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authorAUTOMATIC1111 <16777216c@gmail.com>2023-05-22 07:15:34 +0300
committerGitHub <noreply@github.com>2023-05-22 07:15:34 +0300
commit8137bdba61fd57cc1ddae801f6080d51e13d70c5 (patch)
treec5a02e9f9ae57c9f0ff8499379c6cc61a97c094e /extensions-builtin/SwinIR/scripts
parenta862428902c4aecde8852761c3a4d95c196885cb (diff)
parent3366e494a1147e570d8527eea19da88edb3a1e0c (diff)
Merge branch 'dev' into text-drag-fix
Diffstat (limited to 'extensions-builtin/SwinIR/scripts')
-rw-r--r--extensions-builtin/SwinIR/scripts/swinir_model.py9
1 files changed, 4 insertions, 5 deletions
diff --git a/extensions-builtin/SwinIR/scripts/swinir_model.py b/extensions-builtin/SwinIR/scripts/swinir_model.py
index e8783bca..1c7bf325 100644
--- a/extensions-builtin/SwinIR/scripts/swinir_model.py
+++ b/extensions-builtin/SwinIR/scripts/swinir_model.py
@@ -1,4 +1,3 @@
-import contextlib
import os
import numpy as np
@@ -8,7 +7,7 @@ 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 cmd_opts, opts, state
+from modules.shared import opts, state
from swinir_model_arch import SwinIR as net
from swinir_model_arch_v2 import Swin2SR as net2
from modules.upscaler import Upscaler, UpscalerData
@@ -45,14 +44,14 @@ class UpscalerSwinIR(Upscaler):
img = upscale(img, model)
try:
torch.cuda.empty_cache()
- except:
+ except Exception:
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)
+ filename = load_file_from_url(url=path, model_dir=self.model_download_path, file_name=dl_name, progress=True)
else:
filename = path
if filename is None or not os.path.exists(filename):
@@ -151,7 +150,7 @@ def inference(img, model, tile, tile_overlap, window_size, scale):
for w_idx in w_idx_list:
if state.interrupted or state.skipped:
break
-
+
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)