Merge branch 'master' into xygrid_infotext_improvements

1 files changed, 0 insertions, 78 deletions

diff --git a/modules/bsrgan_model.py b/modules/bsrgan_model.py
deleted file mode 100644
index e62c6657..00000000
--- a/modules/bsrgan_model.py
+++ /dev/null
@@ -1,78 +0,0 @@
-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(f"BSRGAN: Unable to load model from {filename}", file=sys.stderr)
-            return None
-        model = RRDBNet(in_nc=3, out_nc=3, nf=64, nb=23, gc=32, sf=4)  # 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
-