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Administrator
stable-diffusion-webui
Commits
61788c05
Unverified
Commit
61788c05
authored
Oct 11, 2022
by
DepFA
Committed by
GitHub
Oct 11, 2022
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shift embedding logic out of textual_inversion
parent
e5fbf5c7
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1 changed file
with
6 additions
and
119 deletions
+6
-119
textual_inversion.py
modules/textual_inversion/textual_inversion.py
+6
-119
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modules/textual_inversion/textual_inversion.py
View file @
61788c05
...
@@ -7,124 +7,11 @@ import tqdm
...
@@ -7,124 +7,11 @@ import tqdm
import
html
import
html
import
datetime
import
datetime
from
PIL
import
Image
,
PngImagePlugin
,
ImageDraw
from
PIL
import
Image
,
PngImagePlugin
from
..images
import
captionImageOverlay
import
numpy
as
np
import
base64
import
json
import
zlib
from
modules
import
shared
,
devices
,
sd_hijack
,
processing
,
sd_models
from
modules
import
shared
,
devices
,
sd_hijack
,
processing
,
sd_models
import
modules.textual_inversion.dataset
import
modules.textual_inversion.dataset
class
EmbeddingEncoder
(
json
.
JSONEncoder
):
def
default
(
self
,
obj
):
if
isinstance
(
obj
,
torch
.
Tensor
):
return
{
'TORCHTENSOR'
:
obj
.
cpu
()
.
detach
()
.
numpy
()
.
tolist
()}
return
json
.
JSONEncoder
.
default
(
self
,
obj
)
class
EmbeddingDecoder
(
json
.
JSONDecoder
):
def
__init__
(
self
,
*
args
,
**
kwargs
):
json
.
JSONDecoder
.
__init__
(
self
,
object_hook
=
self
.
object_hook
,
*
args
,
**
kwargs
)
def
object_hook
(
self
,
d
):
if
'TORCHTENSOR'
in
d
:
return
torch
.
from_numpy
(
np
.
array
(
d
[
'TORCHTENSOR'
]))
return
d
def
embeddingToB64
(
data
):
d
=
json
.
dumps
(
data
,
cls
=
EmbeddingEncoder
)
return
base64
.
b64encode
(
d
.
encode
())
def
embeddingFromB64
(
data
):
d
=
base64
.
b64decode
(
data
)
return
json
.
loads
(
d
,
cls
=
EmbeddingDecoder
)
def
lcg
(
m
=
2
**
32
,
a
=
1664525
,
c
=
1013904223
,
seed
=
0
):
while
True
:
seed
=
(
a
*
seed
+
c
)
%
m
yield
seed
def
xorBlock
(
block
):
g
=
lcg
()
randblock
=
np
.
array
([
next
(
g
)
for
_
in
range
(
np
.
product
(
block
.
shape
))])
.
astype
(
np
.
uint8
)
.
reshape
(
block
.
shape
)
return
np
.
bitwise_xor
(
block
.
astype
(
np
.
uint8
),
randblock
&
0x0F
)
def
styleBlock
(
block
,
sequence
):
im
=
Image
.
new
(
'RGB'
,(
block
.
shape
[
1
],
block
.
shape
[
0
]))
draw
=
ImageDraw
.
Draw
(
im
)
i
=
0
for
x
in
range
(
-
6
,
im
.
size
[
0
],
8
):
for
yi
,
y
in
enumerate
(
range
(
-
6
,
im
.
size
[
1
],
8
)):
offset
=
0
if
yi
%
2
==
0
:
offset
=
4
shade
=
sequence
[
i
%
len
(
sequence
)]
i
+=
1
draw
.
ellipse
((
x
+
offset
,
y
,
x
+
6
+
offset
,
y
+
6
),
fill
=
(
shade
,
shade
,
shade
)
)
fg
=
np
.
array
(
im
)
.
astype
(
np
.
uint8
)
&
0xF0
return
block
^
fg
def
insertImageDataEmbed
(
image
,
data
):
d
=
3
data_compressed
=
zlib
.
compress
(
json
.
dumps
(
data
,
cls
=
EmbeddingEncoder
)
.
encode
(),
level
=
9
)
dnp
=
np
.
frombuffer
(
data_compressed
,
np
.
uint8
)
.
copy
()
dnphigh
=
dnp
>>
4
dnplow
=
dnp
&
0x0F
h
=
image
.
size
[
1
]
next_size
=
dnplow
.
shape
[
0
]
+
(
h
-
(
dnplow
.
shape
[
0
]
%
h
))
next_size
=
next_size
+
((
h
*
d
)
-
(
next_size
%
(
h
*
d
)))
dnplow
.
resize
(
next_size
)
dnplow
=
dnplow
.
reshape
((
h
,
-
1
,
d
))
dnphigh
.
resize
(
next_size
)
dnphigh
=
dnphigh
.
reshape
((
h
,
-
1
,
d
))
edgeStyleWeights
=
list
(
data
[
'string_to_param'
]
.
values
())[
0
]
.
cpu
()
.
detach
()
.
numpy
()
.
tolist
()[
0
][:
1024
]
edgeStyleWeights
=
(
np
.
abs
(
edgeStyleWeights
)
/
np
.
max
(
np
.
abs
(
edgeStyleWeights
))
*
255
)
.
astype
(
np
.
uint8
)
dnplow
=
styleBlock
(
dnplow
,
sequence
=
edgeStyleWeights
)
dnplow
=
xorBlock
(
dnplow
)
dnphigh
=
styleBlock
(
dnphigh
,
sequence
=
edgeStyleWeights
[::
-
1
])
dnphigh
=
xorBlock
(
dnphigh
)
imlow
=
Image
.
fromarray
(
dnplow
,
mode
=
'RGB'
)
imhigh
=
Image
.
fromarray
(
dnphigh
,
mode
=
'RGB'
)
background
=
Image
.
new
(
'RGB'
,(
image
.
size
[
0
]
+
imlow
.
size
[
0
]
+
imhigh
.
size
[
0
]
+
2
,
image
.
size
[
1
]),(
0
,
0
,
0
))
background
.
paste
(
imlow
,(
0
,
0
))
background
.
paste
(
image
,(
imlow
.
size
[
0
]
+
1
,
0
))
background
.
paste
(
imhigh
,(
imlow
.
size
[
0
]
+
1
+
image
.
size
[
0
]
+
1
,
0
))
return
background
def
crop_black
(
img
,
tol
=
0
):
mask
=
(
img
>
tol
)
.
all
(
2
)
mask0
,
mask1
=
mask
.
any
(
0
),
mask
.
any
(
1
)
col_start
,
col_end
=
mask0
.
argmax
(),
mask
.
shape
[
1
]
-
mask0
[::
-
1
]
.
argmax
()
row_start
,
row_end
=
mask1
.
argmax
(),
mask
.
shape
[
0
]
-
mask1
[::
-
1
]
.
argmax
()
return
img
[
row_start
:
row_end
,
col_start
:
col_end
]
def
extractImageDataEmbed
(
image
):
d
=
3
outarr
=
crop_black
(
np
.
array
(
image
.
convert
(
'RGB'
)
.
getdata
())
.
reshape
(
image
.
size
[
1
],
image
.
size
[
0
],
d
)
.
astype
(
np
.
uint8
)
)
&
0x0F
blackCols
=
np
.
where
(
np
.
sum
(
outarr
,
axis
=
(
0
,
2
))
==
0
)
if
blackCols
[
0
]
.
shape
[
0
]
<
2
:
print
(
'No Image data blocks found.'
)
return
None
dataBlocklower
=
outarr
[:,:
blackCols
[
0
]
.
min
(),:]
.
astype
(
np
.
uint8
)
dataBlockupper
=
outarr
[:,
blackCols
[
0
]
.
max
()
+
1
:,:]
.
astype
(
np
.
uint8
)
dataBlocklower
=
xorBlock
(
dataBlocklower
)
dataBlockupper
=
xorBlock
(
dataBlockupper
)
dataBlock
=
(
dataBlockupper
<<
4
)
|
(
dataBlocklower
)
dataBlock
=
dataBlock
.
flatten
()
.
tobytes
()
data
=
zlib
.
decompress
(
dataBlock
)
return
json
.
loads
(
data
,
cls
=
EmbeddingDecoder
)
class
Embedding
:
class
Embedding
:
def
__init__
(
self
,
vec
,
name
,
step
=
None
):
def
__init__
(
self
,
vec
,
name
,
step
=
None
):
...
@@ -199,10 +86,10 @@ class EmbeddingDatabase:
...
@@ -199,10 +86,10 @@ class EmbeddingDatabase:
if
filename
.
upper
()
.
endswith
(
'.PNG'
):
if
filename
.
upper
()
.
endswith
(
'.PNG'
):
embed_image
=
Image
.
open
(
path
)
embed_image
=
Image
.
open
(
path
)
if
'sd-ti-embedding'
in
embed_image
.
text
:
if
'sd-ti-embedding'
in
embed_image
.
text
:
data
=
embedding
FromB
64
(
embed_image
.
text
[
'sd-ti-embedding'
])
data
=
embedding
_from_b
64
(
embed_image
.
text
[
'sd-ti-embedding'
])
name
=
data
.
get
(
'name'
,
name
)
name
=
data
.
get
(
'name'
,
name
)
else
:
else
:
data
=
extract
ImageDataE
mbed
(
embed_image
)
data
=
extract
_image_data_e
mbed
(
embed_image
)
name
=
data
.
get
(
'name'
,
name
)
name
=
data
.
get
(
'name'
,
name
)
else
:
else
:
data
=
torch
.
load
(
path
,
map_location
=
"cpu"
)
data
=
torch
.
load
(
path
,
map_location
=
"cpu"
)
...
@@ -393,7 +280,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
...
@@ -393,7 +280,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
info
=
PngImagePlugin
.
PngInfo
()
info
=
PngImagePlugin
.
PngInfo
()
data
=
torch
.
load
(
last_saved_file
)
data
=
torch
.
load
(
last_saved_file
)
info
.
add_text
(
"sd-ti-embedding"
,
embedding
ToB
64
(
data
))
info
.
add_text
(
"sd-ti-embedding"
,
embedding
_to_b
64
(
data
))
title
=
"<{}>"
.
format
(
data
.
get
(
'name'
,
'???'
))
title
=
"<{}>"
.
format
(
data
.
get
(
'name'
,
'???'
))
checkpoint
=
sd_models
.
select_checkpoint
()
checkpoint
=
sd_models
.
select_checkpoint
()
...
@@ -401,8 +288,8 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
...
@@ -401,8 +288,8 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
footer_mid
=
'[{}]'
.
format
(
checkpoint
.
hash
)
footer_mid
=
'[{}]'
.
format
(
checkpoint
.
hash
)
footer_right
=
'{}'
.
format
(
embedding
.
step
)
footer_right
=
'{}'
.
format
(
embedding
.
step
)
captioned_image
=
caption
ImageO
verlay
(
image
,
title
,
footer_left
,
footer_mid
,
footer_right
)
captioned_image
=
caption
_image_o
verlay
(
image
,
title
,
footer_left
,
footer_mid
,
footer_right
)
captioned_image
=
insert
ImageDataE
mbed
(
captioned_image
,
data
)
captioned_image
=
insert
_image_data_e
mbed
(
captioned_image
,
data
)
captioned_image
.
save
(
last_saved_image_chunks
,
"PNG"
,
pnginfo
=
info
)
captioned_image
.
save
(
last_saved_image_chunks
,
"PNG"
,
pnginfo
=
info
)
...
...
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