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novelai-storage
Stable Diffusion Webui
Commits
258a2d4f
Commit
258a2d4f
authored
Sep 26, 2022
by
Martin Cairns
Committed by
AUTOMATIC1111
Sep 27, 2022
Browse files
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Plain Diff
Add option to img2imgalt.py to use sigma adjustment instead of original method for #736
parent
c74becca
Changes
1
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1 changed file
with
62 additions
and
6 deletions
+62
-6
scripts/img2imgalt.py
scripts/img2imgalt.py
+62
-6
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scripts/img2imgalt.py
View file @
258a2d4f
...
...
@@ -59,7 +59,55 @@ def find_noise_for_image(p, cond, uncond, cfg_scale, steps):
return
x
/
x
.
std
()
Cached
=
namedtuple
(
"Cached"
,
[
"noise"
,
"cfg_scale"
,
"steps"
,
"latent"
,
"original_prompt"
,
"original_negative_prompt"
])
Cached
=
namedtuple
(
"Cached"
,
[
"noise"
,
"cfg_scale"
,
"steps"
,
"latent"
,
"original_prompt"
,
"original_negative_prompt"
,
"sigma_adjustment"
])
# Based on changes suggested by briansemrau in https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/736
def
find_noise_for_image_sigma_adjustment
(
p
,
cond
,
uncond
,
cfg_scale
,
steps
):
x
=
p
.
init_latent
s_in
=
x
.
new_ones
([
x
.
shape
[
0
]])
dnw
=
K
.
external
.
CompVisDenoiser
(
shared
.
sd_model
)
sigmas
=
dnw
.
get_sigmas
(
steps
)
.
flip
(
0
)
shared
.
state
.
sampling_steps
=
steps
for
i
in
trange
(
1
,
len
(
sigmas
)):
shared
.
state
.
sampling_step
+=
1
x_in
=
torch
.
cat
([
x
]
*
2
)
sigma_in
=
torch
.
cat
([
sigmas
[
i
-
1
]
*
s_in
]
*
2
)
cond_in
=
torch
.
cat
([
uncond
,
cond
])
c_out
,
c_in
=
[
K
.
utils
.
append_dims
(
k
,
x_in
.
ndim
)
for
k
in
dnw
.
get_scalings
(
sigma_in
)]
if
i
==
1
:
t
=
dnw
.
sigma_to_t
(
torch
.
cat
([
sigmas
[
i
]
*
s_in
]
*
2
))
else
:
t
=
dnw
.
sigma_to_t
(
sigma_in
)
eps
=
shared
.
sd_model
.
apply_model
(
x_in
*
c_in
,
t
,
cond
=
cond_in
)
denoised_uncond
,
denoised_cond
=
(
x_in
+
eps
*
c_out
)
.
chunk
(
2
)
denoised
=
denoised_uncond
+
(
denoised_cond
-
denoised_uncond
)
*
cfg_scale
if
i
==
1
:
d
=
(
x
-
denoised
)
/
(
2
*
sigmas
[
i
])
else
:
d
=
(
x
-
denoised
)
/
sigmas
[
i
-
1
]
dt
=
sigmas
[
i
]
-
sigmas
[
i
-
1
]
x
=
x
+
d
*
dt
sd_samplers
.
store_latent
(
x
)
# This shouldn't be necessary, but solved some VRAM issues
del
x_in
,
sigma_in
,
cond_in
,
c_out
,
c_in
,
t
,
del
eps
,
denoised_uncond
,
denoised_cond
,
denoised
,
d
,
dt
shared
.
state
.
nextjob
()
return
x
/
sigmas
[
-
1
]
class
Script
(
scripts
.
Script
):
...
...
@@ -78,9 +126,10 @@ class Script(scripts.Script):
cfg
=
gr
.
Slider
(
label
=
"Decode CFG scale"
,
minimum
=
0.0
,
maximum
=
15.0
,
step
=
0.1
,
value
=
1.0
)
st
=
gr
.
Slider
(
label
=
"Decode steps"
,
minimum
=
1
,
maximum
=
150
,
step
=
1
,
value
=
50
)
randomness
=
gr
.
Slider
(
label
=
"Randomness"
,
minimum
=
0.0
,
maximum
=
1.0
,
step
=
0.01
,
value
=
0.0
)
return
[
original_prompt
,
original_negative_prompt
,
cfg
,
st
,
randomness
]
sigma_adjustment
=
gr
.
Checkbox
(
label
=
"Sigma adjustment for finding noise for image"
,
value
=
False
)
return
[
original_prompt
,
original_negative_prompt
,
cfg
,
st
,
randomness
,
sigma_adjustment
]
def
run
(
self
,
p
,
original_prompt
,
original_negative_prompt
,
cfg
,
st
,
randomness
):
def
run
(
self
,
p
,
original_prompt
,
original_negative_prompt
,
cfg
,
st
,
randomness
,
sigma_adjustment
):
p
.
batch_size
=
1
p
.
batch_count
=
1
...
...
@@ -88,7 +137,10 @@ class Script(scripts.Script):
def
sample_extra
(
conditioning
,
unconditional_conditioning
,
seeds
,
subseeds
,
subseed_strength
):
lat
=
(
p
.
init_latent
.
cpu
()
.
numpy
()
*
10
)
.
astype
(
int
)
same_params
=
self
.
cache
is
not
None
and
self
.
cache
.
cfg_scale
==
cfg
and
self
.
cache
.
steps
==
st
and
self
.
cache
.
original_prompt
==
original_prompt
and
self
.
cache
.
original_negative_prompt
==
original_negative_prompt
same_params
=
self
.
cache
is
not
None
and
self
.
cache
.
cfg_scale
==
cfg
and
self
.
cache
.
steps
==
st
\
and
self
.
cache
.
original_prompt
==
original_prompt
\
and
self
.
cache
.
original_negative_prompt
==
original_negative_prompt
\
and
self
.
cache
.
sigma_adjustment
==
sigma_adjustment
same_everything
=
same_params
and
self
.
cache
.
latent
.
shape
==
lat
.
shape
and
np
.
abs
(
self
.
cache
.
latent
-
lat
)
.
sum
()
<
100
if
same_everything
:
...
...
@@ -97,8 +149,11 @@ class Script(scripts.Script):
shared
.
state
.
job_count
+=
1
cond
=
p
.
sd_model
.
get_learned_conditioning
(
p
.
batch_size
*
[
original_prompt
])
uncond
=
p
.
sd_model
.
get_learned_conditioning
(
p
.
batch_size
*
[
original_negative_prompt
])
if
sigma_adjustment
:
rec_noise
=
find_noise_for_image_sigma_adjustment
(
p
,
cond
,
uncond
,
cfg
,
st
)
else
:
rec_noise
=
find_noise_for_image
(
p
,
cond
,
uncond
,
cfg
,
st
)
self
.
cache
=
Cached
(
rec_noise
,
cfg
,
st
,
lat
,
original_prompt
,
original_negative_prompt
)
self
.
cache
=
Cached
(
rec_noise
,
cfg
,
st
,
lat
,
original_prompt
,
original_negative_prompt
,
sigma_adjustment
)
rand_noise
=
processing
.
create_random_tensors
(
p
.
init_latent
.
shape
[
1
:],
[
p
.
seed
+
x
+
1
for
x
in
range
(
p
.
init_latent
.
shape
[
0
])])
...
...
@@ -121,6 +176,7 @@ class Script(scripts.Script):
p
.
extra_generation_params
[
"Decode CFG scale"
]
=
cfg
p
.
extra_generation_params
[
"Decode steps"
]
=
st
p
.
extra_generation_params
[
"Randomness"
]
=
randomness
p
.
extra_generation_params
[
"Sigma Adjustment"
]
=
sigma_adjustment
processed
=
processing
.
process_images
(
p
)
...
...
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