Re-implement universal model loading

modules/swinir_model.py

@@ -1,21 +1,39 @@
+import contextlib
+import os
 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
 
+import numpy as np
+import torch
+from PIL import Image
+from basicsr.utils.download_util import load_file_from_url
+
+import modules.images
+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
+
+model_dir = "SwinIR"
+model_url = "https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR-L_x4_GAN.pth"
+model_name = "SwinIR x4"
+model_path = os.path.join(models_path, model_dir)
+cmd_path = ""
 precision_scope = (
     torch.autocast if cmd_opts.precision == "autocast" else contextlib.nullcontext
 )
 
 
-def load_model(filename, scale=4):
+def load_model(path, scale=4):
+    global model_path
+    global model_name
+    if "http" in path:
+        dl_name = "%s%s" % (model_name.replace(" ", "_"), ".pth")
+        filename = load_file_from_url(url=path, model_dir=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,
@@ -37,19 +55,29 @@ def load_model(filename, scale=4):
     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)
+def setup_model(dirname):
+    global model_path
+    global model_name
+    global cmd_path
+    if not os.path.exists(model_path):
+        os.makedirs(model_path)
+    cmd_path = dirname
+    model_file = ""
+    try:
+        models = modelloader.load_models(model_path, ext_filter=[".pt", ".pth"], command_path=cmd_path)
 
-        if extension != ".pt" and extension != ".pth":
-            continue
+        if len(models) != 0:
+            model_file = models[0]
+            name = modelloader.friendly_name(model_file)
+        else:
+            # Add the "default" model if none are found.
+            model_file = model_url
+            name = model_name
 
-        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)
+        modules.shared.sd_upscalers.append(UpscalerSwin(model_file, name))
+    except Exception:
+        print(f"Error loading SwinIR model: {model_file}", file=sys.stderr)
+        print(traceback.format_exc(), file=sys.stderr)
 
 
 def upscale(
@@ -115,9 +143,16 @@ def inference(img, model, tile, tile_overlap, window_size, scale):
 class UpscalerSwin(modules.images.Upscaler):
     def __init__(self, filename, title):
         self.name = title
-        self.model = load_model(filename)
+        self.filename = filename
 
     def do_upscale(self, img):
-        model = self.model.to(device)
+        model = load_model(self.filename)
+        if model is None:
+            return img
+        model = model.to(device)
         img = upscale(img, model)
-        return img
+        try:
+            torch.cuda.empty_cache()
+        except:
+            pass
+        return img