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Stable Diffusion Webui
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novelai-storage
Stable Diffusion Webui
Commits
54f74d44
Commit
54f74d44
authored
Aug 30, 2022
by
AUTOMATIC
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added first version of inpainting
fixed flag option
parent
587db9c4
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1
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72 additions
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10 deletions
+72
-10
webui.py
webui.py
+72
-10
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webui.py
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54f74d44
...
...
@@ -9,7 +9,7 @@ import torch.nn as nn
import
numpy
as
np
import
gradio
as
gr
from
omegaconf
import
OmegaConf
from
PIL
import
Image
,
ImageFont
,
ImageDraw
,
PngImagePlugin
from
PIL
import
Image
,
ImageFont
,
ImageDraw
,
PngImagePlugin
,
ImageFilter
,
ImageOps
from
torch
import
autocast
import
mimetypes
import
random
...
...
@@ -158,6 +158,7 @@ class Options:
"samples_save"
:
OptionInfo
(
True
,
"Save indiviual samples"
),
"samples_format"
:
OptionInfo
(
'png'
,
'File format for indiviual samples'
),
"grid_save"
:
OptionInfo
(
True
,
"Save image grids"
),
"return_grid"
:
OptionInfo
(
True
,
"Show grid in results for web"
),
"grid_format"
:
OptionInfo
(
'png'
,
'File format for grids'
),
"grid_extended_filename"
:
OptionInfo
(
False
,
"Add extended info (seed, prompt) to filename when saving grid"
),
"grid_only_if_multiple"
:
OptionInfo
(
True
,
"Do not save grids consisting of one picture"
),
...
...
@@ -957,6 +958,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
unwanted_grid_because_of_img_count
=
len
(
output_images
)
<
2
and
opts
.
grid_only_if_multiple
if
(
p
.
prompt_matrix
or
opts
.
grid_save
)
and
not
p
.
do_not_save_grid
and
not
unwanted_grid_because_of_img_count
:
return_grid
=
opts
.
return_grid
if
p
.
prompt_matrix
:
grid
=
image_grid
(
output_images
,
p
.
batch_size
,
rows
=
1
<<
((
len
(
prompt_matrix_parts
)
-
1
)
//
2
))
...
...
@@ -967,10 +970,13 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
print
(
"Error creating prompt_matrix text:"
,
file
=
sys
.
stderr
)
print
(
traceback
.
format_exc
(),
file
=
sys
.
stderr
)
output_images
.
insert
(
0
,
grid
)
return_grid
=
True
else
:
grid
=
image_grid
(
output_images
,
p
.
batch_size
)
if
return_grid
:
output_images
.
insert
(
0
,
grid
)
save_image
(
grid
,
p
.
outpath
,
f
"grid-{grid_count:04}"
,
seed
,
prompt
,
opts
.
grid_format
,
info
=
infotext
(),
short_filename
=
not
opts
.
grid_extended_filename
)
grid_count
+=
1
...
...
@@ -1042,7 +1048,7 @@ class Flagging(gr.FlaggingCallback):
os
.
makedirs
(
"log/images"
,
exist_ok
=
True
)
# those must match the "txt2img" function
prompt
,
ddim_steps
,
sampler_name
,
use_gfpgan
,
prompt_matrix
,
ddim_eta
,
n_iter
,
n_samples
,
cfg_scale
,
request_
seed
,
height
,
width
,
code
,
images
,
seed
,
comment
=
flag_data
prompt
,
steps
,
sampler_index
,
use_gfpgan
,
prompt_matrix
,
n_iter
,
batch_size
,
cfg_scale
,
seed
,
height
,
width
,
code
,
images
,
seed
,
comment
=
flag_data
filenames
=
[]
...
...
@@ -1067,7 +1073,7 @@ class Flagging(gr.FlaggingCallback):
filenames
.
append
(
filename
)
writer
.
writerow
([
prompt
,
seed
,
width
,
height
,
cfg_scale
,
ddim_
steps
,
filenames
[
0
]])
writer
.
writerow
([
prompt
,
seed
,
width
,
height
,
cfg_scale
,
steps
,
filenames
[
0
]])
print
(
"Logged:"
,
filenames
[
0
])
...
...
@@ -1097,27 +1103,64 @@ txt2img_interface = gr.Interface(
flagging_callback
=
Flagging
()
)
def
fill
(
image
,
mask
):
image_mod
=
Image
.
new
(
'RGBA'
,
(
image
.
width
,
image
.
height
))
image_masked
=
Image
.
new
(
'RGBa'
,
(
image
.
width
,
image
.
height
))
image_masked
.
paste
(
image
.
convert
(
"RGBA"
)
.
convert
(
"RGBa"
),
mask
=
ImageOps
.
invert
(
mask
.
convert
(
'L'
)))
image_masked
=
image_masked
.
convert
(
'RGBa'
)
for
radius
,
repeats
in
[(
64
,
1
),
(
16
,
2
),
(
4
,
4
),
(
2
,
2
),
(
0
,
1
)]:
blurred
=
image_masked
.
filter
(
ImageFilter
.
GaussianBlur
(
radius
))
.
convert
(
'RGBA'
)
for
_
in
range
(
repeats
):
image_mod
.
alpha_composite
(
blurred
)
return
image_mod
.
convert
(
"RGB"
)
class
StableDiffusionProcessingImg2Img
(
StableDiffusionProcessing
):
sampler
=
None
def
__init__
(
self
,
init_images
=
None
,
resize_mode
=
0
,
denoising_strength
=
0.75
,
**
kwargs
):
def
__init__
(
self
,
init_images
=
None
,
resize_mode
=
0
,
denoising_strength
=
0.75
,
mask
=
None
,
mask_blur
=
4
,
**
kwargs
):
super
()
.
__init__
(
**
kwargs
)
self
.
init_images
=
init_images
self
.
resize_mode
:
int
=
resize_mode
self
.
denoising_strength
:
float
=
denoising_strength
self
.
init_latent
=
None
self
.
original_mask
=
mask
self
.
mask_blur
=
mask_blur
self
.
mask
=
None
self
.
nmask
=
None
def
init
(
self
):
self
.
sampler
=
samplers_for_img2img
[
self
.
sampler_index
]
.
constructor
()
if
self
.
original_mask
is
not
None
:
if
self
.
mask_blur
>
0
:
self
.
original_mask
=
self
.
original_mask
.
filter
(
ImageFilter
.
GaussianBlur
(
self
.
mask_blur
))
.
convert
(
'L'
)
latmask
=
self
.
original_mask
.
convert
(
'RGB'
)
.
resize
((
64
,
64
))
latmask
=
np
.
moveaxis
(
np
.
array
(
latmask
,
dtype
=
np
.
float
),
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
)
imgs
=
[]
for
img
in
self
.
init_images
:
image
=
img
.
convert
(
"RGB"
)
image
=
resize_image
(
self
.
resize_mode
,
image
,
self
.
width
,
self
.
height
)
if
self
.
original_mask
is
not
None
image
=
fill
(
image
,
self
.
original_mask
)
image
=
np
.
array
(
image
)
.
astype
(
np
.
float32
)
/
255.0
image
=
np
.
moveaxis
(
image
,
2
,
0
)
imgs
.
append
(
image
)
if
len
(
imgs
)
==
1
:
...
...
@@ -1139,16 +1182,33 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
sigmas
=
self
.
sampler
.
model_wrap
.
get_sigmas
(
self
.
steps
)
noise
=
x
*
sigmas
[
self
.
steps
-
t_enc
-
1
]
xi
=
self
.
init_latent
+
noise
sigma_sched
=
sigmas
[
self
.
steps
-
t_enc
-
1
:]
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
)
#if self.mask is not None:
# xi = xi * self.mask + noise * self.nmask
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
def
img2img
(
prompt
:
str
,
init_img
,
ddim_steps
:
int
,
sampler_index
:
int
,
use_GFPGAN
:
bool
,
prompt_matrix
,
loopback
:
bool
,
sd_upscale
:
bool
,
n_iter
:
int
,
batch_size
:
int
,
cfg_scale
:
float
,
denoising_strength
:
float
,
seed
:
int
,
height
:
int
,
width
:
int
,
resize_mode
:
int
):
def
img2img
(
prompt
:
str
,
init_img
,
init_img_with_mask
,
ddim_steps
:
int
,
sampler_index
:
int
,
use_GFPGAN
:
bool
,
prompt_matrix
,
loopback
:
bool
,
sd_upscale
:
bool
,
n_iter
:
int
,
batch_size
:
int
,
cfg_scale
:
float
,
denoising_strength
:
float
,
seed
:
int
,
height
:
int
,
width
:
int
,
resize_mode
:
int
):
outpath
=
opts
.
outdir
or
"outputs/img2img-samples"
if
init_img_with_mask
is
not
None
:
image
=
init_img_with_mask
[
'image'
]
mask
=
init_img_with_mask
[
'mask'
]
else
:
image
=
init_img
mask
=
None
assert
0.
<=
denoising_strength
<=
1.
,
'can only work with strength in [0.0, 1.0]'
p
=
StableDiffusionProcessingImg2Img
(
...
...
@@ -1164,7 +1224,8 @@ def img2img(prompt: str, init_img, ddim_steps: int, sampler_index: int, use_GFPG
height
=
height
,
prompt_matrix
=
prompt_matrix
,
use_GFPGAN
=
use_GFPGAN
,
init_images
=
[
init_img
],
init_images
=
[
image
],
mask
=
mask
,
resize_mode
=
resize_mode
,
denoising_strength
=
denoising_strength
,
extra_generation_params
=
{
"Denoising Strength"
:
denoising_strength
}
...
...
@@ -1262,7 +1323,8 @@ img2img_interface = gr.Interface(
wrap_gradio_call
(
img2img
),
inputs
=
[
gr
.
Textbox
(
placeholder
=
"A fantasy landscape, trending on artstation."
,
lines
=
1
),
gr
.
Image
(
value
=
sample_img2img
,
source
=
"upload"
,
interactive
=
True
,
type
=
"pil"
),
gr
.
Image
(
label
=
"Image for img2img"
,
source
=
"upload"
,
interactive
=
True
,
type
=
"pil"
),
gr
.
Image
(
label
=
"Image for inpainting with mask"
,
source
=
"upload"
,
interactive
=
True
,
type
=
"pil"
,
tool
=
"sketch"
),
gr
.
Slider
(
minimum
=
1
,
maximum
=
150
,
step
=
1
,
label
=
"Sampling Steps"
,
value
=
20
),
gr
.
Radio
(
label
=
'Sampling method'
,
choices
=
[
x
.
name
for
x
in
samplers_for_img2img
],
value
=
samplers_for_img2img
[
0
]
.
name
,
type
=
"index"
),
gr
.
Checkbox
(
label
=
'Fix faces using GFPGAN'
,
value
=
False
,
visible
=
have_gfpgan
),
...
...
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