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stable-diffusion-webui
Commits
de1dc0d2
Commit
de1dc0d2
authored
Oct 29, 2022
by
Martin Cairns
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Add adjust_steps_if_invalid to find next valid step for ddim uniform sampler
parent
35c45df2
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1 changed file
with
15 additions
and
13 deletions
+15
-13
sd_samplers.py
modules/sd_samplers.py
+15
-13
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modules/sd_samplers.py
View file @
de1dc0d2
from
collections
import
namedtuple
from
collections
import
namedtuple
import
numpy
as
np
import
numpy
as
np
from
math
import
floor
import
torch
import
torch
import
tqdm
import
tqdm
from
PIL
import
Image
from
PIL
import
Image
...
@@ -205,17 +206,22 @@ class VanillaStableDiffusionSampler:
...
@@ -205,17 +206,22 @@ class VanillaStableDiffusionSampler:
self
.
mask
=
p
.
mask
if
hasattr
(
p
,
'mask'
)
else
None
self
.
mask
=
p
.
mask
if
hasattr
(
p
,
'mask'
)
else
None
self
.
nmask
=
p
.
nmask
if
hasattr
(
p
,
'nmask'
)
else
None
self
.
nmask
=
p
.
nmask
if
hasattr
(
p
,
'nmask'
)
else
None
def
adjust_steps_if_invalid
(
self
,
p
,
num_steps
):
if
self
.
config
.
name
==
'DDIM'
and
p
.
ddim_discretize
==
'uniform'
:
valid_step
=
999
/
(
1000
//
num_steps
)
if
valid_step
==
floor
(
valid_step
):
return
int
(
valid_step
)
+
1
return
num_steps
def
sample_img2img
(
self
,
p
,
x
,
noise
,
conditioning
,
unconditional_conditioning
,
steps
=
None
,
image_conditioning
=
None
):
def
sample_img2img
(
self
,
p
,
x
,
noise
,
conditioning
,
unconditional_conditioning
,
steps
=
None
,
image_conditioning
=
None
):
steps
,
t_enc
=
setup_img2img_steps
(
p
,
steps
)
steps
,
t_enc
=
setup_img2img_steps
(
p
,
steps
)
steps
=
self
.
adjust_steps_if_invalid
(
p
,
steps
)
self
.
initialize
(
p
)
self
.
initialize
(
p
)
# existing code fails with certain step counts, like 9
self
.
sampler
.
make_schedule
(
ddim_num_steps
=
steps
,
ddim_eta
=
self
.
eta
,
ddim_discretize
=
p
.
ddim_discretize
,
verbose
=
False
)
try
:
self
.
sampler
.
make_schedule
(
ddim_num_steps
=
steps
,
ddim_eta
=
self
.
eta
,
ddim_discretize
=
p
.
ddim_discretize
,
verbose
=
False
)
except
Exception
:
self
.
sampler
.
make_schedule
(
ddim_num_steps
=
steps
+
1
,
ddim_eta
=
self
.
eta
,
ddim_discretize
=
p
.
ddim_discretize
,
verbose
=
False
)
x1
=
self
.
sampler
.
stochastic_encode
(
x
,
torch
.
tensor
([
t_enc
]
*
int
(
x
.
shape
[
0
]))
.
to
(
shared
.
device
),
noise
=
noise
)
x1
=
self
.
sampler
.
stochastic_encode
(
x
,
torch
.
tensor
([
t_enc
]
*
int
(
x
.
shape
[
0
]))
.
to
(
shared
.
device
),
noise
=
noise
)
self
.
init_latent
=
x
self
.
init_latent
=
x
...
@@ -239,18 +245,14 @@ class VanillaStableDiffusionSampler:
...
@@ -239,18 +245,14 @@ class VanillaStableDiffusionSampler:
self
.
last_latent
=
x
self
.
last_latent
=
x
self
.
step
=
0
self
.
step
=
0
steps
=
s
teps
or
p
.
steps
steps
=
s
elf
.
adjust_steps_if_invalid
(
p
,
steps
or
p
.
steps
)
# Wrap the conditioning models with additional image conditioning for inpainting model
# Wrap the conditioning models with additional image conditioning for inpainting model
if
image_conditioning
is
not
None
:
if
image_conditioning
is
not
None
:
conditioning
=
{
"c_concat"
:
[
image_conditioning
],
"c_crossattn"
:
[
conditioning
]}
conditioning
=
{
"c_concat"
:
[
image_conditioning
],
"c_crossattn"
:
[
conditioning
]}
unconditional_conditioning
=
{
"c_concat"
:
[
image_conditioning
],
"c_crossattn"
:
[
unconditional_conditioning
]}
unconditional_conditioning
=
{
"c_concat"
:
[
image_conditioning
],
"c_crossattn"
:
[
unconditional_conditioning
]}
# existing code fails with certain step counts, like 9
samples_ddim
=
self
.
launch_sampling
(
steps
,
lambda
:
self
.
sampler
.
sample
(
S
=
steps
,
conditioning
=
conditioning
,
batch_size
=
int
(
x
.
shape
[
0
]),
shape
=
x
[
0
]
.
shape
,
verbose
=
False
,
unconditional_guidance_scale
=
p
.
cfg_scale
,
unconditional_conditioning
=
unconditional_conditioning
,
x_T
=
x
,
eta
=
self
.
eta
)[
0
])
try
:
samples_ddim
=
self
.
launch_sampling
(
steps
,
lambda
:
self
.
sampler
.
sample
(
S
=
steps
,
conditioning
=
conditioning
,
batch_size
=
int
(
x
.
shape
[
0
]),
shape
=
x
[
0
]
.
shape
,
verbose
=
False
,
unconditional_guidance_scale
=
p
.
cfg_scale
,
unconditional_conditioning
=
unconditional_conditioning
,
x_T
=
x
,
eta
=
self
.
eta
)[
0
])
except
Exception
:
samples_ddim
=
self
.
launch_sampling
(
steps
,
lambda
:
self
.
sampler
.
sample
(
S
=
steps
+
1
,
conditioning
=
conditioning
,
batch_size
=
int
(
x
.
shape
[
0
]),
shape
=
x
[
0
]
.
shape
,
verbose
=
False
,
unconditional_guidance_scale
=
p
.
cfg_scale
,
unconditional_conditioning
=
unconditional_conditioning
,
x_T
=
x
,
eta
=
self
.
eta
)[
0
])
return
samples_ddim
return
samples_ddim
...
...
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