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Stable Diffusion Webui
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novelai-storage
Stable Diffusion Webui
Commits
9c9f048b
Commit
9c9f048b
authored
Aug 29, 2022
by
AUTOMATIC
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support for generating images on video cards with 4GB
parent
7a7a3a6b
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86 additions
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4 deletions
+86
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webui.py
webui.py
+86
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webui.py
View file @
9c9f048b
...
...
@@ -2,6 +2,8 @@ import argparse
import
os
import
sys
from
collections
import
namedtuple
from
contextlib
import
nullcontext
import
torch
import
torch.nn
as
nn
import
numpy
as
np
...
...
@@ -51,6 +53,7 @@ parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not
parser
.
add_argument
(
"--max-batch-count"
,
type
=
int
,
default
=
16
,
help
=
"maximum batch count value for the UI"
)
parser
.
add_argument
(
"--embeddings-dir"
,
type
=
str
,
default
=
'embeddings'
,
help
=
"embeddings dirtectory for textual inversion (default: embeddings)"
)
parser
.
add_argument
(
"--allow-code"
,
action
=
'store_true'
,
help
=
"allow custom script execution from webui"
)
parser
.
add_argument
(
"--lowvram"
,
action
=
'store_true'
,
help
=
"enamble optimizations for low vram"
)
cmd_opts
=
parser
.
parse_args
()
...
...
@@ -185,11 +188,80 @@ def load_model_from_config(config, ckpt, verbose=False):
print
(
"unexpected keys:"
)
print
(
u
)
model
.
cuda
()
model
.
eval
()
return
model
module_in_gpu
=
None
def
setup_for_low_vram
(
sd_model
):
parents
=
{}
def
send_me_to_gpu
(
module
,
_
):
"""send this module to GPU; send whatever tracked module was previous in GPU to CPU;
we add this as forward_pre_hook to a lot of modules and this way all but one of them will
be in CPU
"""
global
module_in_gpu
module
=
parents
.
get
(
module
,
module
)
if
module_in_gpu
==
module
:
return
if
module_in_gpu
is
not
None
:
print
(
'removing from gpu:'
,
type
(
module_in_gpu
))
module_in_gpu
.
to
(
cpu
)
print
(
'adding to gpu:'
,
type
(
module
))
module
.
to
(
gpu
)
print
(
'added to gpu:'
,
type
(
module
))
module_in_gpu
=
module
# see below for register_forward_pre_hook;
# first_stage_model does not use forward(), it uses encode/decode, so register_forward_pre_hook is
# useless here, and we just replace those methods
def
first_stage_model_encode_wrap
(
self
,
encoder
,
x
):
send_me_to_gpu
(
self
,
None
)
return
encoder
(
x
)
def
first_stage_model_decode_wrap
(
self
,
decoder
,
z
):
send_me_to_gpu
(
self
,
None
)
return
decoder
(
z
)
# remove three big modules, cond, first_stage, and unet from the model and then
# send the model to GPU. Then put modules back. the modules will be in CPU.
stored
=
sd_model
.
cond_stage_model
.
transformer
,
sd_model
.
first_stage_model
,
sd_model
.
model
sd_model
.
cond_stage_model
.
transformer
,
sd_model
.
first_stage_model
,
sd_model
.
model
=
None
,
None
,
None
sd_model
.
to
(
device
)
sd_model
.
cond_stage_model
.
transformer
,
sd_model
.
first_stage_model
,
sd_model
.
model
=
stored
# register hooks for those the first two models
sd_model
.
cond_stage_model
.
transformer
.
register_forward_pre_hook
(
send_me_to_gpu
)
sd_model
.
first_stage_model
.
register_forward_pre_hook
(
send_me_to_gpu
)
sd_model
.
first_stage_model
.
encode
=
lambda
x
,
en
=
sd_model
.
first_stage_model
.
encode
:
first_stage_model_encode_wrap
(
sd_model
.
first_stage_model
,
en
,
x
)
sd_model
.
first_stage_model
.
decode
=
lambda
z
,
de
=
sd_model
.
first_stage_model
.
decode
:
first_stage_model_decode_wrap
(
sd_model
.
first_stage_model
,
de
,
z
)
parents
[
sd_model
.
cond_stage_model
.
transformer
]
=
sd_model
.
cond_stage_model
# the third remaining model is still too big for 4GB, so we also do the same for its submodules
# so that only one of them is in GPU at a time
diff_model
=
sd_model
.
model
.
diffusion_model
stored
=
diff_model
.
input_blocks
,
diff_model
.
middle_block
,
diff_model
.
output_blocks
,
diff_model
.
time_embed
diff_model
.
input_blocks
,
diff_model
.
middle_block
,
diff_model
.
output_blocks
,
diff_model
.
time_embed
=
None
,
None
,
None
,
None
sd_model
.
model
.
to
(
device
)
diff_model
.
input_blocks
,
diff_model
.
middle_block
,
diff_model
.
output_blocks
,
diff_model
.
time_embed
=
stored
# install hooks for bits of third model
diff_model
.
time_embed
.
register_forward_pre_hook
(
send_me_to_gpu
)
for
block
in
diff_model
.
input_blocks
:
block
.
register_forward_pre_hook
(
send_me_to_gpu
)
diff_model
.
middle_block
.
register_forward_pre_hook
(
send_me_to_gpu
)
for
block
in
diff_model
.
output_blocks
:
block
.
register_forward_pre_hook
(
send_me_to_gpu
)
def
create_random_tensors
(
shape
,
seeds
):
xs
=
[]
for
seed
in
seeds
:
...
...
@@ -838,7 +910,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
model_hijack
.
load_textual_inversion_embeddings
(
cmd_opts
.
embeddings_dir
,
model
)
output_images
=
[]
with
torch
.
no_grad
(),
autocast
(
"cuda"
),
model
.
ema_scope
():
ema_scope
=
(
nullcontext
if
cmd_opts
.
lowvram
else
model
.
ema_scope
)
with
torch
.
no_grad
(),
autocast
(
"cuda"
),
ema_scope
():
p
.
init
()
for
n
in
range
(
p
.
n_iter
):
...
...
@@ -1327,8 +1400,17 @@ interfaces = [
sd_config
=
OmegaConf
.
load
(
cmd_opts
.
config
)
sd_model
=
load_model_from_config
(
sd_config
,
cmd_opts
.
ckpt
)
device
=
torch
.
device
(
"cuda"
)
if
torch
.
cuda
.
is_available
()
else
torch
.
device
(
"cpu"
)
sd_model
=
(
sd_model
if
cmd_opts
.
no_half
else
sd_model
.
half
())
.
to
(
device
)
cpu
=
torch
.
device
(
"cpu"
)
gpu
=
torch
.
device
(
"cuda"
)
device
=
gpu
if
torch
.
cuda
.
is_available
()
else
cpu
sd_model
=
(
sd_model
if
cmd_opts
.
no_half
else
sd_model
.
half
())
if
not
cmd_opts
.
lowvram
:
sd_model
=
sd_model
.
to
(
device
)
else
:
setup_for_low_vram
(
sd_model
)
model_hijack
=
StableDiffusionModelHijack
()
model_hijack
.
hijack
(
sd_model
)
...
...
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