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novelai-storage
Stable Diffusion Webui
Commits
948eff4b
Commit
948eff4b
authored
Sep 20, 2022
by
C43H66N12O12S2
Committed by
AUTOMATIC1111
Sep 20, 2022
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make swinir actually useful
parent
7267b7d2
Changes
1
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1 changed file
with
23 additions
and
5 deletions
+23
-5
modules/swinir.py
modules/swinir.py
+23
-5
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swinir.py
→
modules/
swinir.py
View file @
948eff4b
...
@@ -12,7 +12,13 @@ import modules.images
...
@@ -12,7 +12,13 @@ import modules.images
from
modules.shared
import
cmd_opts
,
opts
,
device
from
modules.shared
import
cmd_opts
,
opts
,
device
from
modules.swinir_arch
import
SwinIR
as
net
from
modules.swinir_arch
import
SwinIR
as
net
precision_scope
=
torch
.
autocast
if
cmd_opts
.
precision
==
"autocast"
else
contextlib
.
nullcontext
precision_scope
=
torch
.
autocast
if
cmd_opts
.
precision
==
"autocast"
else
contextlib
.
nullcontext
def
load_model
(
task
=
"realsr"
,
large_model
=
True
,
model_path
=
next
(
os
.
listdir
(
cmd_opts
.
esrgan_models_path
))):
def
load_model
(
task
=
"realsr"
,
large_model
=
True
,
model_path
=
"C:/sd/ESRGANn/4x-large.pth"
,
scale
=
4
):
try
:
modules
.
shared
.
sd_upscalers
.
append
(
UpscalerSwin
(
"McSwinnySwin"
))
except
Exception
:
print
(
f
"Error loading ESRGAN model"
,
file
=
sys
.
stderr
)
print
(
traceback
.
format_exc
(),
file
=
sys
.
stderr
)
if
not
large_model
:
if
not
large_model
:
# use 'nearest+conv' to avoid block artifacts
# use 'nearest+conv' to avoid block artifacts
model
=
net
(
upscale
=
scale
,
in_chans
=
3
,
img_size
=
64
,
window_size
=
8
,
model
=
net
(
upscale
=
scale
,
in_chans
=
3
,
img_size
=
64
,
window_size
=
8
,
...
@@ -26,12 +32,16 @@ def load_model(task = "realsr", large_model = True, model_path=next(os.listdir(c
...
@@ -26,12 +32,16 @@ def load_model(task = "realsr", large_model = True, model_path=next(os.listdir(c
mlp_ratio
=
2
,
upsampler
=
'nearest+conv'
,
resi_connection
=
'3conv'
)
mlp_ratio
=
2
,
upsampler
=
'nearest+conv'
,
resi_connection
=
'3conv'
)
pretrained_model
=
torch
.
load
(
model_path
)
pretrained_model
=
torch
.
load
(
model_path
)
model
.
load_state_dict
(
pretrained_model
,
strict
=
True
)
model
.
load_state_dict
(
pretrained_model
[
"params_ema"
]
,
strict
=
True
)
return
model
.
half
()
.
to
(
device
)
return
model
.
half
()
.
to
(
device
)
def
upscale
(
img
,
tile
=
opts
.
ESRGAN_tile
,
tile_overlap
=
opts
.
ESRGAN_tile_overlap
,
window_size
=
8
,
scale
=
4
):
def
upscale
(
img
,
tile
=
opts
.
ESRGAN_tile
,
tile_overlap
=
opts
.
ESRGAN_tile_overlap
,
window_size
=
8
,
scale
=
4
):
img
=
cv2
.
imread
(
img
,
cv2
.
IMREAD_COLOR
)
.
astype
(
np
.
float16
)
/
255.
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
(
device
)
model
=
load_model
()
model
=
load_model
()
with
torch
.
no_grad
(),
precision_scope
(
"cuda"
):
with
torch
.
no_grad
(),
precision_scope
(
"cuda"
):
_
,
_
,
h_old
,
w_old
=
img
.
size
()
_
,
_
,
h_old
,
w_old
=
img
.
size
()
...
@@ -45,7 +55,7 @@ def upscale(img, tile=opts.ESRGAN_tile, tile_overlap=opts.ESRGAN_tile_overlap, w
...
@@ -45,7 +55,7 @@ def upscale(img, tile=opts.ESRGAN_tile, tile_overlap=opts.ESRGAN_tile_overlap, w
if
output
.
ndim
==
3
:
if
output
.
ndim
==
3
:
output
=
np
.
transpose
(
output
[[
2
,
1
,
0
],
:,
:],
(
1
,
2
,
0
))
# CHW-RGB to HCW-BGR
output
=
np
.
transpose
(
output
[[
2
,
1
,
0
],
:,
:],
(
1
,
2
,
0
))
# CHW-RGB to HCW-BGR
output
=
(
output
*
255.0
)
.
round
()
.
astype
(
np
.
uint8
)
# float32 to uint8
output
=
(
output
*
255.0
)
.
round
()
.
astype
(
np
.
uint8
)
# float32 to uint8
return
output
return
Image
.
fromarray
(
output
,
'RGB'
)
def
inference
(
img
,
model
,
tile
,
tile_overlap
,
window_size
,
scale
):
def
inference
(
img
,
model
,
tile
,
tile_overlap
,
window_size
,
scale
):
...
@@ -72,3 +82,11 @@ def inference(img, model, tile, tile_overlap, window_size, scale):
...
@@ -72,3 +82,11 @@ def inference(img, model, tile, tile_overlap, window_size, scale):
output
=
E
.
div_
(
W
)
output
=
E
.
div_
(
W
)
return
output
return
output
class
UpscalerSwin
(
modules
.
images
.
Upscaler
):
def
__init__
(
self
,
title
):
self
.
name
=
title
def
do_upscale
(
self
,
img
):
img
=
upscale
(
img
)
return
img
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