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Stable Diffusion Webui
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bef40851
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Stable Diffusion Webui
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bef40851
编写于
7月 29, 2023
作者:
A
AUTOMATIC1111
提交者:
GitHub
7月 29, 2023
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差异文件
Merge pull request #11850 from lambertae/restart_sampling
Restart sampling
上级
9a52a30d
8de6d3ff
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
72 addition
and
2 deletion
+72
-2
README.md
README.md
+1
-0
modules/sd_samplers_kdiffusion.py
modules/sd_samplers_kdiffusion.py
+71
-2
未找到文件。
README.md
浏览文件 @
bef40851
...
...
@@ -145,6 +145,7 @@ Licenses for borrowed code can be found in `Settings -> Licenses` screen, and al
-
Stable Diffusion - https://github.com/CompVis/stable-diffusion, https://github.com/CompVis/taming-transformers
-
k-diffusion - https://github.com/crowsonkb/k-diffusion.git
-
Restart sampling - https://github.com/Newbeeer/diffusion_restart_sampling
-
GFPGAN - https://github.com/TencentARC/GFPGAN.git
-
CodeFormer - https://github.com/sczhou/CodeFormer
-
ESRGAN - https://github.com/xinntao/ESRGAN
...
...
modules/sd_samplers_kdiffusion.py
浏览文件 @
bef40851
...
...
@@ -30,12 +30,81 @@ samplers_k_diffusion = [
(
'DPM++ 2M Karras'
,
'sample_dpmpp_2m'
,
[
'k_dpmpp_2m_ka'
],
{
'scheduler'
:
'karras'
}),
(
'DPM++ SDE Karras'
,
'sample_dpmpp_sde'
,
[
'k_dpmpp_sde_ka'
],
{
'scheduler'
:
'karras'
,
"second_order"
:
True
,
"brownian_noise"
:
True
}),
(
'DPM++ 2M SDE Karras'
,
'sample_dpmpp_2m_sde'
,
[
'k_dpmpp_2m_sde_ka'
],
{
'scheduler'
:
'karras'
,
"brownian_noise"
:
True
}),
(
'Restart (new)'
,
'restart_sampler'
,
[
'restart'
],
{
'scheduler'
:
'karras'
,
"second_order"
:
True
}),
]
@
torch
.
no_grad
()
def
restart_sampler
(
model
,
x
,
sigmas
,
extra_args
=
None
,
callback
=
None
,
disable
=
None
,
s_noise
=
1.
,
restart_list
=
None
):
"""Implements restart sampling in Restart Sampling for Improving Generative Processes (2023)"""
'''Restart_list format: {min_sigma: [ restart_steps, restart_times, max_sigma]}'''
'''If restart_list is None: will choose restart_list automatically, otherwise will use the given restart_list'''
from
tqdm.auto
import
trange
extra_args
=
{}
if
extra_args
is
None
else
extra_args
s_in
=
x
.
new_ones
([
x
.
shape
[
0
]])
step_id
=
0
from
k_diffusion.sampling
import
to_d
,
get_sigmas_karras
def
heun_step
(
x
,
old_sigma
,
new_sigma
,
second_order
=
True
):
nonlocal
step_id
denoised
=
model
(
x
,
old_sigma
*
s_in
,
**
extra_args
)
d
=
to_d
(
x
,
old_sigma
,
denoised
)
if
callback
is
not
None
:
callback
({
'x'
:
x
,
'i'
:
step_id
,
'sigma'
:
new_sigma
,
'sigma_hat'
:
old_sigma
,
'denoised'
:
denoised
})
dt
=
new_sigma
-
old_sigma
if
new_sigma
==
0
or
not
second_order
:
# Euler method
x
=
x
+
d
*
dt
else
:
# Heun's method
x_2
=
x
+
d
*
dt
denoised_2
=
model
(
x_2
,
new_sigma
*
s_in
,
**
extra_args
)
d_2
=
to_d
(
x_2
,
new_sigma
,
denoised_2
)
d_prime
=
(
d
+
d_2
)
/
2
x
=
x
+
d_prime
*
dt
step_id
+=
1
return
x
steps
=
sigmas
.
shape
[
0
]
-
1
if
restart_list
is
None
:
if
steps
>=
20
:
restart_steps
=
9
restart_times
=
1
if
steps
>=
36
:
restart_steps
=
steps
//
4
restart_times
=
2
sigmas
=
get_sigmas_karras
(
steps
-
restart_steps
*
restart_times
,
sigmas
[
-
2
].
item
(),
sigmas
[
0
].
item
(),
device
=
sigmas
.
device
)
restart_list
=
{
0.1
:
[
restart_steps
+
1
,
restart_times
,
2
]}
else
:
restart_list
=
dict
()
temp_list
=
dict
()
for
key
,
value
in
restart_list
.
items
():
temp_list
[
int
(
torch
.
argmin
(
abs
(
sigmas
-
key
),
dim
=
0
))]
=
value
restart_list
=
temp_list
step_list
=
[]
for
i
in
range
(
len
(
sigmas
)
-
1
):
step_list
.
append
((
sigmas
[
i
],
sigmas
[
i
+
1
]))
if
i
+
1
in
restart_list
:
restart_steps
,
restart_times
,
restart_max
=
restart_list
[
i
+
1
]
min_idx
=
i
+
1
max_idx
=
int
(
torch
.
argmin
(
abs
(
sigmas
-
restart_max
),
dim
=
0
))
if
max_idx
<
min_idx
:
sigma_restart
=
get_sigmas_karras
(
restart_steps
,
sigmas
[
min_idx
].
item
(),
sigmas
[
max_idx
].
item
(),
device
=
sigmas
.
device
)[:
-
1
]
while
restart_times
>
0
:
restart_times
-=
1
step_list
.
extend
([(
old_sigma
,
new_sigma
)
for
(
old_sigma
,
new_sigma
)
in
zip
(
sigma_restart
[:
-
1
],
sigma_restart
[
1
:])])
last_sigma
=
None
for
i
in
trange
(
len
(
step_list
),
disable
=
disable
):
if
last_sigma
is
None
:
last_sigma
=
step_list
[
i
][
0
]
elif
last_sigma
<
step_list
[
i
][
0
]:
x
=
x
+
k_diffusion
.
sampling
.
torch
.
randn_like
(
x
)
*
s_noise
*
(
step_list
[
i
][
0
]
**
2
-
last_sigma
**
2
)
**
0.5
x
=
heun_step
(
x
,
step_list
[
i
][
0
],
step_list
[
i
][
1
])
last_sigma
=
step_list
[
i
][
1
]
return
x
samplers_data_k_diffusion
=
[
sd_samplers_common
.
SamplerData
(
label
,
lambda
model
,
funcname
=
funcname
:
KDiffusionSampler
(
funcname
,
model
),
aliases
,
options
)
for
label
,
funcname
,
aliases
,
options
in
samplers_k_diffusion
if
hasattr
(
k_diffusion
.
sampling
,
funcname
)
if
(
hasattr
(
k_diffusion
.
sampling
,
funcname
)
or
funcname
==
'restart_sampler'
)
]
sampler_extra_params
=
{
...
...
@@ -270,7 +339,7 @@ class KDiffusionSampler:
self
.
model_wrap
=
denoiser
(
sd_model
,
quantize
=
shared
.
opts
.
enable_quantization
)
self
.
funcname
=
funcname
self
.
func
=
getattr
(
k_diffusion
.
sampling
,
self
.
funcname
)
self
.
func
=
getattr
(
k_diffusion
.
sampling
,
self
.
funcname
)
if
funcname
!=
"restart_sampler"
else
restart_sampler
self
.
extra_params
=
sampler_extra_params
.
get
(
funcname
,
[])
self
.
model_wrap_cfg
=
CFGDenoiser
(
self
.
model_wrap
)
self
.
sampler_noises
=
None
...
...
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