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Stable Diffusion Webui
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a26fc283
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前往新版Gitcode,体验更适合开发者的 AI 搜索 >>
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a26fc283
编写于
10月 21, 2022
作者:
A
AUTOMATIC1111
提交者:
GitHub
10月 21, 2022
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差异文件
Merge pull request #3199 from discus0434/master
Add features to insert activation functions to hypernetworks
上级
12a97c53
f8733ad0
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
23 addition
and
11 deletion
+23
-11
modules/hypernetworks/hypernetwork.py
modules/hypernetworks/hypernetwork.py
+19
-10
modules/hypernetworks/ui.py
modules/hypernetworks/ui.py
+2
-1
modules/ui.py
modules/ui.py
+2
-0
未找到文件。
modules/hypernetworks/hypernetwork.py
浏览文件 @
a26fc283
...
...
@@ -22,16 +22,20 @@ from modules.textual_inversion.learn_schedule import LearnRateScheduler
class
HypernetworkModule
(
torch
.
nn
.
Module
):
multiplier
=
1.0
def
__init__
(
self
,
dim
,
state_dict
=
None
,
layer_structure
=
None
,
add_layer_norm
=
False
):
def
__init__
(
self
,
dim
,
state_dict
=
None
,
layer_structure
=
None
,
add_layer_norm
=
False
,
activation_func
=
None
):
super
().
__init__
()
assert
layer_structure
is
not
None
,
"layer_structure mut not be None"
assert
layer_structure
is
not
None
,
"layer_structure mu
s
t not be None"
assert
layer_structure
[
0
]
==
1
,
"Multiplier Sequence should start with size 1!"
assert
layer_structure
[
-
1
]
==
1
,
"Multiplier Sequence should end with size 1!"
linears
=
[]
for
i
in
range
(
len
(
layer_structure
)
-
1
):
linears
.
append
(
torch
.
nn
.
Linear
(
int
(
dim
*
layer_structure
[
i
]),
int
(
dim
*
layer_structure
[
i
+
1
])))
if
activation_func
==
"relu"
:
linears
.
append
(
torch
.
nn
.
ReLU
())
if
activation_func
==
"leakyrelu"
:
linears
.
append
(
torch
.
nn
.
LeakyReLU
())
if
add_layer_norm
:
linears
.
append
(
torch
.
nn
.
LayerNorm
(
int
(
dim
*
layer_structure
[
i
+
1
])))
...
...
@@ -42,8 +46,9 @@ class HypernetworkModule(torch.nn.Module):
self
.
load_state_dict
(
state_dict
)
else
:
for
layer
in
self
.
linear
:
layer
.
weight
.
data
.
normal_
(
mean
=
0.0
,
std
=
0.01
)
layer
.
bias
.
data
.
zero_
()
if
not
"ReLU"
in
layer
.
__str__
():
layer
.
weight
.
data
.
normal_
(
mean
=
0.0
,
std
=
0.01
)
layer
.
bias
.
data
.
zero_
()
self
.
to
(
devices
.
device
)
...
...
@@ -69,7 +74,8 @@ class HypernetworkModule(torch.nn.Module):
def
trainables
(
self
):
layer_structure
=
[]
for
layer
in
self
.
linear
:
layer_structure
+=
[
layer
.
weight
,
layer
.
bias
]
if
not
"ReLU"
in
layer
.
__str__
():
layer_structure
+=
[
layer
.
weight
,
layer
.
bias
]
return
layer_structure
...
...
@@ -81,7 +87,7 @@ class Hypernetwork:
filename
=
None
name
=
None
def
__init__
(
self
,
name
=
None
,
enable_sizes
=
None
,
layer_structure
=
None
,
add_layer_norm
=
False
):
def
__init__
(
self
,
name
=
None
,
enable_sizes
=
None
,
layer_structure
=
None
,
add_layer_norm
=
False
,
activation_func
=
None
):
self
.
filename
=
None
self
.
name
=
name
self
.
layers
=
{}
...
...
@@ -90,11 +96,12 @@ class Hypernetwork:
self
.
sd_checkpoint_name
=
None
self
.
layer_structure
=
layer_structure
self
.
add_layer_norm
=
add_layer_norm
self
.
activation_func
=
activation_func
for
size
in
enable_sizes
or
[]:
self
.
layers
[
size
]
=
(
HypernetworkModule
(
size
,
None
,
self
.
layer_structure
,
self
.
add_layer_norm
),
HypernetworkModule
(
size
,
None
,
self
.
layer_structure
,
self
.
add_layer_norm
),
HypernetworkModule
(
size
,
None
,
self
.
layer_structure
,
self
.
add_layer_norm
,
self
.
activation_func
),
HypernetworkModule
(
size
,
None
,
self
.
layer_structure
,
self
.
add_layer_norm
,
self
.
activation_func
),
)
def
weights
(
self
):
...
...
@@ -117,6 +124,7 @@ class Hypernetwork:
state_dict
[
'name'
]
=
self
.
name
state_dict
[
'layer_structure'
]
=
self
.
layer_structure
state_dict
[
'is_layer_norm'
]
=
self
.
add_layer_norm
state_dict
[
'activation_func'
]
=
self
.
activation_func
state_dict
[
'sd_checkpoint'
]
=
self
.
sd_checkpoint
state_dict
[
'sd_checkpoint_name'
]
=
self
.
sd_checkpoint_name
...
...
@@ -131,12 +139,13 @@ class Hypernetwork:
self
.
layer_structure
=
state_dict
.
get
(
'layer_structure'
,
[
1
,
2
,
1
])
self
.
add_layer_norm
=
state_dict
.
get
(
'is_layer_norm'
,
False
)
self
.
activation_func
=
state_dict
.
get
(
'activation_func'
,
None
)
for
size
,
sd
in
state_dict
.
items
():
if
type
(
size
)
==
int
:
self
.
layers
[
size
]
=
(
HypernetworkModule
(
size
,
sd
[
0
],
self
.
layer_structure
,
self
.
add_layer_norm
),
HypernetworkModule
(
size
,
sd
[
1
],
self
.
layer_structure
,
self
.
add_layer_norm
),
HypernetworkModule
(
size
,
sd
[
0
],
self
.
layer_structure
,
self
.
add_layer_norm
,
self
.
activation_func
),
HypernetworkModule
(
size
,
sd
[
1
],
self
.
layer_structure
,
self
.
add_layer_norm
,
self
.
activation_func
),
)
self
.
name
=
state_dict
.
get
(
'name'
,
self
.
name
)
...
...
modules/hypernetworks/ui.py
浏览文件 @
a26fc283
...
...
@@ -10,7 +10,7 @@ from modules import sd_hijack, shared, devices
from
modules.hypernetworks
import
hypernetwork
def
create_hypernetwork
(
name
,
enable_sizes
,
layer_structure
=
None
,
add_layer_norm
=
False
):
def
create_hypernetwork
(
name
,
enable_sizes
,
layer_structure
=
None
,
add_layer_norm
=
False
,
activation_func
=
None
):
fn
=
os
.
path
.
join
(
shared
.
cmd_opts
.
hypernetwork_dir
,
f
"
{
name
}
.pt"
)
assert
not
os
.
path
.
exists
(
fn
),
f
"file
{
fn
}
already exists"
...
...
@@ -22,6 +22,7 @@ def create_hypernetwork(name, enable_sizes, layer_structure=None, add_layer_norm
enable_sizes
=
[
int
(
x
)
for
x
in
enable_sizes
],
layer_structure
=
layer_structure
,
add_layer_norm
=
add_layer_norm
,
activation_func
=
activation_func
,
)
hypernet
.
save
(
fn
)
...
...
modules/ui.py
浏览文件 @
a26fc283
...
...
@@ -1224,6 +1224,7 @@ def create_ui(wrap_gradio_gpu_call):
new_hypernetwork_sizes
=
gr
.
CheckboxGroup
(
label
=
"Modules"
,
value
=
[
"768"
,
"320"
,
"640"
,
"1280"
],
choices
=
[
"768"
,
"320"
,
"640"
,
"1280"
])
new_hypernetwork_layer_structure
=
gr
.
Textbox
(
"1, 2, 1"
,
label
=
"Enter hypernetwork layer structure"
,
placeholder
=
"1st and last digit must be 1. ex:'1, 2, 1'"
)
new_hypernetwork_add_layer_norm
=
gr
.
Checkbox
(
label
=
"Add layer normalization"
)
new_hypernetwork_activation_func
=
gr
.
Dropdown
(
value
=
"relu"
,
label
=
"Select activation function of hypernetwork"
,
choices
=
[
"linear"
,
"relu"
,
"leakyrelu"
])
with
gr
.
Row
():
with
gr
.
Column
(
scale
=
3
):
...
...
@@ -1308,6 +1309,7 @@ def create_ui(wrap_gradio_gpu_call):
new_hypernetwork_sizes
,
new_hypernetwork_layer_structure
,
new_hypernetwork_add_layer_norm
,
new_hypernetwork_activation_func
,
],
outputs
=
[
train_hypernetwork_name
,
...
...
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