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novelai-storage
Stable Diffusion Webui
Commits
6021f7a7
Commit
6021f7a7
authored
Oct 19, 2022
by
discus0434
Browse files
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add options to custom hypernetwork layer structure
parent
c1093b80
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4
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4 changed files
with
75 additions
and
24 deletions
+75
-24
.gitignore
.gitignore
+1
-0
modules/hypernetworks/hypernetwork.py
modules/hypernetworks/hypernetwork.py
+67
-21
modules/shared.py
modules/shared.py
+3
-1
webui.py
webui.py
+4
-2
No files found.
.gitignore
View file @
6021f7a7
...
...
@@ -27,3 +27,4 @@ __pycache__
notification.mp3
/SwinIR
/textual_inversion
/hypernetwork
modules/hypernetworks/hypernetwork.py
View file @
6021f7a7
import
csv
import
datetime
import
glob
import
html
import
os
import
sys
import
traceback
import
tqdm
import
csv
import
torch
from
ldm.util
import
default
from
modules
import
devices
,
shared
,
processing
,
sd_models
import
modules.textual_inversion.dataset
import
torch
from
torch
import
einsu
m
import
tqd
m
from
einops
import
rearrange
,
repeat
import
modules.textual_inversion.dataset
from
ldm.util
import
default
from
modules
import
devices
,
processing
,
sd_models
,
shared
from
modules.textual_inversion
import
textual_inversion
from
modules.textual_inversion.learn_schedule
import
LearnRateScheduler
from
torch
import
einsum
def
parse_layer_structure
(
dim
,
state_dict
):
i
=
0
res
=
[
1
]
while
(
key
:
=
"linear.{}.weight"
.
format
(
i
))
in
state_dict
:
weight
=
state_dict
[
key
]
res
.
append
(
len
(
weight
)
//
dim
)
i
+=
1
return
res
class
HypernetworkModule
(
torch
.
nn
.
Module
):
multiplier
=
1.0
layer_structure
=
None
add_layer_norm
=
False
def
__init__
(
self
,
dim
,
state_dict
=
None
):
super
()
.
__init__
()
if
(
state_dict
is
None
or
'linear.0.weight'
not
in
state_dict
)
and
self
.
layer_structure
is
None
:
layer_structure
=
(
1
,
2
,
1
)
else
:
if
self
.
layer_structure
is
not
None
:
assert
self
.
layer_structure
[
0
]
==
1
,
"Multiplier Sequence should start with size 1!"
assert
self
.
layer_structure
[
-
1
]
==
1
,
"Multiplier Sequence should end with size 1!"
layer_structure
=
self
.
layer_structure
else
:
layer_structure
=
parse_layer_structure
(
dim
,
state_dict
)
self
.
linear1
=
torch
.
nn
.
Linear
(
dim
,
dim
*
2
)
self
.
linear2
=
torch
.
nn
.
Linear
(
dim
*
2
,
dim
)
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
self
.
add_layer_norm
:
linears
.
append
(
torch
.
nn
.
LayerNorm
(
int
(
dim
*
layer_structure
[
i
+
1
])))
self
.
linear
=
torch
.
nn
.
Sequential
(
*
linears
)
if
state_dict
is
not
None
:
self
.
load_state_dict
(
state_dict
,
strict
=
True
)
try
:
self
.
load_state_dict
(
state_dict
)
except
RuntimeError
:
self
.
try_load_previous
(
state_dict
)
else
:
self
.
linear1
.
weight
.
data
.
normal_
(
mean
=
0.0
,
std
=
0.01
)
self
.
linear1
.
bias
.
data
.
zero_
()
self
.
linear2
.
weight
.
data
.
normal_
(
mean
=
0.0
,
std
=
0.01
)
self
.
linear2
.
bias
.
data
.
zero_
()
for
layer
in
self
.
linear
:
layer
.
weight
.
data
.
normal_
(
mean
=
0.0
,
std
=
0.01
)
layer
.
bias
.
data
.
zero_
()
self
.
to
(
devices
.
device
)
def
try_load_previous
(
self
,
state_dict
):
states
=
self
.
state_dict
()
states
[
'linear.0.bias'
]
.
copy_
(
state_dict
[
'linear1.bias'
])
states
[
'linear.0.weight'
]
.
copy_
(
state_dict
[
'linear1.weight'
])
states
[
'linear.1.bias'
]
.
copy_
(
state_dict
[
'linear2.bias'
])
states
[
'linear.1.weight'
]
.
copy_
(
state_dict
[
'linear2.weight'
])
def
forward
(
self
,
x
):
return
x
+
(
self
.
linear2
(
self
.
linear1
(
x
)))
*
self
.
multiplier
return
x
+
self
.
linear
(
x
)
*
self
.
multiplier
def
trainables
(
self
):
res
=
[]
for
layer
in
self
.
linear
:
res
+=
[
layer
.
weight
,
layer
.
bias
]
return
res
def
apply_strength
(
value
=
None
):
HypernetworkModule
.
multiplier
=
value
if
value
is
not
None
else
shared
.
opts
.
sd_hypernetwork_strength
def
apply_layer_structure
(
value
=
None
):
HypernetworkModule
.
layer_structure
=
value
if
value
is
not
None
else
shared
.
opts
.
sd_hypernetwork_layer_structure
def
apply_layer_norm
(
value
=
None
):
HypernetworkModule
.
add_layer_norm
=
value
if
value
is
not
None
else
shared
.
opts
.
sd_hypernetwork_add_layer_norm
class
Hypernetwork
:
filename
=
None
name
=
None
...
...
@@ -68,7 +114,7 @@ class Hypernetwork:
for
k
,
layers
in
self
.
layers
.
items
():
for
layer
in
layers
:
layer
.
train
()
res
+=
[
layer
.
linear1
.
weight
,
layer
.
linear1
.
bias
,
layer
.
linear2
.
weight
,
layer
.
linear2
.
bias
]
res
+=
layer
.
trainables
()
return
res
...
...
@@ -226,7 +272,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
shared
.
state
.
textinfo
=
f
"Preparing dataset from {html.escape(data_root)}..."
with
torch
.
autocast
(
"cuda"
):
ds
=
modules
.
textual_inversion
.
dataset
.
PersonalizedBase
(
data_root
=
data_root
,
width
=
512
,
height
=
512
,
repeats
=
shared
.
opts
.
training_image_repeats_per_epoch
,
placeholder_token
=
hypernetwork_name
,
model
=
shared
.
sd_model
,
device
=
devices
.
device
,
template_file
=
template_file
,
include_cond
=
True
,
batch_size
=
batch_size
)
assert
ds
.
length
>
1
,
"Dataset should contain more than 1 images"
if
unload
:
shared
.
sd_model
.
cond_stage_model
.
to
(
devices
.
cpu
)
shared
.
sd_model
.
first_stage_model
.
to
(
devices
.
cpu
)
...
...
@@ -261,7 +307,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
with
torch
.
autocast
(
"cuda"
):
c
=
stack_conds
([
entry
.
cond
for
entry
in
entries
])
.
to
(
devices
.
device
)
#
c = torch.vstack([entry.cond for entry in entries]).to(devices.device)
c
=
torch
.
vstack
([
entry
.
cond
for
entry
in
entries
])
.
to
(
devices
.
device
)
x
=
torch
.
stack
([
entry
.
latent
for
entry
in
entries
])
.
to
(
devices
.
device
)
loss
=
shared
.
sd_model
(
x
,
c
)[
0
]
del
x
...
...
@@ -283,7 +329,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
textual_inversion
.
write_loss
(
log_directory
,
"hypernetwork_loss.csv"
,
hypernetwork
.
step
,
len
(
ds
),
{
"loss"
:
f
"{mean_loss:.7f}"
,
"learn_rate"
:
scheduler
.
learn_rate
"learn_rate"
:
f
"{scheduler.learn_rate:.7f}"
})
if
hypernetwork
.
step
>
0
and
images_dir
is
not
None
and
hypernetwork
.
step
%
create_image_every
==
0
:
...
...
modules/shared.py
View file @
6021f7a7
...
...
@@ -13,7 +13,7 @@ import modules.memmon
import
modules.sd_models
import
modules.styles
import
modules.devices
as
devices
from
modules
import
sd_
samplers
,
sd_model
s
,
localization
from
modules
import
sd_
models
,
sd_sampler
s
,
localization
from
modules.hypernetworks
import
hypernetwork
from
modules.paths
import
models_path
,
script_path
,
sd_path
...
...
@@ -258,6 +258,8 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
"sd_model_checkpoint"
:
OptionInfo
(
None
,
"Stable Diffusion checkpoint"
,
gr
.
Dropdown
,
lambda
:
{
"choices"
:
modules
.
sd_models
.
checkpoint_tiles
()},
refresh
=
sd_models
.
list_models
),
"sd_checkpoint_cache"
:
OptionInfo
(
0
,
"Checkpoints to cache in RAM"
,
gr
.
Slider
,
{
"minimum"
:
0
,
"maximum"
:
10
,
"step"
:
1
}),
"sd_hypernetwork"
:
OptionInfo
(
"None"
,
"Hypernetwork"
,
gr
.
Dropdown
,
lambda
:
{
"choices"
:
[
"None"
]
+
[
x
for
x
in
hypernetworks
.
keys
()]},
refresh
=
reload_hypernetworks
),
"sd_hypernetwork_layer_structure"
:
OptionInfo
(
None
,
"Hypernetwork layer structure Default: (1,2,1)."
,
gr
.
Dropdown
,
lambda
:
{
"choices"
:
[(
1
,
2
,
1
),
(
1
,
2
,
2
,
1
),
(
1
,
2
,
4
,
2
,
1
)]}),
"sd_hypernetwork_add_layer_norm"
:
OptionInfo
(
False
,
"Add layer normalization to hypernetwork architecture."
),
"sd_hypernetwork_strength"
:
OptionInfo
(
1.0
,
"Hypernetwork strength"
,
gr
.
Slider
,
{
"minimum"
:
0.0
,
"maximum"
:
1.0
,
"step"
:
0.001
}),
"img2img_color_correction"
:
OptionInfo
(
False
,
"Apply color correction to img2img results to match original colors."
),
"save_images_before_color_correction"
:
OptionInfo
(
False
,
"Save a copy of image before applying color correction to img2img results"
),
...
...
webui.py
View file @
6021f7a7
...
...
@@ -86,6 +86,8 @@ def initialize():
shared
.
opts
.
onchange
(
"sd_model_checkpoint"
,
wrap_queued_call
(
lambda
:
modules
.
sd_models
.
reload_model_weights
(
shared
.
sd_model
)))
shared
.
opts
.
onchange
(
"sd_hypernetwork"
,
wrap_queued_call
(
lambda
:
modules
.
hypernetworks
.
hypernetwork
.
load_hypernetwork
(
shared
.
opts
.
sd_hypernetwork
)))
shared
.
opts
.
onchange
(
"sd_hypernetwork_strength"
,
modules
.
hypernetworks
.
hypernetwork
.
apply_strength
)
shared
.
opts
.
onchange
(
"sd_hypernetwork_layer_structure"
,
modules
.
hypernetworks
.
hypernetwork
.
apply_layer_structure
)
shared
.
opts
.
onchange
(
"sd_hypernetwork_add_layer_norm"
,
modules
.
hypernetworks
.
hypernetwork
.
apply_layer_norm
)
def
webui
():
...
...
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