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authorbrkirch <brkirch@users.noreply.github.com>2022-10-04 04:24:35 -0400
committerbrkirch <brkirch@users.noreply.github.com>2022-10-04 04:24:35 -0400
commiteeab7aedf532680a6ae9058ee272450bb07e41eb (patch)
treeb5434f872fefd7642cc322b63c7e0aa90c575ebd /modules/esrgan_model.py
parentb88e4ea7d6536ddc814a9101a258f87b06230019 (diff)
Add --use-cpu command line option
Remove MPS detection to use CPU for GFPGAN / CodeFormer and add a --use-cpu command line option.
Diffstat (limited to 'modules/esrgan_model.py')
-rw-r--r--modules/esrgan_model.py9
1 files changed, 4 insertions, 5 deletions
diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py
index 4aed9283..d17e730f 100644
--- a/modules/esrgan_model.py
+++ b/modules/esrgan_model.py
@@ -6,8 +6,7 @@ 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, modelloader, images
-from modules.devices import has_mps
+from modules import shared, modelloader, images, devices
from modules.paths import models_path
from modules.upscaler import Upscaler, UpscalerData
from modules.shared import opts
@@ -97,7 +96,7 @@ class UpscalerESRGAN(Upscaler):
model = self.load_model(selected_model)
if model is None:
return img
- model.to(shared.device)
+ model.to(devices.device_esrgan)
img = esrgan_upscale(model, img)
return img
@@ -112,7 +111,7 @@ class UpscalerESRGAN(Upscaler):
print("Unable to load %s from %s" % (self.model_path, filename))
return None
- pretrained_net = torch.load(filename, map_location='cpu' if has_mps else None)
+ pretrained_net = torch.load(filename, map_location='cpu' if shared.device.type == 'mps' else None)
crt_model = arch.RRDBNet(3, 3, 64, 23, gc=32)
pretrained_net = fix_model_layers(crt_model, pretrained_net)
@@ -127,7 +126,7 @@ def upscale_without_tiling(model, img):
img = img[:, :, ::-1]
img = np.moveaxis(img, 2, 0) / 255
img = torch.from_numpy(img).float()
- img = img.unsqueeze(0).to(shared.device)
+ img = img.unsqueeze(0).to(devices.device_esrgan)
with torch.no_grad():
output = model(img)
output = output.squeeze().float().cpu().clamp_(0, 1).numpy()