Skip to content
体验新版
项目
组织
正在加载...
登录
切换导航
打开侧边栏
Overbill1683
Stable Diffusion Webui
提交
53e7616b
S
Stable Diffusion Webui
项目概览
Overbill1683
/
Stable Diffusion Webui
10 个月 前同步成功
通知
1746
Star
81
Fork
0
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
分析
仓库
DevOps
项目成员
Pages
S
Stable Diffusion Webui
项目概览
项目概览
详情
发布
仓库
仓库
文件
提交
分支
标签
贡献者
分支图
比较
Pages
分析
分析
仓库分析
DevOps
成员
成员
收起侧边栏
关闭侧边栏
动态
分支图
提交
前往新版Gitcode,体验更适合开发者的 AI 搜索 >>
提交
53e7616b
编写于
8月 31, 2022
作者:
A
AUTOMATIC
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
DDIM support returned for img2img
上级
9427e4e2
变更
1
隐藏空白更改
内联
并排
Showing
1 changed file
with
55 addition
and
24 deletion
+55
-24
webui.py
webui.py
+55
-24
未找到文件。
webui.py
浏览文件 @
53e7616b
...
...
@@ -94,7 +94,7 @@ samplers = [
SamplerData
(
'DDIM'
,
lambda
:
VanillaStableDiffusionSampler
(
DDIMSampler
)),
SamplerData
(
'PLMS'
,
lambda
:
VanillaStableDiffusionSampler
(
PLMSSampler
)),
]
samplers_for_img2img
=
[
x
for
x
in
samplers
if
x
.
name
!=
'
DDIM'
and
x
.
name
!=
'
PLMS'
]
samplers_for_img2img
=
[
x
for
x
in
samplers
if
x
.
name
!=
'PLMS'
]
RealesrganModelInfo
=
namedtuple
(
"RealesrganModelInfo"
,
[
"name"
,
"location"
,
"model"
,
"netscale"
])
...
...
@@ -835,9 +835,37 @@ class StableDiffusionProcessing:
raise
NotImplementedError
()
def
p_sample_ddim_hook
(
sampler_wrapper
,
x_dec
,
cond
,
ts
,
*
args
,
**
kwargs
):
if
sampler_wrapper
.
mask
is
not
None
:
img_orig
=
sampler_wrapper
.
sampler
.
model
.
q_sample
(
sampler_wrapper
.
init_latent
,
ts
)
x_dec
=
img_orig
*
sampler_wrapper
.
mask
+
sampler_wrapper
.
nmask
*
x_dec
return
sampler_wrapper
.
orig_p_sample_ddim
(
x_dec
,
cond
,
ts
,
*
args
,
**
kwargs
)
class
VanillaStableDiffusionSampler
:
def
__init__
(
self
,
constructor
):
self
.
sampler
=
constructor
(
sd_model
)
self
.
orig_p_sample_ddim
=
self
.
sampler
.
p_sample_ddim
self
.
sampler
.
p_sample_ddim
=
lambda
x_dec
,
cond
,
ts
,
*
args
,
**
kwargs
:
p_sample_ddim_hook
(
self
,
x_dec
,
cond
,
ts
,
*
args
,
**
kwargs
)
self
.
mask
=
None
self
.
nmask
=
None
self
.
init_latent
=
None
def
sample_img2img
(
self
,
p
,
x
,
noise
,
conditioning
,
unconditional_conditioning
):
t_enc
=
int
(
min
(
p
.
denoising_strength
,
0.999
)
*
p
.
steps
)
self
.
sampler
.
make_schedule
(
ddim_num_steps
=
p
.
steps
,
ddim_eta
=
0.0
,
verbose
=
False
)
x1
=
self
.
sampler
.
stochastic_encode
(
x
,
torch
.
tensor
([
t_enc
]
*
int
(
x
.
shape
[
0
])).
to
(
device
),
noise
=
noise
)
self
.
mask
=
p
.
mask
self
.
nmask
=
p
.
nmask
self
.
init_latent
=
p
.
init_latent
samples
=
self
.
sampler
.
decode
(
x1
,
conditioning
,
t_enc
,
unconditional_guidance_scale
=
p
.
cfg_scale
,
unconditional_conditioning
=
unconditional_conditioning
)
return
samples
def
sample
(
self
,
p
:
StableDiffusionProcessing
,
x
,
conditioning
,
unconditional_conditioning
):
samples_ddim
,
_
=
self
.
sampler
.
sample
(
S
=
p
.
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
)
...
...
@@ -864,6 +892,27 @@ class KDiffusionSampler:
self
.
func
=
getattr
(
k_diffusion
.
sampling
,
self
.
funcname
)
self
.
model_wrap_cfg
=
CFGDenoiser
(
self
.
model_wrap
)
def
sample_img2img
(
self
,
p
,
x
,
noise
,
conditioning
,
unconditional_conditioning
):
t_enc
=
int
(
min
(
p
.
denoising_strength
,
0.999
)
*
p
.
steps
)
sigmas
=
self
.
model_wrap
.
get_sigmas
(
p
.
steps
)
noise
=
noise
*
sigmas
[
p
.
steps
-
t_enc
-
1
]
xi
=
x
+
noise
if
p
.
mask
is
not
None
:
if
p
.
inpainting_fill
==
2
:
xi
=
xi
*
p
.
mask
+
noise
*
p
.
nmask
elif
p
.
inpainting_fill
==
3
:
xi
=
xi
*
p
.
mask
sigma_sched
=
sigmas
[
p
.
steps
-
t_enc
-
1
:]
def
mask_cb
(
v
):
v
[
"denoised"
][:]
=
v
[
"denoised"
][:]
*
p
.
nmask
+
p
.
init_latent
*
p
.
mask
return
self
.
func
(
self
.
model_wrap_cfg
,
xi
,
sigma_sched
,
extra_args
=
{
'cond'
:
conditioning
,
'uncond'
:
unconditional_conditioning
,
'cond_scale'
:
p
.
cfg_scale
},
disable
=
False
,
callback
=
mask_cb
if
p
.
mask
is
not
None
else
None
)
def
sample
(
self
,
p
:
StableDiffusionProcessing
,
x
,
conditioning
,
unconditional_conditioning
):
sigmas
=
self
.
model_wrap
.
get_sigmas
(
p
.
steps
)
x
=
x
*
sigmas
[
0
]
...
...
@@ -1246,39 +1295,20 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
self
.
original_mask
=
self
.
original_mask
.
filter
(
ImageFilter
.
GaussianBlur
(
self
.
mask_blur
)).
convert
(
'L'
)
latmask
=
self
.
original_mask
.
convert
(
'RGB'
).
resize
((
self
.
init_latent
.
shape
[
3
],
self
.
init_latent
.
shape
[
2
]))
latmask
=
np
.
moveaxis
(
np
.
array
(
latmask
,
dtype
=
np
.
float
),
2
,
0
)
/
255
latmask
=
np
.
moveaxis
(
np
.
array
(
latmask
,
dtype
=
np
.
float
64
),
2
,
0
)
/
255
latmask
=
latmask
[
0
]
latmask
=
np
.
tile
(
latmask
[
None
],
(
4
,
1
,
1
))
self
.
mask
=
torch
.
asarray
(
1.0
-
latmask
).
to
(
device
).
type
(
sd_model
.
dtype
)
self
.
nmask
=
torch
.
asarray
(
latmask
).
to
(
device
).
type
(
sd_model
.
dtype
)
def
sample
(
self
,
x
,
conditioning
,
unconditional_conditioning
):
t_enc
=
int
(
min
(
self
.
denoising_strength
,
0.999
)
*
self
.
steps
)
sigmas
=
self
.
sampler
.
model_wrap
.
get_sigmas
(
self
.
steps
)
noise
=
x
*
sigmas
[
self
.
steps
-
t_enc
-
1
]
xi
=
self
.
init_latent
+
noise
samples
=
self
.
sampler
.
sample_img2img
(
self
,
self
.
init_latent
,
x
,
conditioning
,
unconditional_conditioning
)
if
self
.
mask
is
not
None
:
if
self
.
inpainting_fill
==
2
:
xi
=
xi
*
self
.
mask
+
noise
*
self
.
nmask
elif
self
.
inpainting_fill
==
3
:
xi
=
xi
*
self
.
mask
samples
=
samples
*
self
.
nmask
+
self
.
init_latent
*
self
.
mask
sigma_sched
=
sigmas
[
self
.
steps
-
t_enc
-
1
:]
def
mask_cb
(
v
):
v
[
"denoised"
][:]
=
v
[
"denoised"
][:]
*
self
.
nmask
+
self
.
init_latent
*
self
.
mask
samples_ddim
=
self
.
sampler
.
func
(
self
.
sampler
.
model_wrap_cfg
,
xi
,
sigma_sched
,
extra_args
=
{
'cond'
:
conditioning
,
'uncond'
:
unconditional_conditioning
,
'cond_scale'
:
self
.
cfg_scale
},
disable
=
False
,
callback
=
mask_cb
if
self
.
mask
is
not
None
else
None
)
if
self
.
mask
is
not
None
:
samples_ddim
=
samples_ddim
*
self
.
nmask
+
self
.
init_latent
*
self
.
mask
return
samples_ddim
return
samples
def
img2img
(
prompt
:
str
,
init_img
,
init_img_with_mask
,
steps
:
int
,
sampler_index
:
int
,
mask_blur
:
int
,
inpainting_fill
:
int
,
use_GFPGAN
:
bool
,
prompt_matrix
,
mode
:
int
,
n_iter
:
int
,
batch_size
:
int
,
cfg_scale
:
float
,
denoising_strength
:
float
,
seed
:
int
,
height
:
int
,
width
:
int
,
resize_mode
:
int
):
...
...
@@ -1544,6 +1574,7 @@ def run_extras(image, GFPGAN_strength, RealESRGAN_upscaling, RealESRGAN_model_in
if
have_realesrgan
and
RealESRGAN_upscaling
!=
1.0
:
image
=
upscale_with_realesrgan
(
image
,
RealESRGAN_upscaling
,
RealESRGAN_model_index
)
os
.
makedirs
(
outpath
,
exist_ok
=
True
)
base_count
=
len
(
os
.
listdir
(
outpath
))
save_image
(
image
,
outpath
,
f
"
{
base_count
:
05
}
"
,
None
,
''
,
opts
.
samples_format
,
short_filename
=
True
)
...
...
编辑
预览
Markdown
is supported
0%
请重试
或
添加新附件
.
添加附件
取消
You are about to add
0
people
to the discussion. Proceed with caution.
先完成此消息的编辑!
取消
想要评论请
注册
或
登录