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Administrator
stable-diffusion-webui
Commits
26a3fd2f
Commit
26a3fd2f
authored
Oct 27, 2022
by
random_thoughtss
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Plain Diff
Highres fix works with unmasked latent.
Also refactor the mask creation to make it more accesible.
parent
f3f2ffd4
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1 changed file
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76 additions
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58 deletions
+76
-58
processing.py
modules/processing.py
+76
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modules/processing.py
View file @
26a3fd2f
...
@@ -129,6 +129,73 @@ class StableDiffusionProcessing():
...
@@ -129,6 +129,73 @@ class StableDiffusionProcessing():
self
.
all_seeds
=
None
self
.
all_seeds
=
None
self
.
all_subseeds
=
None
self
.
all_subseeds
=
None
def
txt2img_image_conditioning
(
self
,
x
,
width
=
None
,
height
=
None
):
if
self
.
sampler
.
conditioning_key
not
in
{
'hybrid'
,
'concat'
}:
# Dummy zero conditioning if we're not using inpainting model.
# Still takes up a bit of memory, but no encoder call.
# Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
return
torch
.
zeros
(
x
.
shape
[
0
],
5
,
1
,
1
,
dtype
=
x
.
dtype
,
device
=
x
.
device
)
height
=
height
or
self
.
height
width
=
width
or
self
.
width
# The "masked-image" in this case will just be all zeros since the entire image is masked.
image_conditioning
=
torch
.
zeros
(
x
.
shape
[
0
],
3
,
height
,
width
,
device
=
x
.
device
)
image_conditioning
=
self
.
sd_model
.
get_first_stage_encoding
(
self
.
sd_model
.
encode_first_stage
(
image_conditioning
))
# Add the fake full 1s mask to the first dimension.
image_conditioning
=
torch
.
nn
.
functional
.
pad
(
image_conditioning
,
(
0
,
0
,
0
,
0
,
1
,
0
),
value
=
1.0
)
image_conditioning
=
image_conditioning
.
to
(
x
.
dtype
)
return
image_conditioning
def
img2img_image_conditioning
(
self
,
source_image
,
latent_image
,
image_mask
=
None
):
if
self
.
sampler
.
conditioning_key
not
in
{
'hybrid'
,
'concat'
}:
# Dummy zero conditioning if we're not using inpainting model.
return
torch
.
zeros
(
latent_image
.
shape
[
0
],
5
,
1
,
1
,
dtype
=
latent_image
.
dtype
,
device
=
latent_image
.
device
)
# Handle the different mask inputs
if
image_mask
is
not
None
:
if
torch
.
is_tensor
(
image_mask
):
conditioning_mask
=
image_mask
else
:
conditioning_mask
=
np
.
array
(
image_mask
.
convert
(
"L"
))
conditioning_mask
=
conditioning_mask
.
astype
(
np
.
float32
)
/
255.0
conditioning_mask
=
torch
.
from_numpy
(
conditioning_mask
[
None
,
None
])
# Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0
conditioning_mask
=
torch
.
round
(
conditioning_mask
)
else
:
conditioning_mask
=
torch
.
ones
(
1
,
1
,
*
source_image
.
shape
[
-
2
:])
# Create another latent image, this time with a masked version of the original input.
# Smoothly interpolate between the masked and unmasked latent conditioning image using a parameter.
conditioning_mask
=
conditioning_mask
.
to
(
source_image
.
device
)
conditioning_image
=
torch
.
lerp
(
source_image
,
source_image
*
(
1.0
-
conditioning_mask
),
getattr
(
self
,
"inpainting_mask_weight"
,
shared
.
opts
.
inpainting_mask_weight
)
)
# Encode the new masked image using first stage of network.
conditioning_image
=
self
.
sd_model
.
get_first_stage_encoding
(
self
.
sd_model
.
encode_first_stage
(
conditioning_image
))
# Create the concatenated conditioning tensor to be fed to `c_concat`
conditioning_mask
=
torch
.
nn
.
functional
.
interpolate
(
conditioning_mask
,
size
=
latent_image
.
shape
[
-
2
:])
conditioning_mask
=
conditioning_mask
.
expand
(
conditioning_image
.
shape
[
0
],
-
1
,
-
1
,
-
1
)
image_conditioning
=
torch
.
cat
([
conditioning_mask
,
conditioning_image
],
dim
=
1
)
image_conditioning
=
image_conditioning
.
to
(
shared
.
device
)
.
type
(
self
.
sd_model
.
dtype
)
return
image_conditioning
def
init
(
self
,
all_prompts
,
all_seeds
,
all_subseeds
):
def
init
(
self
,
all_prompts
,
all_seeds
,
all_subseeds
):
pass
pass
...
@@ -571,37 +638,16 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
...
@@ -571,37 +638,16 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
self
.
truncate_x
=
int
(
self
.
firstphase_width
-
firstphase_width_truncated
)
//
opt_f
self
.
truncate_x
=
int
(
self
.
firstphase_width
-
firstphase_width_truncated
)
//
opt_f
self
.
truncate_y
=
int
(
self
.
firstphase_height
-
firstphase_height_truncated
)
//
opt_f
self
.
truncate_y
=
int
(
self
.
firstphase_height
-
firstphase_height_truncated
)
//
opt_f
def
create_dummy_mask
(
self
,
x
,
width
=
None
,
height
=
None
):
if
self
.
sampler
.
conditioning_key
in
{
'hybrid'
,
'concat'
}:
height
=
height
or
self
.
height
width
=
width
or
self
.
width
# The "masked-image" in this case will just be all zeros since the entire image is masked.
image_conditioning
=
torch
.
zeros
(
x
.
shape
[
0
],
3
,
height
,
width
,
device
=
x
.
device
)
image_conditioning
=
self
.
sd_model
.
get_first_stage_encoding
(
self
.
sd_model
.
encode_first_stage
(
image_conditioning
))
# Add the fake full 1s mask to the first dimension.
image_conditioning
=
torch
.
nn
.
functional
.
pad
(
image_conditioning
,
(
0
,
0
,
0
,
0
,
1
,
0
),
value
=
1.0
)
image_conditioning
=
image_conditioning
.
to
(
x
.
dtype
)
else
:
# Dummy zero conditioning if we're not using inpainting model.
# Still takes up a bit of memory, but no encoder call.
# Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
image_conditioning
=
torch
.
zeros
(
x
.
shape
[
0
],
5
,
1
,
1
,
dtype
=
x
.
dtype
,
device
=
x
.
device
)
return
image_conditioning
def
sample
(
self
,
conditioning
,
unconditional_conditioning
,
seeds
,
subseeds
,
subseed_strength
):
def
sample
(
self
,
conditioning
,
unconditional_conditioning
,
seeds
,
subseeds
,
subseed_strength
):
self
.
sampler
=
sd_samplers
.
create_sampler_with_index
(
sd_samplers
.
samplers
,
self
.
sampler_index
,
self
.
sd_model
)
self
.
sampler
=
sd_samplers
.
create_sampler_with_index
(
sd_samplers
.
samplers
,
self
.
sampler_index
,
self
.
sd_model
)
if
not
self
.
enable_hr
:
if
not
self
.
enable_hr
:
x
=
create_random_tensors
([
opt_C
,
self
.
height
//
opt_f
,
self
.
width
//
opt_f
],
seeds
=
seeds
,
subseeds
=
subseeds
,
subseed_strength
=
self
.
subseed_strength
,
seed_resize_from_h
=
self
.
seed_resize_from_h
,
seed_resize_from_w
=
self
.
seed_resize_from_w
,
p
=
self
)
x
=
create_random_tensors
([
opt_C
,
self
.
height
//
opt_f
,
self
.
width
//
opt_f
],
seeds
=
seeds
,
subseeds
=
subseeds
,
subseed_strength
=
self
.
subseed_strength
,
seed_resize_from_h
=
self
.
seed_resize_from_h
,
seed_resize_from_w
=
self
.
seed_resize_from_w
,
p
=
self
)
samples
=
self
.
sampler
.
sample
(
self
,
x
,
conditioning
,
unconditional_conditioning
,
image_conditioning
=
self
.
create_dummy_mask
(
x
))
samples
=
self
.
sampler
.
sample
(
self
,
x
,
conditioning
,
unconditional_conditioning
,
image_conditioning
=
self
.
txt2img_image_conditioning
(
x
))
return
samples
return
samples
x
=
create_random_tensors
([
opt_C
,
self
.
firstphase_height
//
opt_f
,
self
.
firstphase_width
//
opt_f
],
seeds
=
seeds
,
subseeds
=
subseeds
,
subseed_strength
=
self
.
subseed_strength
,
seed_resize_from_h
=
self
.
seed_resize_from_h
,
seed_resize_from_w
=
self
.
seed_resize_from_w
,
p
=
self
)
x
=
create_random_tensors
([
opt_C
,
self
.
firstphase_height
//
opt_f
,
self
.
firstphase_width
//
opt_f
],
seeds
=
seeds
,
subseeds
=
subseeds
,
subseed_strength
=
self
.
subseed_strength
,
seed_resize_from_h
=
self
.
seed_resize_from_h
,
seed_resize_from_w
=
self
.
seed_resize_from_w
,
p
=
self
)
samples
=
self
.
sampler
.
sample
(
self
,
x
,
conditioning
,
unconditional_conditioning
,
image_conditioning
=
self
.
create_dummy_mask
(
x
,
self
.
firstphase_width
,
self
.
firstphase_height
))
samples
=
self
.
sampler
.
sample
(
self
,
x
,
conditioning
,
unconditional_conditioning
,
image_conditioning
=
self
.
txt2img_image_conditioning
(
x
,
self
.
firstphase_width
,
self
.
firstphase_height
))
samples
=
samples
[:,
:,
self
.
truncate_y
//
2
:
samples
.
shape
[
2
]
-
self
.
truncate_y
//
2
,
self
.
truncate_x
//
2
:
samples
.
shape
[
3
]
-
self
.
truncate_x
//
2
]
samples
=
samples
[:,
:,
self
.
truncate_y
//
2
:
samples
.
shape
[
2
]
-
self
.
truncate_y
//
2
,
self
.
truncate_x
//
2
:
samples
.
shape
[
3
]
-
self
.
truncate_x
//
2
]
...
@@ -638,7 +684,12 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
...
@@ -638,7 +684,12 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
x
=
None
x
=
None
devices
.
torch_gc
()
devices
.
torch_gc
()
samples
=
self
.
sampler
.
sample_img2img
(
self
,
samples
,
noise
,
conditioning
,
unconditional_conditioning
,
steps
=
self
.
steps
,
image_conditioning
=
self
.
create_dummy_mask
(
samples
))
image_conditioning
=
self
.
img2img_image_conditioning
(
decoded_samples
,
samples
,
decoded_samples
.
new_ones
(
decoded_samples
.
shape
[
0
],
1
,
decoded_samples
.
shape
[
2
],
decoded_samples
.
shape
[
3
])
)
samples
=
self
.
sampler
.
sample_img2img
(
self
,
samples
,
noise
,
conditioning
,
unconditional_conditioning
,
steps
=
self
.
steps
,
image_conditioning
=
image_conditioning
)
return
samples
return
samples
...
@@ -770,40 +821,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
...
@@ -770,40 +821,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
elif
self
.
inpainting_fill
==
3
:
elif
self
.
inpainting_fill
==
3
:
self
.
init_latent
=
self
.
init_latent
*
self
.
mask
self
.
init_latent
=
self
.
init_latent
*
self
.
mask
if
self
.
sampler
.
conditioning_key
in
{
'hybrid'
,
'concat'
}:
self
.
image_conditioning
=
self
.
img2img_image_conditioning
(
image
,
self
.
init_latent
,
self
.
image_mask
)
if
self
.
image_mask
is
not
None
:
conditioning_mask
=
np
.
array
(
self
.
image_mask
.
convert
(
"L"
))
conditioning_mask
=
conditioning_mask
.
astype
(
np
.
float32
)
/
255.0
conditioning_mask
=
torch
.
from_numpy
(
conditioning_mask
[
None
,
None
])
# Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0
conditioning_mask
=
torch
.
round
(
conditioning_mask
)
else
:
conditioning_mask
=
torch
.
ones
(
1
,
1
,
*
image
.
shape
[
-
2
:])
# Create another latent image, this time with a masked version of the original input.
conditioning_mask
=
conditioning_mask
.
to
(
image
.
device
)
# Smoothly interpolate between the masked and unmasked latent conditioning image.
conditioning_image
=
torch
.
lerp
(
image
,
image
*
(
1.0
-
conditioning_mask
),
getattr
(
self
,
"inpainting_mask_weight"
,
shared
.
opts
.
inpainting_mask_weight
)
)
conditioning_image
=
self
.
sd_model
.
get_first_stage_encoding
(
self
.
sd_model
.
encode_first_stage
(
conditioning_image
))
# Create the concatenated conditioning tensor to be fed to `c_concat`
conditioning_mask
=
torch
.
nn
.
functional
.
interpolate
(
conditioning_mask
,
size
=
self
.
init_latent
.
shape
[
-
2
:])
conditioning_mask
=
conditioning_mask
.
expand
(
conditioning_image
.
shape
[
0
],
-
1
,
-
1
,
-
1
)
self
.
image_conditioning
=
torch
.
cat
([
conditioning_mask
,
conditioning_image
],
dim
=
1
)
self
.
image_conditioning
=
self
.
image_conditioning
.
to
(
shared
.
device
)
.
type
(
self
.
sd_model
.
dtype
)
else
:
self
.
image_conditioning
=
torch
.
zeros
(
self
.
init_latent
.
shape
[
0
],
5
,
1
,
1
,
dtype
=
self
.
init_latent
.
dtype
,
device
=
self
.
init_latent
.
device
)
def
sample
(
self
,
conditioning
,
unconditional_conditioning
,
seeds
,
subseeds
,
subseed_strength
):
def
sample
(
self
,
conditioning
,
unconditional_conditioning
,
seeds
,
subseeds
,
subseed_strength
):
...
...
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