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stable-diffusion-webui
Commits
d8acd34f
Unverified
Commit
d8acd34f
authored
Oct 20, 2022
by
AngelBottomless
Committed by
GitHub
Oct 20, 2022
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generalized some functions and option for ignoring first layer
parent
f8733ad0
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1 changed file
with
15 additions
and
8 deletions
+15
-8
hypernetwork.py
modules/hypernetworks/hypernetwork.py
+15
-8
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modules/hypernetworks/hypernetwork.py
View file @
d8acd34f
...
...
@@ -21,21 +21,27 @@ from modules.textual_inversion.learn_schedule import LearnRateScheduler
class
HypernetworkModule
(
torch
.
nn
.
Module
):
multiplier
=
1.0
activation_dict
=
{
"relu"
:
torch
.
nn
.
ReLU
,
"leakyrelu"
:
torch
.
nn
.
LeakyReLU
,
"elu"
:
torch
.
nn
.
ELU
,
"swish"
:
torch
.
nn
.
Hardswish
}
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 must 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 skip_first_layer because first parameters potentially contain negative values
if
i
<
1
:
continue
if
activation_func
in
HypernetworkModule
.
activation_dict
:
linears
.
append
(
HypernetworkModule
.
activation_dict
[
activation_func
]())
else
:
print
(
"Invalid key {} encountered as activation function!"
.
format
(
activation_func
))
# if use_dropout:
linears
.
append
(
torch
.
nn
.
Dropout
(
p
=
0.3
))
if
add_layer_norm
:
linears
.
append
(
torch
.
nn
.
LayerNorm
(
int
(
dim
*
layer_structure
[
i
+
1
])))
...
...
@@ -46,7 +52,7 @@ class HypernetworkModule(torch.nn.Module):
self
.
load_state_dict
(
state_dict
)
else
:
for
layer
in
self
.
linear
:
if
not
"ReLU"
in
layer
.
__str__
(
):
if
isinstance
(
layer
,
torch
.
nn
.
Linear
):
layer
.
weight
.
data
.
normal_
(
mean
=
0.0
,
std
=
0.01
)
layer
.
bias
.
data
.
zero_
()
...
...
@@ -298,7 +304,8 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
return
hypernetwork
,
filename
scheduler
=
LearnRateScheduler
(
learn_rate
,
steps
,
ititial_step
)
optimizer
=
torch
.
optim
.
AdamW
(
weights
,
lr
=
scheduler
.
learn_rate
)
# if optimizer == "Adam": or else Adam / AdamW / etc...
optimizer
=
torch
.
optim
.
Adam
(
weights
,
lr
=
scheduler
.
learn_rate
)
pbar
=
tqdm
.
tqdm
(
enumerate
(
ds
),
total
=
steps
-
ititial_step
)
for
i
,
entries
in
pbar
:
...
...
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