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novelai-storage
Stable Diffusion Webui
Commits
11e03b9a
Commit
11e03b9a
authored
Sep 12, 2022
by
AUTOMATIC1111
Committed by
GitHub
Sep 12, 2022
Browse files
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Merge pull request #294 from EliasOenal/master
Fixes for mps/Metal: use of seeds, img2img, CodeFormer
parents
a655e90f
b7f95869
Changes
3
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3 changed files
with
35 additions
and
11 deletions
+35
-11
modules/codeformer_model.py
modules/codeformer_model.py
+5
-3
modules/processing.py
modules/processing.py
+28
-8
modules/shared.py
modules/shared.py
+2
-0
No files found.
modules/codeformer_model.py
View file @
11e03b9a
...
...
@@ -47,6 +47,8 @@ def setup_codeformer():
def
__init__
(
self
):
self
.
net
=
None
self
.
face_helper
=
None
if
shared
.
device
.
type
==
'mps'
:
# CodeFormer currently does not support mps backend
shared
.
device_codeformer
=
torch
.
device
(
'cpu'
)
def
create_models
(
self
):
...
...
@@ -54,13 +56,13 @@ def setup_codeformer():
self
.
net
.
to
(
shared
.
device
)
return
self
.
net
,
self
.
face_helper
net
=
net_class
(
dim_embd
=
512
,
codebook_size
=
1024
,
n_head
=
8
,
n_layers
=
9
,
connect_list
=
[
'32'
,
'64'
,
'128'
,
'256'
])
.
to
(
shared
.
device
)
net
=
net_class
(
dim_embd
=
512
,
codebook_size
=
1024
,
n_head
=
8
,
n_layers
=
9
,
connect_list
=
[
'32'
,
'64'
,
'128'
,
'256'
])
.
to
(
shared
.
device
_codeformer
)
ckpt_path
=
load_file_from_url
(
url
=
pretrain_model_url
,
model_dir
=
os
.
path
.
join
(
path
,
'weights/CodeFormer'
),
progress
=
True
)
checkpoint
=
torch
.
load
(
ckpt_path
)[
'params_ema'
]
net
.
load_state_dict
(
checkpoint
)
net
.
eval
()
face_helper
=
FaceRestoreHelper
(
1
,
face_size
=
512
,
crop_ratio
=
(
1
,
1
),
det_model
=
'retinaface_resnet50'
,
save_ext
=
'png'
,
use_parse
=
True
,
device
=
shared
.
device
)
face_helper
=
FaceRestoreHelper
(
1
,
face_size
=
512
,
crop_ratio
=
(
1
,
1
),
det_model
=
'retinaface_resnet50'
,
save_ext
=
'png'
,
use_parse
=
True
,
device
=
shared
.
device
_codeformer
)
self
.
net
=
net
self
.
face_helper
=
face_helper
...
...
@@ -82,7 +84,7 @@ def setup_codeformer():
for
idx
,
cropped_face
in
enumerate
(
self
.
face_helper
.
cropped_faces
):
cropped_face_t
=
img2tensor
(
cropped_face
/
255.
,
bgr2rgb
=
True
,
float32
=
True
)
normalize
(
cropped_face_t
,
(
0.5
,
0.5
,
0.5
),
(
0.5
,
0.5
,
0.5
),
inplace
=
True
)
cropped_face_t
=
cropped_face_t
.
unsqueeze
(
0
)
.
to
(
shared
.
device
)
cropped_face_t
=
cropped_face_t
.
unsqueeze
(
0
)
.
to
(
shared
.
device
_codeformer
)
try
:
with
torch
.
no_grad
():
...
...
modules/processing.py
View file @
11e03b9a
...
...
@@ -103,17 +103,32 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
for
i
,
seed
in
enumerate
(
seeds
):
noise_shape
=
shape
if
seed_resize_from_h
<=
0
or
seed_resize_from_w
<=
0
else
(
shape
[
0
],
seed_resize_from_h
//
8
,
seed_resize_from_w
//
8
)
# Pytorch currently doesn't handle seeting randomness correctly when the metal backend is used.
generator
=
torch
if
shared
.
device
.
type
==
'mps'
:
shared
.
device_seed_type
=
'cpu'
generator
=
torch
.
Generator
(
device
=
shared
.
device_seed_type
)
subnoise
=
None
if
subseeds
is
not
None
:
subseed
=
0
if
i
>=
len
(
subseeds
)
else
subseeds
[
i
]
torch
.
manual_seed
(
subseed
)
generator
.
manual_seed
(
subseed
)
if
shared
.
device
.
type
!=
shared
.
device_seed_type
:
subnoise
=
torch
.
randn
(
noise_shape
,
generator
=
generator
,
device
=
shared
.
device_seed_type
)
.
to
(
shared
.
device
)
else
:
subnoise
=
torch
.
randn
(
noise_shape
,
device
=
shared
.
device
)
# randn results depend on device; gpu and cpu get different results for same seed;
# the way I see it, it's better to do this on CPU, so that everyone gets same result;
# but the original script had it like this, so I do not dare change it for now because
# it will break everyone's seeds.
torch
.
manual_seed
(
seed
)
# When using the mps backend falling back to the cpu device is needed, since mps currently
# does not implement seeding properly.
generator
.
manual_seed
(
seed
)
if
shared
.
device
.
type
!=
shared
.
device_seed_type
:
noise
=
torch
.
randn
(
noise_shape
,
generator
=
generator
,
device
=
shared
.
device_seed_type
)
.
to
(
shared
.
device
)
else
:
noise
=
torch
.
randn
(
noise_shape
,
device
=
shared
.
device
)
if
subnoise
is
not
None
:
...
...
@@ -124,8 +139,10 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
#noise = torch.nn.functional.interpolate(noise.unsqueeze(1), size=shape[1:], mode="bilinear").squeeze()
# noise_shape = (64, 80)
# shape = (64, 72)
torch
.
manual_seed
(
seed
)
generator
.
manual_seed
(
seed
)
if
shared
.
device
.
type
!=
shared
.
device_seed_type
:
x
=
torch
.
randn
(
shape
,
generator
=
generator
,
device
=
shared
.
device_seed_type
)
.
to
(
shared
.
device
)
else
:
x
=
torch
.
randn
(
shape
,
device
=
shared
.
device
)
dx
=
(
shape
[
2
]
-
noise_shape
[
2
])
//
2
# -4
dy
=
(
shape
[
1
]
-
noise_shape
[
1
])
//
2
...
...
@@ -465,7 +482,10 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
if
self
.
image_mask
is
not
None
:
init_mask
=
latent_mask
latmask
=
init_mask
.
convert
(
'RGB'
)
.
resize
((
self
.
init_latent
.
shape
[
3
],
self
.
init_latent
.
shape
[
2
]))
latmask
=
np
.
moveaxis
(
np
.
array
(
latmask
,
dtype
=
np
.
float64
),
2
,
0
)
/
255
precision
=
np
.
float64
if
shared
.
device
.
type
==
'mps'
:
# mps backend does not support float64
precision
=
np
.
float32
latmask
=
np
.
moveaxis
(
np
.
array
(
latmask
,
dtype
=
precision
),
2
,
0
)
/
255
latmask
=
latmask
[
0
]
latmask
=
np
.
around
(
latmask
)
latmask
=
np
.
tile
(
latmask
[
None
],
(
4
,
1
,
1
))
...
...
modules/shared.py
View file @
11e03b9a
...
...
@@ -49,6 +49,8 @@ parser.add_argument("--opt-channelslast", action='store_true', help="change memo
cmd_opts
=
parser
.
parse_args
()
device
=
get_optimal_device
()
device_codeformer
=
device
device_seed_type
=
device
batch_cond_uncond
=
cmd_opts
.
always_batch_cond_uncond
or
not
(
cmd_opts
.
lowvram
or
cmd_opts
.
medvram
)
parallel_processing_allowed
=
not
cmd_opts
.
lowvram
and
not
cmd_opts
.
medvram
...
...
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