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Stable Diffusion Webui
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92a17a7a
S
Stable Diffusion Webui
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Stable Diffusion Webui
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体验新版 GitCode,发现更多精彩内容 >>
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92a17a7a
编写于
10月 20, 2022
作者:
R
random_thoughtss
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差异文件
Made dummy latents smaller. Minor code cleanups
上级
aa7ff2a1
变更
2
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2 changed file
with
8 addition
and
5 deletion
+8
-5
modules/processing.py
modules/processing.py
+4
-3
modules/sd_samplers.py
modules/sd_samplers.py
+4
-2
未找到文件。
modules/processing.py
浏览文件 @
92a17a7a
...
...
@@ -557,7 +557,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
else
:
# Dummy zero conditioning if we're not using inpainting model.
# Still takes up a bit of memory, but no encoder call.
image_conditioning
=
torch
.
zeros
(
x
.
shape
[
0
],
5
,
x
.
shape
[
-
2
],
x
.
shape
[
-
1
],
dtype
=
x
.
dtype
,
device
=
x
.
device
)
# Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
image_conditioning
=
torch
.
zeros
(
x
.
shape
[
0
],
5
,
1
,
1
,
dtype
=
x
.
dtype
,
device
=
x
.
device
)
return
image_conditioning
...
...
@@ -759,7 +760,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
self
.
image_conditioning
=
self
.
image_conditioning
.
to
(
shared
.
device
).
type
(
self
.
sd_model
.
dtype
)
else
:
self
.
image_conditioning
=
torch
.
zeros
(
self
.
init_latent
.
shape
[
0
],
5
,
self
.
init_latent
.
shape
[
-
2
],
self
.
init_latent
.
shape
[
-
1
],
self
.
init_latent
.
shape
[
0
],
5
,
1
,
1
,
dtype
=
self
.
init_latent
.
dtype
,
device
=
self
.
init_latent
.
device
)
...
...
modules/sd_samplers.py
浏览文件 @
92a17a7a
...
...
@@ -138,7 +138,7 @@ class VanillaStableDiffusionSampler:
if
self
.
stop_at
is
not
None
and
self
.
step
>
self
.
stop_at
:
raise
InterruptedException
# Have to unwrap the inpainting conditioning here to perform pre-pr
e
ocessing
# Have to unwrap the inpainting conditioning here to perform pre-processing
image_conditioning
=
None
if
isinstance
(
cond
,
dict
):
image_conditioning
=
cond
[
"c_concat"
][
0
]
...
...
@@ -165,6 +165,8 @@ class VanillaStableDiffusionSampler:
img_orig
=
self
.
sampler
.
model
.
q_sample
(
self
.
init_latent
,
ts
)
x_dec
=
img_orig
*
self
.
mask
+
self
.
nmask
*
x_dec
# Wrap the image conditioning back up since the DDIM code can accept the dict directly.
# Note that they need to be lists because it just concatenates them later.
if
image_conditioning
is
not
None
:
cond
=
{
"c_concat"
:
[
image_conditioning
],
"c_crossattn"
:
[
cond
]}
unconditional_conditioning
=
{
"c_concat"
:
[
image_conditioning
],
"c_crossattn"
:
[
unconditional_conditioning
]}
...
...
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