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Administrator
stable-diffusion-webui
Commits
4d5f1691
Commit
4d5f1691
authored
Nov 29, 2022
by
brkirch
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Use devices.autocast instead of torch.autocast
parent
21effd62
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Showing
5 changed files
with
6 additions
and
11 deletions
+6
-11
hypernetwork.py
modules/hypernetworks/hypernetwork.py
+1
-1
interrogate.py
modules/interrogate.py
+1
-2
swinir_model.py
modules/swinir_model.py
+1
-5
dataset.py
modules/textual_inversion/dataset.py
+2
-2
textual_inversion.py
modules/textual_inversion/textual_inversion.py
+1
-1
No files found.
modules/hypernetworks/hypernetwork.py
View file @
4d5f1691
...
...
@@ -495,7 +495,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step,
if
shared
.
state
.
interrupted
:
break
with
torch
.
autocast
(
"cuda"
):
with
devices
.
autocast
(
):
x
=
batch
.
latent_sample
.
to
(
devices
.
device
,
non_blocking
=
pin_memory
)
if
tag_drop_out
!=
0
or
shuffle_tags
:
shared
.
sd_model
.
cond_stage_model
.
to
(
devices
.
device
)
...
...
modules/interrogate.py
View file @
4d5f1691
...
...
@@ -148,8 +148,7 @@ class InterrogateModels:
clip_image
=
self
.
clip_preprocess
(
pil_image
)
.
unsqueeze
(
0
)
.
type
(
self
.
dtype
)
.
to
(
devices
.
device_interrogate
)
precision_scope
=
torch
.
autocast
if
shared
.
cmd_opts
.
precision
==
"autocast"
else
contextlib
.
nullcontext
with
torch
.
no_grad
(),
precision_scope
(
"cuda"
):
with
torch
.
no_grad
(),
devices
.
autocast
():
image_features
=
self
.
clip_model
.
encode_image
(
clip_image
)
.
type
(
self
.
dtype
)
image_features
/=
image_features
.
norm
(
dim
=-
1
,
keepdim
=
True
)
...
...
modules/swinir_model.py
View file @
4d5f1691
...
...
@@ -13,10 +13,6 @@ from modules.swinir_model_arch import SwinIR as net
from
modules.swinir_model_arch_v2
import
Swin2SR
as
net2
from
modules.upscaler
import
Upscaler
,
UpscalerData
precision_scope
=
(
torch
.
autocast
if
cmd_opts
.
precision
==
"autocast"
else
contextlib
.
nullcontext
)
class
UpscalerSwinIR
(
Upscaler
):
def
__init__
(
self
,
dirname
):
...
...
@@ -112,7 +108,7 @@ def upscale(
img
=
np
.
moveaxis
(
img
,
2
,
0
)
/
255
img
=
torch
.
from_numpy
(
img
)
.
float
()
img
=
img
.
unsqueeze
(
0
)
.
to
(
devices
.
device_swinir
)
with
torch
.
no_grad
(),
precision_scope
(
"cuda"
):
with
torch
.
no_grad
(),
devices
.
autocast
(
):
_
,
_
,
h_old
,
w_old
=
img
.
size
()
h_pad
=
(
h_old
//
window_size
+
1
)
*
window_size
-
h_old
w_pad
=
(
w_old
//
window_size
+
1
)
*
window_size
-
w_old
...
...
modules/textual_inversion/dataset.py
View file @
4d5f1691
...
...
@@ -82,7 +82,7 @@ class PersonalizedBase(Dataset):
torchdata
=
torch
.
from_numpy
(
npimage
)
.
permute
(
2
,
0
,
1
)
.
to
(
device
=
device
,
dtype
=
torch
.
float32
)
latent_sample
=
None
with
torch
.
autocast
(
"cuda"
):
with
devices
.
autocast
(
):
latent_dist
=
model
.
encode_first_stage
(
torchdata
.
unsqueeze
(
dim
=
0
))
if
latent_sampling_method
==
"once"
or
(
latent_sampling_method
==
"deterministic"
and
not
isinstance
(
latent_dist
,
DiagonalGaussianDistribution
)):
...
...
@@ -101,7 +101,7 @@ class PersonalizedBase(Dataset):
entry
.
cond_text
=
self
.
create_text
(
filename_text
)
if
include_cond
and
not
(
self
.
tag_drop_out
!=
0
or
self
.
shuffle_tags
):
with
torch
.
autocast
(
"cuda"
):
with
devices
.
autocast
(
):
entry
.
cond
=
cond_model
([
entry
.
cond_text
])
.
to
(
devices
.
cpu
)
.
squeeze
(
0
)
self
.
dataset
.
append
(
entry
)
...
...
modules/textual_inversion/textual_inversion.py
View file @
4d5f1691
...
...
@@ -316,7 +316,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
if
shared
.
state
.
interrupted
:
break
with
torch
.
autocast
(
"cuda"
):
with
devices
.
autocast
(
):
# c = stack_conds(batch.cond).to(devices.device)
# mask = torch.tensor(batch.emb_index).to(devices.device, non_blocking=pin_memory)
# print(mask)
...
...
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