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Stable Diffusion Webui
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aa7ff2a1
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Stable Diffusion Webui
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aa7ff2a1
编写于
10月 19, 2022
作者:
R
random_thoughtss
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电子邮件补丁
差异文件
Fixed non-square highres fix generation
上级
c418467c
变更
1
隐藏空白更改
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并排
Showing
1 changed file
with
6 addition
and
3 deletion
+6
-3
modules/processing.py
modules/processing.py
+6
-3
未找到文件。
modules/processing.py
浏览文件 @
aa7ff2a1
...
...
@@ -541,10 +541,13 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
self
.
truncate_y
=
int
(
self
.
firstphase_height
-
firstphase_height_truncated
)
//
opt_f
def
create_dummy_mask
(
self
,
x
):
def
create_dummy_mask
(
self
,
x
,
first_phase
:
bool
=
False
):
if
self
.
sampler
.
conditioning_key
in
{
'hybrid'
,
'concat'
}:
height
=
self
.
firstphase_height
if
first_phase
else
self
.
height
width
=
self
.
firstphase_width
if
first_phase
else
self
.
width
# The "masked-image" in this case will just be all zeros since the entire image is masked.
image_conditioning
=
torch
.
zeros
(
x
.
shape
[
0
],
3
,
self
.
height
,
self
.
width
,
device
=
x
.
device
)
image_conditioning
=
torch
.
zeros
(
x
.
shape
[
0
],
3
,
height
,
width
,
device
=
x
.
device
)
image_conditioning
=
self
.
sd_model
.
get_first_stage_encoding
(
self
.
sd_model
.
encode_first_stage
(
image_conditioning
))
# Add the fake full 1s mask to the first dimension.
...
...
@@ -567,7 +570,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
return
samples
x
=
create_random_tensors
([
opt_C
,
self
.
firstphase_height
//
opt_f
,
self
.
firstphase_width
//
opt_f
],
seeds
=
seeds
,
subseeds
=
subseeds
,
subseed_strength
=
self
.
subseed_strength
,
seed_resize_from_h
=
self
.
seed_resize_from_h
,
seed_resize_from_w
=
self
.
seed_resize_from_w
,
p
=
self
)
samples
=
self
.
sampler
.
sample
(
self
,
x
,
conditioning
,
unconditional_conditioning
,
image_conditioning
=
self
.
create_dummy_mask
(
x
))
samples
=
self
.
sampler
.
sample
(
self
,
x
,
conditioning
,
unconditional_conditioning
,
image_conditioning
=
self
.
create_dummy_mask
(
x
,
first_phase
=
True
))
samples
=
samples
[:,
:,
self
.
truncate_y
//
2
:
samples
.
shape
[
2
]
-
self
.
truncate_y
//
2
,
self
.
truncate_x
//
2
:
samples
.
shape
[
3
]
-
self
.
truncate_x
//
2
]
...
...
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