Merge pull request #1109 from d8ahazard/ModelLoader

Model Loader, Fixes

.gitignore

@@ -1,10 +1,13 @@
 __pycache__
-/ESRGAN
+*.ckpt
+*.pth
+/ESRGAN/*
+/SwinIR/*
 /repositories
 /venv
 /tmp
 /model.ckpt
-/models/**/*.ckpt
+/models/**/*
 /GFPGANv1.3.pth
 /gfpgan/weights/*.pth
 /ui-config.json

SwinIR/put_swinir_models_here.txt

@@ -1 +0,0 @@
-

launch.py

@@ -1,5 +1,5 @@
 # this scripts installs necessary requirements and launches main program in webui.py
-
+import shutil
 import subprocess
 import os
 import sys
@@ -22,7 +22,6 @@ taming_transformers_commit_hash = os.environ.get('TAMING_TRANSFORMERS_COMMIT_HAS
 k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "a7ec1974d4ccb394c2dca275f42cd97490618924")
 codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
 blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
-ldsr_commit_hash = os.environ.get('LDSR_COMMIT_HASH', "abf33e7002d59d9085081bce93ec798dcabd49af")
 
 args = shlex.split(commandline_args)
 
@@ -120,9 +119,11 @@ git_clone("https://github.com/CompVis/taming-transformers.git", repo_dir('taming
 git_clone("https://github.com/crowsonkb/k-diffusion.git", repo_dir('k-diffusion'), "K-diffusion", k_diffusion_commit_hash)
 git_clone("https://github.com/sczhou/CodeFormer.git", repo_dir('CodeFormer'), "CodeFormer", codeformer_commit_hash)
 git_clone("https://github.com/salesforce/BLIP.git", repo_dir('BLIP'), "BLIP", blip_commit_hash)
-# Using my repo until my changes are merged, as this makes interfacing with our version of SD-web a lot easier
-git_clone("https://github.com/Hafiidz/latent-diffusion", repo_dir('latent-diffusion'), "LDSR", ldsr_commit_hash)
-
+if os.path.isdir(repo_dir('latent-diffusion')):
+    try:
+        shutil.rmtree(repo_dir('latent-diffusion'))
+    except:
+        pass
 if not is_installed("lpips"):
     run_pip(f"install -r {os.path.join(repo_dir('CodeFormer'), 'requirements.txt')}", "requirements for CodeFormer")
 

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
+

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

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:

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)
+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)
 
-    if 'conv_first.weight' in pretrained_net:
-        crt_model.load_state_dict(pretrained_net)
+            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
+
+    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:
+            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
 
-    if 'model.0.weight' not in pretrained_net:
-        is_realesrgan = "params_ema" in pretrained_net and 'body.0.rdb1.conv1.weight' in pretrained_net["params_ema"]
-        if is_realesrgan:
-            raise Exception("The file is a RealESRGAN model, it can't be used as a ESRGAN model.")
-        else:
-            raise Exception("The file is not a ESRGAN model.")
-
-    crt_net = crt_model.state_dict()
-    load_net_clean = {}
-    for k, v in pretrained_net.items():
-        if k.startswith('module.'):
-            load_net_clean[k[7:]] = v
-        else:
-            load_net_clean[k] = v
-    pretrained_net = load_net_clean
-
-    tbd = []
-    for k, v in crt_net.items():
-        tbd.append(k)
-
-    # directly copy
-    for k, v in crt_net.items():
-        if k in pretrained_net and pretrained_net[k].size() == v.size():
-            crt_net[k] = pretrained_net[k]
-            tbd.remove(k)
-
-    crt_net['conv_first.weight'] = pretrained_net['model.0.weight']
-    crt_net['conv_first.bias'] = pretrained_net['model.0.bias']
-
-    for k in tbd.copy():
-        if 'RDB' in k:
-            ori_k = k.replace('RRDB_trunk.', 'model.1.sub.')
-            if '.weight' in k:
-                ori_k = ori_k.replace('.weight', '.0.weight')
-            elif '.bias' in k:
-                ori_k = ori_k.replace('.bias', '.0.bias')
-            crt_net[k] = pretrained_net[ori_k]
-            tbd.remove(k)
-
-    crt_net['trunk_conv.weight'] = pretrained_net['model.1.sub.23.weight']
-    crt_net['trunk_conv.bias'] = pretrained_net['model.1.sub.23.bias']
-    crt_net['upconv1.weight'] = pretrained_net['model.3.weight']
-    crt_net['upconv1.bias'] = pretrained_net['model.3.bias']
-    crt_net['upconv2.weight'] = pretrained_net['model.6.weight']
-    crt_net['upconv2.bias'] = pretrained_net['model.6.bias']
-    crt_net['HRconv.weight'] = pretrained_net['model.8.weight']
-    crt_net['HRconv.bias'] = pretrained_net['model.8.bias']
-    crt_net['conv_last.weight'] = pretrained_net['model.10.weight']
-    crt_net['conv_last.bias'] = pretrained_net['model.10.bias']
-
-    crt_model.load_state_dict(crt_net)
-    crt_model.eval()
-    return crt_model
 
 def upscale_without_tiling(model, img):
     img = np.array(img)
@@ -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)

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()))]

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 facexlib
+import gfpgan
+
 import modules.face_restoration
+from modules import shared, devices, modelloader
+from modules.paths import models_path
 
-
-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
-
-
+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():
+
+def setup_model(dirname):
+    global model_path
+    if not os.path.exists(model_path):
+        os.makedirs(model_path)
+
     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
-
+        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

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())

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
-
-LDSRModelInfo = namedtuple("LDSRModelInfo", ["name", "location", "model", "netscale"])
-
-ldsr_models = []
-have_ldsr = False
-LDSR_obj = None
+from modules.paths import models_path
 
 
-class UpscalerLDSR(modules.images.Upscaler):
-    def __init__(self, steps):
-        self.steps = steps
+class UpscalerLDSR(Upscaler):
+    def __init__(self, user_path):
         self.name = "LDSR"
+        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 do_upscale(self, img):
-        return upscale_with_ldsr(img)
+    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)
 
-def add_lsdr():
-    modules.shared.sd_upscalers.append(UpscalerLDSR(100))
+        except Exception:
+            print("Error importing LDSR:", file=sys.stderr)
+            print(traceback.format_exc(), file=sys.stderr)
+        return None
 
-
-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
+    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)

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

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

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:

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 load_models(self, _):
+        return get_realesrgan_models(self)
 
 
-def get_realesrgan_models():
+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

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)
 

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]}),
 }))
 

modules/swinir.py

@@ -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

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

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)]
+

webui.py

@@ -3,36 +3,36 @@ import threading
 
 from modules import devices
 from modules.paths import script_path
-
 import signal
-
-from modules.shared import opts, cmd_opts, state
-import modules.shared as shared
-import modules.ui
+import threading
+import modules.paths
+import modules.codeformer_model as codeformer
+import modules.esrgan_model as esrgan
+import modules.bsrgan_model as bsrgan
+import modules.extras
+import modules.face_restoration
+import modules.gfpgan_model as gfpgan
+import modules.img2img
+import modules.ldsr_model as ldsr
+import modules.lowvram
+import modules.realesrgan_model as realesrgan
 import modules.scripts
 import modules.sd_hijack
-import modules.codeformer_model
-import modules.gfpgan_model
-import modules.face_restoration
-import modules.realesrgan_model as realesrgan
-import modules.esrgan_model as esrgan
-import modules.ldsr_model as ldsr
-import modules.extras
-import modules.lowvram
-import modules.txt2img
-import modules.img2img
-import modules.swinir as swinir
 import modules.sd_models
+import modules.shared as shared
+import modules.swinir_model as swinir
+import modules.txt2img
+import modules.ui
+from modules import modelloader
+from modules.paths import script_path
+from modules.shared import cmd_opts
 
-
-modules.codeformer_model.setup_codeformer()
-modules.gfpgan_model.setup_gfpgan()
+modelloader.cleanup_models()
+modules.sd_models.setup_model(cmd_opts.stablediffusion_models_path)
+codeformer.setup_model(cmd_opts.codeformer_models_path)
+gfpgan.setup_model(cmd_opts.gfpgan_models_path)
 shared.face_restorers.append(modules.face_restoration.FaceRestoration())
-
-esrgan.load_models(cmd_opts.esrgan_models_path)
-swinir.load_models(cmd_opts.swinir_models_path)
-realesrgan.setup_realesrgan()
-ldsr.add_lsdr()
+modelloader.load_upscalers()
 queue_lock = threading.Lock()