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Stable Diffusion Webui
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24694e59
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Stable Diffusion Webui
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前往新版Gitcode,体验更适合开发者的 AI 搜索 >>
提交
24694e59
编写于
10月 23, 2022
作者:
A
AngelBottomless
提交者:
AUTOMATIC1111
10月 22, 2022
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差异文件
Update hypernetwork.py
上级
321bacc6
变更
1
隐藏空白更改
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并排
Showing
1 changed file
with
44 addition
and
11 deletion
+44
-11
modules/hypernetworks/hypernetwork.py
modules/hypernetworks/hypernetwork.py
+44
-11
未找到文件。
modules/hypernetworks/hypernetwork.py
浏览文件 @
24694e59
...
...
@@ -16,6 +16,7 @@ from modules.textual_inversion import textual_inversion
from
modules.textual_inversion.learn_schedule
import
LearnRateScheduler
from
torch
import
einsum
from
statistics
import
stdev
,
mean
class
HypernetworkModule
(
torch
.
nn
.
Module
):
multiplier
=
1.0
...
...
@@ -268,6 +269,32 @@ def stack_conds(conds):
return
torch
.
stack
(
conds
)
def
log_statistics
(
loss_info
:
dict
,
key
,
value
):
if
key
not
in
loss_info
:
loss_info
[
key
]
=
[
value
]
else
:
loss_info
[
key
].
append
(
value
)
if
len
(
loss_info
)
>
1024
:
loss_info
.
pop
(
0
)
def
statistics
(
data
):
total_information
=
f
"loss:
{
mean
(
data
):.
3
f
}
"
+
u
"
\u00B1
"
+
f
"(
{
stdev
(
data
)
/
(
len
(
data
)
**
0.5
):.
3
f
}
)"
recent_data
=
data
[
-
32
:]
recent_information
=
f
"recent 32 loss:
{
mean
(
recent_data
):.
3
f
}
"
+
u
"
\u00B1
"
+
f
"(
{
stdev
(
recent_data
)
/
(
len
(
recent_data
)
**
0.5
):.
3
f
}
)"
return
total_information
,
recent_information
def
report_statistics
(
loss_info
:
dict
):
keys
=
sorted
(
loss_info
.
keys
(),
key
=
lambda
x
:
sum
(
loss_info
[
x
])
/
len
(
loss_info
[
x
]))
for
key
in
keys
:
info
,
recent
=
statistics
(
loss_info
[
key
])
print
(
"Loss statistics for file "
+
key
)
print
(
info
)
print
(
recent
)
def
train_hypernetwork
(
hypernetwork_name
,
learn_rate
,
batch_size
,
data_root
,
log_directory
,
training_width
,
training_height
,
steps
,
create_image_every
,
save_hypernetwork_every
,
template_file
,
preview_from_txt2img
,
preview_prompt
,
preview_negative_prompt
,
preview_steps
,
preview_sampler_index
,
preview_cfg_scale
,
preview_seed
,
preview_width
,
preview_height
):
# images allows training previews to have infotext. Importing it at the top causes a circular import problem.
from
modules
import
images
...
...
@@ -310,7 +337,11 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
for
weight
in
weights
:
weight
.
requires_grad
=
True
losses
=
torch
.
zeros
((
32
,))
size
=
len
(
ds
.
indexes
)
loss_dict
=
{}
losses
=
torch
.
zeros
((
size
,))
previous_mean_loss
=
0
print
(
"Mean loss of {} elements"
.
format
(
size
))
last_saved_file
=
"<none>"
last_saved_image
=
"<none>"
...
...
@@ -329,7 +360,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
pbar
=
tqdm
.
tqdm
(
enumerate
(
ds
),
total
=
steps
-
ititial_step
)
for
i
,
entries
in
pbar
:
hypernetwork
.
step
=
i
+
ititial_step
if
loss_dict
and
i
%
size
==
0
:
previous_mean_loss
=
sum
(
i
[
-
1
]
for
i
in
loss_dict
.
values
())
/
len
(
loss_dict
)
scheduler
.
apply
(
optimizer
,
hypernetwork
.
step
)
if
scheduler
.
finished
:
break
...
...
@@ -346,7 +379,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
del
c
losses
[
hypernetwork
.
step
%
losses
.
shape
[
0
]]
=
loss
.
item
()
for
entry
in
entries
:
log_statistics
(
loss_dict
,
entry
.
filename
,
loss
.
item
())
optimizer
.
zero_grad
()
weights
[
0
].
grad
=
None
loss
.
backward
()
...
...
@@ -359,10 +394,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
optimizer
.
step
()
mean_loss
=
losses
.
mean
()
if
torch
.
isnan
(
mean_loss
):
if
torch
.
isnan
(
losses
[
hypernetwork
.
step
%
losses
.
shape
[
0
]]):
raise
RuntimeError
(
"Loss diverged."
)
pbar
.
set_description
(
f
"
loss:
{
mean_loss
:.
7
f
}
"
)
pbar
.
set_description
(
f
"
dataset loss:
{
previous_
mean_loss
:.
7
f
}
"
)
if
hypernetwork
.
step
>
0
and
hypernetwork_dir
is
not
None
and
hypernetwork
.
step
%
save_hypernetwork_every
==
0
:
# Before saving, change name to match current checkpoint.
...
...
@@ -371,7 +405,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
hypernetwork
.
save
(
last_saved_file
)
textual_inversion
.
write_loss
(
log_directory
,
"hypernetwork_loss.csv"
,
hypernetwork
.
step
,
len
(
ds
),
{
"loss"
:
f
"
{
mean_loss
:.
7
f
}
"
,
"loss"
:
f
"
{
previous_
mean_loss
:.
7
f
}
"
,
"learn_rate"
:
scheduler
.
learn_rate
})
...
...
@@ -420,14 +454,15 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
shared
.
state
.
textinfo
=
f
"""
<p>
Loss:
{
mean_loss
:.
7
f
}
<br/>
Loss:
{
previous_
mean_loss
:.
7
f
}
<br/>
Step:
{
hypernetwork
.
step
}
<br/>
Last prompt:
{
html
.
escape
(
entries
[
0
].
cond_text
)
}
<br/>
Last saved hypernetwork:
{
html
.
escape
(
last_saved_file
)
}
<br/>
Last saved image:
{
html
.
escape
(
last_saved_image
)
}
<br/>
</p>
"""
report_statistics
(
loss_dict
)
checkpoint
=
sd_models
.
select_checkpoint
()
hypernetwork
.
sd_checkpoint
=
checkpoint
.
hash
...
...
@@ -438,5 +473,3 @@ Last saved image: {html.escape(last_saved_image)}<br/>
hypernetwork
.
save
(
filename
)
return
hypernetwork
,
filename
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