Skip to content
Projects
Groups
Snippets
Help
Loading...
Help
Support
Keyboard shortcuts
?
Submit feedback
Sign in / Register
Toggle navigation
S
Stable Diffusion Webui
Project overview
Project overview
Details
Activity
Releases
Repository
Repository
Files
Commits
Branches
Tags
Contributors
Graph
Compare
Locked Files
Issues
0
Issues
0
List
Boards
Labels
Service Desk
Milestones
Merge Requests
0
Merge Requests
0
CI / CD
CI / CD
Pipelines
Jobs
Schedules
Security & Compliance
Security & Compliance
Dependency List
License Compliance
Packages
Packages
List
Container Registry
Analytics
Analytics
CI / CD
Code Review
Insights
Issues
Repository
Value Stream
Wiki
Wiki
Snippets
Snippets
Members
Members
Collapse sidebar
Close sidebar
Activity
Graph
Create a new issue
Jobs
Commits
Issue Boards
Open sidebar
novelai-storage
Stable Diffusion Webui
Commits
cf2f6f20
Commit
cf2f6f20
authored
Jan 08, 2023
by
Vladimir Repin
Committed by
GitHub
Jan 08, 2023
Browse files
Options
Browse Files
Download
Plain Diff
Merge branch 'AUTOMATIC1111:master' into master
parents
cabd9501
085427de
Changes
2
Hide whitespace changes
Inline
Side-by-side
Showing
2 changed files
with
108 additions
and
65 deletions
+108
-65
modules/sd_hijack.py
modules/sd_hijack.py
+4
-3
modules/textual_inversion/textual_inversion.py
modules/textual_inversion/textual_inversion.py
+104
-62
No files found.
modules/sd_hijack.py
View file @
cf2f6f20
...
...
@@ -83,10 +83,12 @@ class StableDiffusionModelHijack:
clip
=
None
optimization_method
=
None
embedding_db
=
modules
.
textual_inversion
.
textual_inversion
.
EmbeddingDatabase
(
cmd_opts
.
embeddings_dir
)
embedding_db
=
modules
.
textual_inversion
.
textual_inversion
.
EmbeddingDatabase
()
def
hijack
(
self
,
m
):
def
__init__
(
self
):
self
.
embedding_db
.
add_embedding_dir
(
cmd_opts
.
embeddings_dir
)
def
hijack
(
self
,
m
):
if
type
(
m
.
cond_stage_model
)
==
xlmr
.
BertSeriesModelWithTransformation
:
model_embeddings
=
m
.
cond_stage_model
.
roberta
.
embeddings
model_embeddings
.
token_embedding
=
EmbeddingsWithFixes
(
model_embeddings
.
word_embeddings
,
self
)
...
...
@@ -117,7 +119,6 @@ class StableDiffusionModelHijack:
self
.
layers
=
flatten
(
m
)
def
undo_hijack
(
self
,
m
):
if
type
(
m
.
cond_stage_model
)
==
xlmr
.
BertSeriesModelWithTransformation
:
m
.
cond_stage_model
=
m
.
cond_stage_model
.
wrapped
...
...
modules/textual_inversion/textual_inversion.py
View file @
cf2f6f20
...
...
@@ -66,17 +66,41 @@ class Embedding:
return
self
.
cached_checksum
class
DirWithTextualInversionEmbeddings
:
def
__init__
(
self
,
path
):
self
.
path
=
path
self
.
mtime
=
None
def
has_changed
(
self
):
if
not
os
.
path
.
isdir
(
self
.
path
):
return
False
mt
=
os
.
path
.
getmtime
(
self
.
path
)
if
self
.
mtime
is
None
or
mt
>
self
.
mtime
:
return
True
def
update
(
self
):
if
not
os
.
path
.
isdir
(
self
.
path
):
return
self
.
mtime
=
os
.
path
.
getmtime
(
self
.
path
)
class
EmbeddingDatabase
:
def
__init__
(
self
,
embeddings_dir
):
def
__init__
(
self
):
self
.
ids_lookup
=
{}
self
.
word_embeddings
=
{}
self
.
skipped_embeddings
=
{}
self
.
dir_mtime
=
None
self
.
embeddings_dir
=
embeddings_dir
self
.
expected_shape
=
-
1
self
.
embedding_dirs
=
{}
def
register_embedding
(
self
,
embedding
,
model
):
def
add_embedding_dir
(
self
,
path
):
self
.
embedding_dirs
[
path
]
=
DirWithTextualInversionEmbeddings
(
path
)
def
clear_embedding_dirs
(
self
):
self
.
embedding_dirs
.
clear
()
def
register_embedding
(
self
,
embedding
,
model
):
self
.
word_embeddings
[
embedding
.
name
]
=
embedding
ids
=
model
.
cond_stage_model
.
tokenize
([
embedding
.
name
])[
0
]
...
...
@@ -93,65 +117,62 @@ class EmbeddingDatabase:
vec
=
shared
.
sd_model
.
cond_stage_model
.
encode_embedding_init_text
(
","
,
1
)
return
vec
.
shape
[
1
]
def
load_textual_inversion_embeddings
(
self
,
force_reload
=
False
):
mt
=
os
.
path
.
getmtime
(
self
.
embeddings_dir
)
if
not
force_reload
and
self
.
dir_mtime
is
not
None
and
mt
<=
self
.
dir_mtime
:
return
def
load_from_file
(
self
,
path
,
filename
):
name
,
ext
=
os
.
path
.
splitext
(
filename
)
ext
=
ext
.
upper
()
self
.
dir_mtime
=
mt
self
.
ids_lookup
.
clear
()
self
.
word_embeddings
.
clear
()
self
.
skipped_embeddings
.
clear
()
self
.
expected_shape
=
self
.
get_expected_shape
()
def
process_file
(
path
,
filename
):
name
,
ext
=
os
.
path
.
splitext
(
filename
)
ext
=
ext
.
upper
()
if
ext
in
[
'.PNG'
,
'.WEBP'
,
'.JXL'
,
'.AVIF'
]:
embed_image
=
Image
.
open
(
path
)
if
hasattr
(
embed_image
,
'text'
)
and
'sd-ti-embedding'
in
embed_image
.
text
:
data
=
embedding_from_b64
(
embed_image
.
text
[
'sd-ti-embedding'
])
name
=
data
.
get
(
'name'
,
name
)
else
:
data
=
extract_image_data_embed
(
embed_image
)
name
=
data
.
get
(
'name'
,
name
)
elif
ext
in
[
'.BIN'
,
'.PT'
]:
data
=
torch
.
load
(
path
,
map_location
=
"cpu"
)
else
:
if
ext
in
[
'.PNG'
,
'.WEBP'
,
'.JXL'
,
'.AVIF'
]:
_
,
second_ext
=
os
.
path
.
splitext
(
name
)
if
second_ext
.
upper
()
==
'.PREVIEW'
:
return
# textual inversion embeddings
if
'string_to_param'
in
data
:
param_dict
=
data
[
'string_to_param'
]
if
hasattr
(
param_dict
,
'_parameters'
):
param_dict
=
getattr
(
param_dict
,
'_parameters'
)
# fix for torch 1.12.1 loading saved file from torch 1.11
assert
len
(
param_dict
)
==
1
,
'embedding file has multiple terms in it'
emb
=
next
(
iter
(
param_dict
.
items
()))[
1
]
# diffuser concepts
elif
type
(
data
)
==
dict
and
type
(
next
(
iter
(
data
.
values
())))
==
torch
.
Tensor
:
assert
len
(
data
.
keys
())
==
1
,
'embedding file has multiple terms in it'
emb
=
next
(
iter
(
data
.
values
()))
if
len
(
emb
.
shape
)
==
1
:
emb
=
emb
.
unsqueeze
(
0
)
else
:
raise
Exception
(
f
"Couldn't identify {filename} as neither textual inversion embedding nor diffuser concept."
)
vec
=
emb
.
detach
()
.
to
(
devices
.
device
,
dtype
=
torch
.
float32
)
embedding
=
Embedding
(
vec
,
name
)
embedding
.
step
=
data
.
get
(
'step'
,
None
)
embedding
.
sd_checkpoint
=
data
.
get
(
'sd_checkpoint'
,
None
)
embedding
.
sd_checkpoint_name
=
data
.
get
(
'sd_checkpoint_name'
,
None
)
embedding
.
vectors
=
vec
.
shape
[
0
]
embedding
.
shape
=
vec
.
shape
[
-
1
]
if
self
.
expected_shape
==
-
1
or
self
.
expected_shape
==
embedding
.
shape
:
self
.
register_embedding
(
embedding
,
shared
.
sd_model
)
embed_image
=
Image
.
open
(
path
)
if
hasattr
(
embed_image
,
'text'
)
and
'sd-ti-embedding'
in
embed_image
.
text
:
data
=
embedding_from_b64
(
embed_image
.
text
[
'sd-ti-embedding'
])
name
=
data
.
get
(
'name'
,
name
)
else
:
self
.
skipped_embeddings
[
name
]
=
embedding
data
=
extract_image_data_embed
(
embed_image
)
name
=
data
.
get
(
'name'
,
name
)
elif
ext
in
[
'.BIN'
,
'.PT'
]:
data
=
torch
.
load
(
path
,
map_location
=
"cpu"
)
else
:
return
# textual inversion embeddings
if
'string_to_param'
in
data
:
param_dict
=
data
[
'string_to_param'
]
if
hasattr
(
param_dict
,
'_parameters'
):
param_dict
=
getattr
(
param_dict
,
'_parameters'
)
# fix for torch 1.12.1 loading saved file from torch 1.11
assert
len
(
param_dict
)
==
1
,
'embedding file has multiple terms in it'
emb
=
next
(
iter
(
param_dict
.
items
()))[
1
]
# diffuser concepts
elif
type
(
data
)
==
dict
and
type
(
next
(
iter
(
data
.
values
())))
==
torch
.
Tensor
:
assert
len
(
data
.
keys
())
==
1
,
'embedding file has multiple terms in it'
emb
=
next
(
iter
(
data
.
values
()))
if
len
(
emb
.
shape
)
==
1
:
emb
=
emb
.
unsqueeze
(
0
)
else
:
raise
Exception
(
f
"Couldn't identify {filename} as neither textual inversion embedding nor diffuser concept."
)
vec
=
emb
.
detach
()
.
to
(
devices
.
device
,
dtype
=
torch
.
float32
)
embedding
=
Embedding
(
vec
,
name
)
embedding
.
step
=
data
.
get
(
'step'
,
None
)
embedding
.
sd_checkpoint
=
data
.
get
(
'sd_checkpoint'
,
None
)
embedding
.
sd_checkpoint_name
=
data
.
get
(
'sd_checkpoint_name'
,
None
)
embedding
.
vectors
=
vec
.
shape
[
0
]
embedding
.
shape
=
vec
.
shape
[
-
1
]
if
self
.
expected_shape
==
-
1
or
self
.
expected_shape
==
embedding
.
shape
:
self
.
register_embedding
(
embedding
,
shared
.
sd_model
)
else
:
self
.
skipped_embeddings
[
name
]
=
embedding
for
root
,
dirs
,
fns
in
os
.
walk
(
self
.
embeddings_dir
):
def
load_from_dir
(
self
,
embdir
):
if
not
os
.
path
.
isdir
(
embdir
.
path
):
return
for
root
,
dirs
,
fns
in
os
.
walk
(
embdir
.
path
):
for
fn
in
fns
:
try
:
fullfn
=
os
.
path
.
join
(
root
,
fn
)
...
...
@@ -159,12 +180,32 @@ class EmbeddingDatabase:
if
os
.
stat
(
fullfn
)
.
st_size
==
0
:
continue
process
_file
(
fullfn
,
fn
)
self
.
load_from
_file
(
fullfn
,
fn
)
except
Exception
:
print
(
f
"Error loading embedding {fn}:"
,
file
=
sys
.
stderr
)
print
(
traceback
.
format_exc
(),
file
=
sys
.
stderr
)
continue
def
load_textual_inversion_embeddings
(
self
,
force_reload
=
False
):
if
not
force_reload
:
need_reload
=
False
for
path
,
embdir
in
self
.
embedding_dirs
.
items
():
if
embdir
.
has_changed
():
need_reload
=
True
break
if
not
need_reload
:
return
self
.
ids_lookup
.
clear
()
self
.
word_embeddings
.
clear
()
self
.
skipped_embeddings
.
clear
()
self
.
expected_shape
=
self
.
get_expected_shape
()
for
path
,
embdir
in
self
.
embedding_dirs
.
items
():
self
.
load_from_dir
(
embdir
)
embdir
.
update
()
print
(
f
"Textual inversion embeddings loaded({len(self.word_embeddings)}): {', '.join(self.word_embeddings.keys())}"
)
if
len
(
self
.
skipped_embeddings
)
>
0
:
print
(
f
"Textual inversion embeddings skipped({len(self.skipped_embeddings)}): {', '.join(self.skipped_embeddings.keys())}"
)
...
...
@@ -247,14 +288,15 @@ def validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, dat
assert
os
.
path
.
isfile
(
template_file
),
"Prompt template file doesn't exist"
assert
steps
,
"Max steps is empty or 0"
assert
isinstance
(
steps
,
int
),
"Max steps must be integer"
assert
steps
>
0
,
"Max steps must be positive"
assert
steps
>
0
,
"Max steps must be positive"
assert
isinstance
(
save_model_every
,
int
),
"Save {name} must be integer"
assert
save_model_every
>=
0
,
"Save {name} must be positive or 0"
assert
save_model_every
>=
0
,
"Save {name} must be positive or 0"
assert
isinstance
(
create_image_every
,
int
),
"Create image must be integer"
assert
create_image_every
>=
0
,
"Create image must be positive or 0"
assert
create_image_every
>=
0
,
"Create image must be positive or 0"
if
save_model_every
or
create_image_every
:
assert
log_directory
,
"Log directory is empty"
def
train_embedding
(
embedding_name
,
learn_rate
,
batch_size
,
gradient_step
,
data_root
,
log_directory
,
training_width
,
training_height
,
steps
,
clip_grad_mode
,
clip_grad_value
,
shuffle_tags
,
tag_drop_out
,
latent_sampling_method
,
create_image_every
,
save_embedding_every
,
template_file
,
save_image_with_stored_embedding
,
preview_from_txt2img
,
preview_prompt
,
preview_negative_prompt
,
preview_steps
,
preview_sampler_index
,
preview_cfg_scale
,
preview_seed
,
preview_width
,
preview_height
):
save_embedding_every
=
save_embedding_every
or
0
create_image_every
=
create_image_every
or
0
...
...
Write
Preview
Markdown
is supported
0%
Try again
or
attach a new file
Attach a file
Cancel
You are about to add
0
people
to the discussion. Proceed with caution.
Finish editing this message first!
Cancel
Please
register
or
sign in
to comment