os.makedirs(self.model_path)
+
+        try:
+            import cv2
+            self.can_tile = True
+        except:
+            pass
+
+    @abstractmethod
+    def do_upscale(self, img: PIL.Image, selected_model: str):
+        return img
+
+    def upscale(self, img: PIL.Image, scale: int, selected_model: str = None):
+        self.scale = scale
+        dest_w = img.width * scale
+        dest_h = img.height * scale
+        for i in range(3):
+            if img.width >= dest_w and img.height >= dest_h:
+                break
+            img = self.do_upscale(img, selected_model)
+        if img.width != dest_w or img.height != dest_h:
+            img = img.resize(dest_w, dest_h, resample=LANCZOS)
+
+        return img
+
+    @abstractmethod
+    def load_model(self, path: str):
+        pass
+
+    def find_models(self, ext_filter=None) -> list:
+        return modelloader.load_models(model_path=self.model_path, model_url=self.model_url, command_path=self.user_path)
+
+    def update_status(self, prompt):
+        print(f"\nextras: {prompt}", file=shared.progress_print_out)
+
+
+class UpscalerData:
+    name = None
+    data_path = None
+    scale: int = 4
+    scaler: Upscaler = None
+    model: None
+
+    def __init__(self, name: str, path: str, upscaler: Upscaler = None, scale: int = 4, model=None):
+        self.name = name
+        self.data_path = path
+        self.scaler = upscaler
+        self.scale = scale
+        self.model = model
+
+
+class UpscalerNone(Upscaler):
+    name = "None"
+    scalers = []
+
+    def load_model(self, path):
+        pass
+
+    def do_upscale(self, img, selected_model=None):
+        return img
+
+    def __init__(self, dirname=None):
+        super().__init__(False)
+        self.scalers = [UpscalerData("None", None, self)]
+
+
+class UpscalerLanczos(Upscaler):
+    scalers = []
+
+    def do_upscale(self, img, selected_model=None):
+        return img.resize((int(img.width * self.scale), int(img.height * self.scale)), resample=LANCZOS)
+
+    def load_model(self, _):
+        pass
+
+    def __init__(self, dirname=None):
+        super().__init__(False)
+        self.name = "Lanczos"
+        self.scalers = [UpscalerData("Lanczos", None, self)]
+