Upscaler model loading cleanup

extensions-builtin/LDSR/scripts/ldsr_model.py

@@ -1,7 +1,6 @@
 import os
 
-from basicsr.utils.download_util import load_file_from_url
-
+from modules.modelloader import load_file_from_url
 from modules.upscaler import Upscaler, UpscalerData
 from ldsr_model_arch import LDSR
 from modules import shared, script_callbacks, errors
@@ -43,20 +42,17 @@ def load_model(self, path: str):
         if local_safetensors_path is not None and os.path.exists(local_safetensors_path):
             model = local_safetensors_path
         else:
-            model = local_ckpt_path if local_ckpt_path is not None else load_file_from_url(url=self.model_url, model_dir=self.model_download_path, file_name="model.ckpt", progress=True)
+            model = local_ckpt_path or load_file_from_url(self.model_url, model_dir=self.model_download_path, file_name="model.ckpt")
 
-        yaml = local_yaml_path if local_yaml_path is not None else load_file_from_url(url=self.yaml_url, model_dir=self.model_download_path, file_name="project.yaml", progress=True)
+        yaml = local_yaml_path or load_file_from_url(self.yaml_url, model_dir=self.model_download_path, file_name="project.yaml")
 
-        try:
-            return LDSR(model, yaml)
-        except Exception:
-            errors.report("Error importing LDSR", exc_info=True)
-        return None
+        return LDSR(model, yaml)
 
     def do_upscale(self, img, path):
-        ldsr = self.load_model(path)
-        if ldsr is None:
-            print("NO LDSR!")
+        try:
+            ldsr = self.load_model(path)
+        except Exception:
+            errors.report(f"Failed loading LDSR model {path}", exc_info=True)
             return img
         ddim_steps = shared.opts.ldsr_steps
         return ldsr.super_resolution(img, ddim_steps, self.scale)

extensions-builtin/ScuNET/scripts/scunet_model.py

@@ -1,17 +1,15 @@
-import os.path
 import sys
 
 import PIL.Image
 import numpy as np
 import torch
 from tqdm import tqdm
 
-from basicsr.utils.download_util import load_file_from_url
-
 import modules.upscaler
 from modules import devices, modelloader, script_callbacks, errors
-from scunet_model_arch import SCUNet as net
+from scunet_model_arch import SCUNet
 
+from modules.modelloader import load_file_from_url
 from modules.shared import opts
 
 
@@ -28,7 +26,7 @@ def __init__(self, dirname):
         scalers = []
         add_model2 = True
         for file in model_paths:
-            if "http" in file:
+            if file.startswith("http"):
                 name = self.model_name
             else:
                 name = modelloader.friendly_name(file)
@@ -89,9 +87,10 @@ def do_upscale(self, img: PIL.Image.Image, selected_file):
 
         torch.cuda.empty_cache()
 
-        model = self.load_model(selected_file)
-        if model is None:
-            print(f"ScuNET: Unable to load model from {selected_file}", file=sys.stderr)
+        try:
+            model = self.load_model(selected_file)
+        except Exception as e:
+            print(f"ScuNET: Unable to load model from {selected_file}: {e}", file=sys.stderr)
             return img
 
         device = devices.get_device_for('scunet')
@@ -119,15 +118,12 @@ def do_upscale(self, img: PIL.Image.Image, selected_file):
 
     def load_model(self, path: str):
         device = devices.get_device_for('scunet')
-        if "http" in path:
-            filename = load_file_from_url(url=self.model_url, model_dir=self.model_download_path, file_name="%s.pth" % self.name, progress=True)
+        if path.startswith("http"):
+            # TODO: this doesn't use `path` at all?
+            filename = load_file_from_url(self.model_url, model_dir=self.model_download_path, file_name=f"{self.name}.pth")
         else:
             filename = path
-        if not os.path.exists(os.path.join(self.model_path, filename)) or filename is None:
-            print(f"ScuNET: Unable to load model from {filename}", file=sys.stderr)
-            return None
-
-        model = net(in_nc=3, config=[4, 4, 4, 4, 4, 4, 4], dim=64)
+        model = SCUNet(in_nc=3, config=[4, 4, 4, 4, 4, 4, 4], dim=64)
         model.load_state_dict(torch.load(filename), strict=True)
         model.eval()
         for _, v in model.named_parameters():

extensions-builtin/SwinIR/scripts/swinir_model.py

@@ -1,34 +1,32 @@
-import os
+import sys
 
 import numpy as np
 import torch
 from PIL import Image
-from basicsr.utils.download_util import load_file_from_url
 from tqdm import tqdm
 
 from modules import modelloader, devices, script_callbacks, shared
 from modules.shared import opts, state
-from swinir_model_arch import SwinIR as net
-from swinir_model_arch_v2 import Swin2SR as net2
+from swinir_model_arch import SwinIR
+from swinir_model_arch_v2 import Swin2SR
 from modules.upscaler import Upscaler, UpscalerData
 
+SWINIR_MODEL_URL = "https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR-L_x4_GAN.pth"
 
 device_swinir = devices.get_device_for('swinir')
 
 
 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_url = SWINIR_MODEL_URL
         self.model_name = "SwinIR 4x"
         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:
+            if model.startswith("http"):
                 name = self.model_name
             else:
                 name = modelloader.friendly_name(model)
@@ -37,8 +35,10 @@ def __init__(self, dirname):
         self.scalers = scalers
 
     def do_upscale(self, img, model_file):
-        model = self.load_model(model_file)
-        if model is None:
+        try:
+            model = self.load_model(model_file)
+        except Exception as e:
+            print(f"Failed loading SwinIR model {model_file}: {e}", file=sys.stderr)
             return img
         model = model.to(device_swinir, dtype=devices.dtype)
         img = upscale(img, model)
@@ -49,30 +49,31 @@ def do_upscale(self, img, model_file):
         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_download_path, file_name=dl_name, progress=True)
+        if path.startswith("http"):
+            filename = modelloader.load_file_from_url(
+                url=path,
+                model_dir=self.model_download_path,
+                file_name=f"{self.model_name.replace(' ', '_')}.pth",
+            )
         else:
             filename = path
-        if filename is None or not os.path.exists(filename):
-            return None
         if filename.endswith(".v2.pth"):
-            model = net2(
-            upscale=scale,
-            in_chans=3,
-            img_size=64,
-            window_size=8,
-            img_range=1.0,
-            depths=[6, 6, 6, 6, 6, 6],
-            embed_dim=180,
-            num_heads=[6, 6, 6, 6, 6, 6],
-            mlp_ratio=2,
-            upsampler="nearest+conv",
-            resi_connection="1conv",
+            model = Swin2SR(
+                upscale=scale,
+                in_chans=3,
+                img_size=64,
+                window_size=8,
+                img_range=1.0,
+                depths=[6, 6, 6, 6, 6, 6],
+                embed_dim=180,
+                num_heads=[6, 6, 6, 6, 6, 6],
+                mlp_ratio=2,
+                upsampler="nearest+conv",
+                resi_connection="1conv",
             )
             params = None
         else:
-            model = net(
+            model = SwinIR(
                 upscale=scale,
                 in_chans=3,
                 img_size=64,

modules/esrgan_model.py

@@ -1,15 +1,13 @@
-import os
+import sys
 
 import numpy as np
 import torch
 from PIL import Image
-from basicsr.utils.download_util import load_file_from_url
 
 import modules.esrgan_model_arch as arch
 from modules import modelloader, images, devices
-from modules.upscaler import Upscaler, UpscalerData
 from modules.shared import opts
-
+from modules.upscaler import Upscaler, UpscalerData
 
 
 def mod2normal(state_dict):
@@ -134,7 +132,7 @@ def __init__(self, dirname):
             scaler_data = UpscalerData(self.model_name, self.model_url, self, 4)
             scalers.append(scaler_data)
         for file in model_paths:
-            if "http" in file:
+            if file.startswith("http"):
                 name = self.model_name
             else:
                 name = modelloader.friendly_name(file)
@@ -143,26 +141,25 @@ def __init__(self, dirname):
             self.scalers.append(scaler_data)
 
     def do_upscale(self, img, selected_model):
-        model = self.load_model(selected_model)
-        if model is None:
+        try:
+            model = self.load_model(selected_model)
+        except Exception as e:
+            print(f"Unable to load ESRGAN model {selected_model}: {e}", file=sys.stderr)
             return img
         model.to(devices.device_esrgan)
         img = esrgan_upscale(model, img)
         return img
 
     def load_model(self, path: str):
-        if "http" in path:
-            filename = load_file_from_url(
+        if path.startswith("http"):
+            # TODO: this doesn't use `path` at all?
+            filename = modelloader.load_file_from_url(
                 url=self.model_url,
                 model_dir=self.model_download_path,
                 file_name=f"{self.model_name}.pth",
-                progress=True,
             )
         else:
             filename = path
-        if not os.path.exists(filename) or filename is None:
-            print(f"Unable to load {self.model_path} from {filename}")
-            return None
 
         state_dict = torch.load(filename, map_location='cpu' if devices.device_esrgan.type == 'mps' else None)
 

modules/gfpgan_model.py

@@ -25,7 +25,7 @@ def gfpgann():
         return None
 
     models = modelloader.load_models(model_path, model_url, user_path, ext_filter="GFPGAN")
-    if len(models) == 1 and "http" in models[0]:
+    if len(models) == 1 and models[0].startswith("http"):
         model_file = models[0]
     elif len(models) != 0:
         latest_file = max(models, key=os.path.getctime)

modules/modelloader.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import os
 import shutil
 import importlib
@@ -8,6 +10,29 @@
 from modules.paths import script_path, models_path
 
 
+def load_file_from_url(
+    url: str,
+    *,
+    model_dir: str,
+    progress: bool = True,
+    file_name: str | None = None,
+) -> str:
+    """Download a file from `url` into `model_dir`, using the file present if possible.
+
+    Returns the path to the downloaded file.
+    """
+    os.makedirs(model_dir, exist_ok=True)
+    if not file_name:
+        parts = urlparse(url)
+        file_name = os.path.basename(parts.path)
+    cached_file = os.path.abspath(os.path.join(model_dir, file_name))
+    if not os.path.exists(cached_file):
+        print(f'Downloading: "{url}" to {cached_file}\n')
+        from torch.hub import download_url_to_file
+        download_url_to_file(url, cached_file, progress=progress)
+    return cached_file
+
+
 def load_models(model_path: str, model_url: str = None, command_path: str = None, ext_filter=None, download_name=None, ext_blacklist=None) -> list:
     """
     A one-and done loader to try finding the desired models in specified directories.
@@ -46,9 +71,7 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None
 
         if model_url is not None and len(output) == 0:
             if download_name is not None:
-                from basicsr.utils.download_util import load_file_from_url
-                dl = load_file_from_url(model_url, places[0], True, download_name)
-                output.append(dl)
+                output.append(load_file_from_url(model_url, model_dir=places[0], file_name=download_name))
             else:
                 output.append(model_url)
 
@@ -59,7 +82,7 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None
 
 
 def friendly_name(file: str):
-    if "http" in file:
+    if file.startswith("http"):
         file = urlparse(file).path
 
     file = os.path.basename(file)

modules/realesrgan_model.py

@@ -2,7 +2,6 @@
 
 import numpy as np
 from PIL import Image
-from basicsr.utils.download_util import load_file_from_url
 from realesrgan import RealESRGANer
 
 from modules.upscaler import Upscaler, UpscalerData
@@ -43,9 +42,10 @@ def do_upscale(self, img, path):
         if not self.enable:
             return img
 
-        info = self.load_model(path)
-        if not os.path.exists(info.local_data_path):
-            print(f"Unable to load RealESRGAN model: {info.name}")
+        try:
+            info = self.load_model(path)
+        except Exception:
+            errors.report(f"Unable to load RealESRGAN model {path}", exc_info=True)
             return img
 
         upsampler = RealESRGANer(
@@ -63,20 +63,17 @@ def do_upscale(self, img, path):
         return image
 
     def load_model(self, path):
-        try:
-            info = next(iter([scaler for scaler in self.scalers if scaler.data_path == path]), None)
-
-            if info is None:
-                print(f"Unable to find model info: {path}")
-                return None
-
-            if info.local_data_path.startswith("http"):
-                info.local_data_path = load_file_from_url(url=info.data_path, model_dir=self.model_download_path, progress=True)
-
-            return info
-        except Exception:
-            errors.report("Error making Real-ESRGAN models list", exc_info=True)
-        return None
+        for scaler in self.scalers:
+            if scaler.data_path == path:
+                if scaler.local_data_path.startswith("http"):
+                    scaler.local_data_path = modelloader.load_file_from_url(
+                        scaler.data_path,
+                        model_dir=self.model_download_path,
+                    )
+                if not os.path.exists(scaler.local_data_path):
+                    raise FileNotFoundError(f"RealESRGAN data missing: {scaler.local_data_path}")
+                return scaler
+        raise ValueError(f"Unable to find model info: {path}")
 
     def load_models(self, _):
         return get_realesrgan_models(self)