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# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import
math
import
warnings
from
typing
import
List
,
Optional
,
Tuple
,
Union
import
numpy
as
np
import
PIL
.
Image
import
torch
import
torch
.
nn
.
functional
as
F
from
PIL
import
Image
,
ImageFilter
,
ImageOps
from
.
configuration_utils
import
ConfigMixin
,
register_to_config
from
.
utils
import
CONFIG_NAME
,
PIL_INTERPOLATION
,
deprecate
PipelineImageInput
=
Union
[
PIL
.
Image
.
Image
,
np
.
ndarray
,
torch
.
Tensor
,
List
[
PIL
.
Image
.
Image
],
List
[
np
.
ndarray
],
List
[
torch
.
Tensor
],
]
PipelineDepthInput
=
PipelineImageInput
def
is_valid_image
(
image
):
return
isinstance
(
image
,
PIL
.
Image
.
Image
)
or
isinstance
(
image
, (
np
.
ndarray
,
torch
.
Tensor
))
and
image
.
ndim
in
(
2
,
3
)
def
is_valid_image_imagelist
(
images
):
# check if the image input is one of the supported formats for image and image list:
# it can be either one of below 3
# (1) a 4d pytorch tensor or numpy array,
# (2) a valid image: PIL.Image.Image, 2-d np.ndarray or torch.Tensor (grayscale image), 3-d np.ndarray or torch.Tensor
# (3) a list of valid image
if
isinstance
(
images
, (
np
.
ndarray
,
torch
.
Tensor
))
and
images
.
ndim
==
4
:
return
True
elif
is_valid_image
(
images
):
return
True
elif
isinstance
(
images
,
list
):
return
all
(
is_valid_image
(
image
)
for
image
in
images
)
return
False
class
VaeImageProcessor
(
ConfigMixin
):
"""
Image processor for VAE.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to downscale the image's (height, width) dimensions to multiples of `vae_scale_factor`. Can accept
`height` and `width` arguments from [`image_processor.VaeImageProcessor.preprocess`] method.
vae_scale_factor (`int`, *optional*, defaults to `8`):
VAE scale factor. If `do_resize` is `True`, the image is automatically resized to multiples of this factor.
resample (`str`, *optional*, defaults to `lanczos`):
Resampling filter to use when resizing the image.
do_normalize (`bool`, *optional*, defaults to `True`):
Whether to normalize the image to [-1,1].
do_binarize (`bool`, *optional*, defaults to `False`):
Whether to binarize the image to 0/1.
do_convert_rgb (`bool`, *optional*, defaults to be `False`):
Whether to convert the images to RGB format.
do_convert_grayscale (`bool`, *optional*, defaults to be `False`):
Whether to convert the images to grayscale format.
"""
config_name
=
CONFIG_NAME
@
register_to_config
def
__init__
(
self
,
do_resize
:
bool
=
True
,
vae_scale_factor
:
int
=
8
,
vae_latent_channels
:
int
=
4
,
resample
:
str
=
"lanczos"
,
do_normalize
:
bool
=
True
,
do_binarize
:
bool
=
False
,
do_convert_rgb
:
bool
=
False
,
do_convert_grayscale
:
bool
=
False
,
):
super
().
__init__
()
if
do_convert_rgb
and
do_convert_grayscale
:
raise
ValueError
(
"`do_convert_rgb` and `do_convert_grayscale` can not both be set to `True`,"
" if you intended to convert the image into RGB format, please set `do_convert_grayscale = False`."
,
" if you intended to convert the image into grayscale format, please set `do_convert_rgb = False`"
,
)
@
staticmethod
def
numpy_to_pil
(
images
:
np
.
ndarray
)
->
List
[
PIL
.
Image
.
Image
]:
"""
Convert a numpy image or a batch of images to a PIL image.
"""
if
images
.
ndim
==
3
:
images
=
images
[
None
, ...]
images
=
(
images
*
255
).
round
().
astype
(
"uint8"
)
if
images
.
shape
[
-
1
]
==
1
:
# special case for grayscale (single channel) images
pil_images
=
[
Image
.
fromarray
(
image
.
squeeze
(),
mode
=
"L"
)
for
image
in
images
]
else
:
pil_images
=
[
Image
.
fromarray
(
image
)
for
image
in
images
]
return
pil_images
@
staticmethod
def
pil_to_numpy
(
images
:
Union
[
List
[
PIL
.
Image
.
Image
],
PIL
.
Image
.
Image
])
->
np
.
ndarray
:
"""
Convert a PIL image or a list of PIL images to NumPy arrays.
"""
if
not
isinstance
(
images
,
list
):
images
=
[
images
]
images
=
[
np
.
array
(
image
).
astype
(
np
.
float32
)
/
255.0
for
image
in
images
]
images
=
np
.
stack
(
images
,
axis
=
0
)
return
images
@
staticmethod
def
numpy_to_pt
(
images
:
np
.
ndarray
)
->
torch
.
Tensor
:
"""
Convert a NumPy image to a PyTorch tensor.
"""
if
images
.
ndim
==
3
:
images
=
images
[...,
None
]
images
=
torch
.
from_numpy
(
images
.
transpose
(
0
,
3
,
1
,
2
))
return
images
@
staticmethod
def
pt_to_numpy
(
images
:
torch
.
Tensor
)
->
np
.
ndarray
:
"""
Convert a PyTorch tensor to a NumPy image.
"""
images
=
images
.
cpu
().
permute
(
0
,
2
,
3
,
1
).
float
().
numpy
()
return
images
@
staticmethod
def
normalize
(
images
:
Union
[
np
.
ndarray
,
torch
.
Tensor
])
->
Union
[
np
.
ndarray
,
torch
.
Tensor
]:
"""
Normalize an image array to [-1,1].
"""
return
2.0
*
images
-
1.0
@
staticmethod
def
denormalize
(
images
:
Union
[
np
.
ndarray
,
torch
.
Tensor
])
->
Union
[
np
.
ndarray
,
torch
.
Tensor
]:
"""
Denormalize an image array to [0,1].
"""
return
(
images
/
2
+
0.5
).
clamp
(
0
,
1
)
@
staticmethod
def
convert_to_rgb
(
image
:
PIL
.
Image
.
Image
)
->
PIL
.
Image
.
Image
:
"""
Converts a PIL image to RGB format.
"""
image
=
image
.
convert
(
"RGB"
)
return
image
@
staticmethod
def
convert_to_grayscale
(
image
:
PIL
.
Image
.
Image
)
->
PIL
.
Image
.
Image
:
"""
Converts a PIL image to grayscale format.
"""
image
=
image
.
convert
(
"L"
)
return
image
@
staticmethod
def
blur
(
image
:
PIL
.
Image
.
Image
,
blur_factor
:
int
=
4
)
->
PIL
.
Image
.
Image
:
"""
Applies Gaussian blur to an image.
"""
image
=
image
.
filter
(
ImageFilter
.
GaussianBlur
(
blur_factor
))
return
image
@
staticmethod
def
get_crop_region
(
mask_image
:
PIL
.
Image
.
Image
,
width
:
int
,
height
:
int
,
pad
=
0
):
"""
Finds a rectangular region that contains all masked ares in an image, and expands region to match the aspect
ratio of the original image; for example, if user drew mask in a 128x32 region, and the dimensions for
processing are 512x512, the region will be expanded to 128x128.
Args:
mask_image (PIL.Image.Image): Mask image.
width (int): Width of the image to be processed.
height (int): Height of the image to be processed.
pad (int, optional): Padding to be added to the crop region. Defaults to 0.
Returns:
tuple: (x1, y1, x2, y2) represent a rectangular region that contains all masked ares in an image and
matches the original aspect ratio.
"""
mask_image
=
mask_image
.
convert
(
"L"
)
mask
=
np
.
array
(
mask_image
)
# 1. find a rectangular region that contains all masked ares in an image
h
,
w
=
mask
.
shape
crop_left
=
0
for
i
in
range
(
w
):
if
not
(
mask
[:,
i
]
==
0
).
all
():
break
crop_left
+=
1
crop_right
=
0
for
i
in
reversed
(
range
(
w
)):
if
not
(
mask
[:,
i
]
==
0
).
all
():
break
crop_right
+=
1
crop_top
=
0
for
i
in
range
(
h
):
if
not
(
mask
[
i
]
==
0
).
all
():
break
crop_top
+=
1
crop_bottom
=
0
for
i
in
reversed
(
range
(
h
)):
if
not
(
mask
[
i
]
==
0
).
all
():
break
crop_bottom
+=
1
# 2. add padding to the crop region
x1
,
y1
,
x2
,
y2
=
(
int
(
max
(
crop_left
-
pad
,
0
)),
int
(
max
(
crop_top
-
pad
,
0
)),
int
(
min
(
w
-
crop_right
+
pad
,
w
)),
int
(
min
(
h
-
crop_bottom
+
pad
,
h
)),
)
# 3. expands crop region to match the aspect ratio of the image to be processed
ratio_crop_region
=
(
x2
-
x1
)
/
(
y2
-
y1
)
ratio_processing
=
width
/
height
if
ratio_crop_region
>
ratio_processing
:
desired_height
=
(
x2
-
x1
)
/
ratio_processing
desired_height_diff
=
int
(
desired_height
-
(
y2
-
y1
))
y1
-=
desired_height_diff
//
2
y2
+=
desired_height_diff
-
desired_height_diff
//
2
if
y2
>=
mask_image
.
height
:
diff
=
y2
-
mask_image
.
height
y2
-=
diff
y1
-=
diff
if
y1
<
0
:
y2
-=
y1
y1
-=
y1
if
y2
>=
mask_image
.
height
:
y2
=
mask_image
.
height
else
:
desired_width
=
(
y2
-
y1
)
*
ratio_processing
desired_width_diff
=
int
(
desired_width
-
(
x2
-
x1
))
x1
-=
desired_width_diff
//
2
x2
+=
desired_width_diff
-
desired_width_diff
//
2
if
x2
>=
mask_image
.
width
:
diff
=
x2
-
mask_image
.
width
x2
-=
diff
x1
-=
diff
if
x1
<
0
:
x2
-=
x1
x1
-=
x1
if
x2
>=
mask_image
.
width
:
x2
=
mask_image
.
width
return
x1
,
y1
,
x2
,
y2
def
_resize_and_fill
(
self
,
image
:
PIL
.
Image
.
Image
,
width
:
int
,
height
:
int
,
)
->
PIL
.
Image
.
Image
:
"""
Resize the image to fit within the specified width and height, maintaining the aspect ratio, and then center
the image within the dimensions, filling empty with data from image.
Args:
image: The image to resize.
width: The width to resize the image to.
height: The height to resize the image to.
"""
ratio
=
width
/
height
src_ratio
=
image
.
width
/
image
.
height
src_w
=
width
if
ratio
<
src_ratio
else
image
.
width
*
height
//
image
.
height
src_h
=
height
if
ratio
>=
src_ratio
else
image
.
height
*
width
//
image
.
width
resized
=
image
.
resize
((
src_w
,
src_h
),
resample
=
PIL_INTERPOLATION
[
"lanczos"
])
res
=
Image
.
new
(
"RGB"
, (
width
,
height
))
res
.
paste
(
resized
,
box
=
(
width
//
2
-
src_w
//
2
,
height
//
2
-
src_h
//
2
))
if
ratio
<
src_ratio
:
fill_height
=
height
//
2
-
src_h
//
2
if
fill_height
>
0
:
res
.
paste
(
resized
.
resize
((
width
,
fill_height
),
box
=
(
0
,
0
,
width
,
0
)),
box
=
(
0
,
0
))
res
.
paste
(
resized
.
resize
((
width
,
fill_height
),
box
=
(
0
,
resized
.
height
,
width
,
resized
.
height
)),
box
=
(
0
,
fill_height
+
src_h
),
)
elif
ratio
>
src_ratio
:
fill_width
=
width
//
2
-
src_w
//
2
if
fill_width
>
0
:
res
.
paste
(
resized
.
resize
((
fill_width
,
height
),
box
=
(
0
,
0
,
0
,
height
)),
box
=
(
0
,
0
))
res
.
paste
(
resized
.
resize
((
fill_width
,
height
),
box
=
(
resized
.
width
,
0
,
resized
.
width
,
height
)),
box
=
(
fill_width
+
src_w
,
0
),
)
return
res
def
_resize_and_crop
(
self
,
image
:
PIL
.
Image
.
Image
,
width
:
int
,
height
:
int
,
)
->
PIL
.
Image
.
Image
:
"""
Resize the image to fit within the specified width and height, maintaining the aspect ratio, and then center
the image within the dimensions, cropping the excess.
Args:
image: The image to resize.
width: The width to resize the image to.
height: The height to resize the image to.
"""
ratio
=
width
/
height
src_ratio
=
image
.
width
/
image
.
height
src_w
=
width
if
ratio
>
src_ratio
else
image
.
width
*
height
//
image
.
height
src_h
=
height
if
ratio
<=
src_ratio
else
image
.
height
*
width
//
image
.
width
resized
=
image
.
resize
((
src_w
,
src_h
),
resample
=
PIL_INTERPOLATION
[
"lanczos"
])
res
=
Image
.
new
(
"RGB"
, (
width
,
height
))
res
.
paste
(
resized
,
box
=
(
width
//
2
-
src_w
//
2
,
height
//
2
-
src_h
//
2
))
return
res
def
resize
(
self
,
image
:
Union
[
PIL
.
Image
.
Image
,
np
.
ndarray
,
torch
.
Tensor
],
height
:
int
,
width
:
int
,
resize_mode
:
str
=
"default"
,
# "default", "fill", "crop"
)
->
Union
[
PIL
.
Image
.
Image
,
np
.
ndarray
,
torch
.
Tensor
]:
"""
Resize image.
Args:
image (`PIL.Image.Image`, `np.ndarray` or `torch.Tensor`):
The image input, can be a PIL image, numpy array or pytorch tensor.
height (`int`):
The height to resize to.
width (`int`):
The width to resize to.
resize_mode (`str`, *optional*, defaults to `default`):
The resize mode to use, can be one of `default` or `fill`. If `default`, will resize the image to fit
within the specified width and height, and it may not maintaining the original aspect ratio. If `fill`,
will resize the image to fit within the specified width and height, maintaining the aspect ratio, and
then center the image within the dimensions, filling empty with data from image. If `crop`, will resize
the image to fit within the specified width and height, maintaining the aspect ratio, and then center
the image within the dimensions, cropping the excess. Note that resize_mode `fill` and `crop` are only
supported for PIL image input.
Returns:
`PIL.Image.Image`, `np.ndarray` or `torch.Tensor`:
The resized image.
"""
if
resize_mode
!=
"default"
and
not
isinstance
(
image
,
PIL
.
Image
.
Image
):
raise
ValueError
(
f"Only PIL image input is supported for resize_mode
{
resize_mode
}
"
)
if
isinstance
(
image
,
PIL
.
Image
.
Image
):
if
resize_mode
==
"default"
:
image
=
image
.
resize
((
width
,
height
),
resample
=
PIL_INTERPOLATION
[
self
.
config
.
resample
])
elif
resize_mode
==
"fill"
:
image
=
self
.
_resize_and_fill
(
image
,
width
,
height
)
elif
resize_mode
==
"crop"
:
image
=
self
.
_resize_and_crop
(
image
,
width
,
height
)
else
:
raise
ValueError
(
f"resize_mode
{
resize_mode
}
is not supported"
)
elif
isinstance
(
image
,
torch
.
Tensor
):
image
=
torch
.
nn
.
functional
.
interpolate
(
image
,
size
=
(
height
,
width
),
)
elif
isinstance
(
image
,
np
.
ndarray
):
image
=
self
.
numpy_to_pt
(
image
)
image
=
torch
.
nn
.
functional
.
interpolate
(
image
,
size
=
(
height
,
width
),
)
image
=
self
.
pt_to_numpy
(
image
)
return
image
def
binarize
(
self
,
image
:
PIL
.
Image
.
Image
)
->
PIL
.
Image
.
Image
:
"""
Create a mask.
Args:
image (`PIL.Image.Image`):
The image input, should be a PIL image.
Returns:
`PIL.Image.Image`:
The binarized image. Values less than 0.5 are set to 0, values greater than 0.5 are set to 1.
"""
image
[
image
<
0.5
]
=
0
image
[
image
>=
0.5
]
=
1
return
image
def
get_default_height_width
(
self
,
image
:
Union
[
PIL
.
Image
.
Image
,
np
.
ndarray
,
torch
.
Tensor
],
height
:
Optional
[
int
]
=
None
,
width
:
Optional
[
int
]
=
None
,
)
->
Tuple
[
int
,
int
]:
"""
This function return the height and width that are downscaled to the next integer multiple of
`vae_scale_factor`.
Args:
image(`PIL.Image.Image`, `np.ndarray` or `torch.Tensor`):
The image input, can be a PIL image, numpy array or pytorch tensor. if it is a numpy array, should have
shape `[batch, height, width]` or `[batch, height, width, channel]` if it is a pytorch tensor, should
have shape `[batch, channel, height, width]`.
height (`int`, *optional*, defaults to `None`):
The height in preprocessed image. If `None`, will use the height of `image` input.
width (`int`, *optional*`, defaults to `None`):
The width in preprocessed. If `None`, will use the width of the `image` input.
"""
if
height
is
None
:
if
isinstance
(
image
,
PIL
.
Image
.
Image
):
height
=
image
.
height
elif
isinstance
(
image
,
torch
.
Tensor
):
height
=
image
.
shape
[
2
]
else
:
height
=
image
.
shape
[
1
]
if
width
is
None
:
if
isinstance
(
image
,
PIL
.
Image
.
Image
):
width
=
image
.
width
elif
isinstance
(
image
,
torch
.
Tensor
):
width
=
image
.
shape
[
3
]
else
:
width
=
image
.
shape
[
2
]
width
,
height
=
(
x
-
x
%
self
.
config
.
vae_scale_factor
for
x
in
(
width
,
height
)
)
# resize to integer multiple of vae_scale_factor
return
height
,
width
def
preprocess
(
self
,
image
:
PipelineImageInput
,
height
:
Optional
[
int
]
=
None
,
width
:
Optional
[
int
]
=
None
,
resize_mode
:
str
=
"default"
,
# "default", "fill", "crop"
crops_coords
:
Optional
[
Tuple
[
int
,
int
,
int
,
int
]]
=
None
,
)
->
torch
.
Tensor
:
"""
Preprocess the image input.
Args:
image (`pipeline_image_input`):
The image input, accepted formats are PIL images, NumPy arrays, PyTorch tensors; Also accept list of
supported formats.
height (`int`, *optional*, defaults to `None`):
The height in preprocessed image. If `None`, will use the `get_default_height_width()` to get default
height.
width (`int`, *optional*`, defaults to `None`):
The width in preprocessed. If `None`, will use get_default_height_width()` to get the default width.
resize_mode (`str`, *optional*, defaults to `default`):
The resize mode, can be one of `default` or `fill`. If `default`, will resize the image to fit within
the specified width and height, and it may not maintaining the original aspect ratio. If `fill`, will
resize the image to fit within the specified width and height, maintaining the aspect ratio, and then
center the image within the dimensions, filling empty with data from image. If `crop`, will resize the
image to fit within the specified width and height, maintaining the aspect ratio, and then center the
image within the dimensions, cropping the excess. Note that resize_mode `fill` and `crop` are only
supported for PIL image input.
crops_coords (`List[Tuple[int, int, int, int]]`, *optional*, defaults to `None`):
The crop coordinates for each image in the batch. If `None`, will not crop the image.
"""
supported_formats
=
(
PIL
.
Image
.
Image
,
np
.
ndarray
,
torch
.
Tensor
)
# Expand the missing dimension for 3-dimensional pytorch tensor or numpy array that represents grayscale image
if
self
.
config
.
do_convert_grayscale
and
isinstance
(
image
, (
torch
.
Tensor
,
np
.
ndarray
))
and
image
.
ndim
==
3
:
if
isinstance
(
image
,
torch
.
Tensor
):
# if image is a pytorch tensor could have 2 possible shapes:
# 1. batch x height x width: we should insert the channel dimension at position 1
# 2. channel x height x width: we should insert batch dimension at position 0,
# however, since both channel and batch dimension has same size 1, it is same to insert at position 1
# for simplicity, we insert a dimension of size 1 at position 1 for both cases
image
=
image
.
unsqueeze
(
1
)
else
:
# if it is a numpy array, it could have 2 possible shapes:
# 1. batch x height x width: insert channel dimension on last position
# 2. height x width x channel: insert batch dimension on first position
if
image
.
shape
[
-
1
]
==
1
:
image
=
np
.
expand_dims
(
image
,
axis
=
0
)
else
:
image
=
np
.
expand_dims
(
image
,
axis
=
-
1
)
if
isinstance
(
image
,
list
)
and
isinstance
(
image
[
0
],
np
.
ndarray
)
and
image
[
0
].
ndim
==
4
:
warnings
.
warn
(
"Passing `image` as a list of 4d np.ndarray is deprecated."
"Please concatenate the list along the batch dimension and pass it as a single 4d np.ndarray"
,
FutureWarning
,
)
image
=
np
.
concatenate
(
image
,
axis
=
0
)
if
isinstance
(
image
,
list
)
and
isinstance
(
image
[
0
],
torch
.
Tensor
)
and
image
[
0
].
ndim
==
4
:
warnings
.
warn
(
"Passing `image` as a list of 4d torch.Tensor is deprecated."
"Please concatenate the list along the batch dimension and pass it as a single 4d torch.Tensor"
,
FutureWarning
,
)
image
=
torch
.
cat
(
image
,
axis
=
0
)
if
not
is_valid_image_imagelist
(
image
):
raise
ValueError
(
f"Input is in incorrect format. Currently, we only support
{
', '
.
join
(
str
(
x
)
for
x
in
supported_formats
)
}
"
)
if
not
isinstance
(
image
,
list
):
image
=
[
image
]
if
isinstance
(
image
[
0
],
PIL
.
Image
.
Image
):
if
crops_coords
is
not
None
:
image
=
[
i
.
crop
(
crops_coords
)
for
i
in
image
]
if
self
.
config
.
do_resize
:
height
,
width
=
self
.
get_default_height_width
(
image
[
0
],
height
,
width
)
image
=
[
self
.
resize
(
i
,
height
,
width
,
resize_mode
=
resize_mode
)
for
i
in
image
]
if
self
.
config
.
do_convert_rgb
:
image
=
[
self
.
convert_to_rgb
(
i
)
for
i
in
image
]
elif
self
.
config
.
do_convert_grayscale
:
image
=
[
self
.
convert_to_grayscale
(
i
)
for
i
in
image
]
image
=
self
.
pil_to_numpy
(
image
)
# to np
image
=
self
.
numpy_to_pt
(
image
)
# to pt
elif
isinstance
(
image
[
0
],
np
.
ndarray
):
image
=
np
.
concatenate
(
image
,
axis
=
0
)
if
image
[
0
].
ndim
==
4
else
np
.
stack
(
image
,
axis
=
0
)
image
=
self
.
numpy_to_pt
(
image
)
height
,
width
=
self
.
get_default_height_width
(
image
,
height
,
width
)
if
self
.
config
.
do_resize
:
image
=
self
.
resize
(
image
,
height
,
width
)
elif
isinstance
(
image
[
0
],
torch
.
Tensor
):
image
=
torch
.
cat
(
image
,
axis
=
0
)
if
image
[
0
].
ndim
==
4
else
torch
.
stack
(
image
,
axis
=
0
)
if
self
.
config
.
do_convert_grayscale
and
image
.
ndim
==
3
:
image
=
image
.
unsqueeze
(
1
)
channel
=
image
.
shape
[
1
]
# don't need any preprocess if the image is latents
if
channel
==
self
.
vae_latent_channels
:
return
image
height
,
width
=
self
.
get_default_height_width
(
image
,
height
,
width
)
if
self
.
config
.
do_resize
:
image
=
self
.
resize
(
image
,
height
,
width
)
# expected range [0,1], normalize to [-1,1]
do_normalize
=
self
.
config
.
do_normalize
if
do_normalize
and
image
.
min
()
<
0
:
warnings
.
warn
(
"Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] "
f"when passing as pytorch tensor or numpy Array. You passed `image` with value range [
{
image
.
min
()
}
,
{
image
.
max
()
}
]"
,
FutureWarning
,
)
do_normalize
=
False
if
do_normalize
:
image
=
self
.
normalize
(
image
)
if
self
.
config
.
do_binarize
:
image
=
self
.
binarize
(
image
)
return
image
def
postprocess
(
self
,
image
:
torch
.
Tensor
,
output_type
:
str
=
"pil"
,
do_denormalize
:
Optional
[
List
[
bool
]]
=
None
,
)
->
Union
[
PIL
.
Image
.
Image
,
np
.
ndarray
,
torch
.
Tensor
]:
"""
Postprocess the image output from tensor to `output_type`.
Args:
image (`torch.Tensor`):
The image input, should be a pytorch tensor with shape `B x C x H x W`.
output_type (`str`, *optional*, defaults to `pil`):
The output type of the image, can be one of `pil`, `np`, `pt`, `latent`.
do_denormalize (`List[bool]`, *optional*, defaults to `None`):
Whether to denormalize the image to [0,1]. If `None`, will use the value of `do_normalize` in the
`VaeImageProcessor` config.
Returns:
`PIL.Image.Image`, `np.ndarray` or `torch.Tensor`:
The postprocessed image.
"""
if
not
isinstance
(
image
,
torch
.
Tensor
):
raise
ValueError
(
f"Input for postprocessing is in incorrect format:
{
type
(
image
)
}
. We only support pytorch tensor"
)
if
output_type
not
in
[
"latent"
,
"pt"
,
"np"
,
"pil"
]:
deprecation_message
=
(
f"the output_type
{
output_type
}
is outdated and has been set to `np`. Please make sure to set it to one of these instead: "
"`pil`, `np`, `pt`, `latent`"
)
deprecate
(
"Unsupported output_type"
,
"1.0.0"
,
deprecation_message
,
standard_warn
=
False
)
output_type
=
"np"
if
output_type
==
"latent"
:
return
image
if
do_denormalize
is
None
:
do_denormalize
=
[
self
.
config
.
do_normalize
]
*
image
.
shape
[
0
]
image
=
torch
.
stack
(
[
self
.
denormalize
(
image
[
i
])
if
do_denormalize
[
i
]
else
image
[
i
]
for
i
in
range
(
image
.
shape
[
0
])]
)
if
output_type
==
"pt"
:
return
image
image
=
self
.
pt_to_numpy
(
image
)
if
output_type
==
"np"
:
return
image
if
output_type
==
"pil"
:
return
self
.
numpy_to_pil
(
image
)
def
apply_overlay
(
self
,
mask
:
PIL
.
Image
.
Image
,
init_image
:
PIL
.
Image
.
Image
,
image
:
PIL
.
Image
.
Image
,
crop_coords
:
Optional
[
Tuple
[
int
,
int
,
int
,
int
]]
=
None
,
)
->
PIL
.
Image
.
Image
:
"""
overlay the inpaint output to the original image
"""
width
,
height
=
image
.
width
,
image
.
height
init_image
=
self
.
resize
(
init_image
,
width
=
width
,
height
=
height
)
mask
=
self
.
resize
(
mask
,
width
=
width
,
height
=
height
)
init_image_masked
=
PIL
.
Image
.
new
(
"RGBa"
, (
width
,
height
))
init_image_masked
.
paste
(
init_image
.
convert
(
"RGBA"
).
convert
(
"RGBa"
),
mask
=
ImageOps
.
invert
(
mask
.
convert
(
"L"
)))
init_image_masked
=
init_image_masked
.
convert
(
"RGBA"
)
if
crop_coords
is
not
None
:
x
,
y
,
x2
,
y2
=
crop_coords
w
=
x2
-
x
h
=
y2
-
y
base_image
=
PIL
.
Image
.
new
(
"RGBA"
, (
width
,
height
))
image
=
self
.
resize
(
image
,
height
=
h
,
width
=
w
,
resize_mode
=
"crop"
)
base_image
.
paste
(
image
, (
x
,
y
))
image
=
base_image
.
convert
(
"RGB"
)
image
=
image
.
convert
(
"RGBA"
)
image
.
alpha_composite
(
init_image_masked
)
image
=
image
.
convert
(
"RGB"
)
return
image
class
VaeImageProcessorLDM3D
(
VaeImageProcessor
):
"""
Image processor for VAE LDM3D.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to downscale the image's (height, width) dimensions to multiples of `vae_scale_factor`.
vae_scale_factor (`int`, *optional*, defaults to `8`):
VAE scale factor. If `do_resize` is `True`, the image is automatically resized to multiples of this factor.
resample (`str`, *optional*, defaults to `lanczos`):
Resampling filter to use when resizing the image.
do_normalize (`bool`, *optional*, defaults to `True`):
Whether to normalize the image to [-1,1].
"""
config_name
=
CONFIG_NAME
@
register_to_config
def
__init__
(
self
,
do_resize
:
bool
=
True
,
vae_scale_factor
:
int
=
8
,
resample
:
str
=
"lanczos"
,
do_normalize
:
bool
=
True
,
):
super
().
__init__
()
@
staticmethod
def
numpy_to_pil
(
images
:
np
.
ndarray
)
->
List
[
PIL
.
Image
.
Image
]:
"""
Convert a NumPy image or a batch of images to a PIL image.
"""
if
images
.
ndim
==
3
:
images
=
images
[
None
, ...]
images
=
(
images
*
255
).
round
().
astype
(
"uint8"
)
if
images
.
shape
[
-
1
]
==
1
:
# special case for grayscale (single channel) images
pil_images
=
[
Image
.
fromarray
(
image
.
squeeze
(),
mode
=
"L"
)
for
image
in
images
]
else
:
pil_images
=
[
Image
.
fromarray
(
image
[:, :, :
3
])
for
image
in
images
]
return
pil_images
@
staticmethod
def
depth_pil_to_numpy
(
images
:
Union
[
List
[
PIL
.
Image
.
Image
],
PIL
.
Image
.
Image
])
->
np
.
ndarray
:
"""
Convert a PIL image or a list of PIL images to NumPy arrays.
"""
if
not
isinstance
(
images
,
list
):
images
=
[
images
]
images
=
[
np
.
array
(
image
).
astype
(
np
.
float32
)
/
(
2
**
16
-
1
)
for
image
in
images
]
images
=
np
.
stack
(
images
,
axis
=
0
)
return
images
@
staticmethod
def
rgblike_to_depthmap
(
image
:
Union
[
np
.
ndarray
,
torch
.
Tensor
])
->
Union
[
np
.
ndarray
,
torch
.
Tensor
]:
"""
Args:
image: RGB-like depth image
Returns: depth map
"""
return
image
[:, :,
1
]
*
2
**
8
+
image
[:, :,
2
]
def
numpy_to_depth
(
self
,
images
:
np
.
ndarray
)
->
List
[
PIL
.
Image
.
Image
]:
"""
Convert a NumPy depth image or a batch of images to a PIL image.
"""
if
images
.
ndim
==
3
:
images
=
images
[
None
, ...]
images_depth
=
images
[:, :, :,
3
:]
if
images
.
shape
[
-
1
]
==
6
:
images_depth
=
(
images_depth
*
255
).
round
().
astype
(
"uint8"
)
pil_images
=
[
Image
.
fromarray
(
self
.
rgblike_to_depthmap
(
image_depth
),
mode
=
"I;16"
)
for
image_depth
in
images_depth
]
elif
images
.
shape
[
-
1
]
==
4
:
images_depth
=
(
images_depth
*
65535.0
).
astype
(
np
.
uint16
)
pil_images
=
[
Image
.
fromarray
(
image_depth
,
mode
=
"I;16"
)
for
image_depth
in
images_depth
]
else
:
raise
Exception
(
"Not supported"
)
return
pil_images
def
postprocess
(
self
,
image
:
torch
.
Tensor
,
output_type
:
str
=
"pil"
,
do_denormalize
:
Optional
[
List
[
bool
]]
=
None
,
)
->
Union
[
PIL
.
Image
.
Image
,
np
.
ndarray
,
torch
.
Tensor
]:
"""
Postprocess the image output from tensor to `output_type`.
Args:
image (`torch.Tensor`):
The image input, should be a pytorch tensor with shape `B x C x H x W`.
output_type (`str`, *optional*, defaults to `pil`):
The output type of the image, can be one of `pil`, `np`, `pt`, `latent`.
do_denormalize (`List[bool]`, *optional*, defaults to `None`):
Whether to denormalize the image to [0,1]. If `None`, will use the value of `do_normalize` in the
`VaeImageProcessor` config.
Returns:
`PIL.Image.Image`, `np.ndarray` or `torch.Tensor`:
The postprocessed image.
"""
if
not
isinstance
(
image
,
torch
.
Tensor
):
raise
ValueError
(
f"Input for postprocessing is in incorrect format:
{
type
(
image
)
}
. We only support pytorch tensor"
)
if
output_type
not
in
[
"latent"
,
"pt"
,
"np"
,
"pil"
]:
deprecation_message
=
(
f"the output_type
{
output_type
}
is outdated and has been set to `np`. Please make sure to set it to one of these instead: "
"`pil`, `np`, `pt`, `latent`"
)
deprecate
(
"Unsupported output_type"
,
"1.0.0"
,
deprecation_message
,
standard_warn
=
False
)
output_type
=
"np"
if
do_denormalize
is
None
:
do_denormalize
=
[
self
.
config
.
do_normalize
]
*
image
.
shape
[
0
]
image
=
torch
.
stack
(
[
self
.
denormalize
(
image
[
i
])
if
do_denormalize
[
i
]
else
image
[
i
]
for
i
in
range
(
image
.
shape
[
0
])]
)
image
=
self
.
pt_to_numpy
(
image
)
if
output_type
==
"np"
:
if
image
.
shape
[
-
1
]
==
6
:
image_depth
=
np
.
stack
([
self
.
rgblike_to_depthmap
(
im
[:, :,
3
:])
for
im
in
image
],
axis
=
0
)
else
:
image_depth
=
image
[:, :, :,
3
:]
return
image
[:, :, :, :
3
],
image_depth
if
output_type
==
"pil"
:
return
self
.
numpy_to_pil
(
image
),
self
.
numpy_to_depth
(
image
)
else
:
raise
Exception
(
f"This type
{
output_type
}
is not supported"
)
def
preprocess
(
self
,
rgb
:
Union
[
torch
.
Tensor
,
PIL
.
Image
.
Image
,
np
.
ndarray
],
depth
:
Union
[
torch
.
Tensor
,
PIL
.
Image
.
Image
,
np
.
ndarray
],
height
:
Optional
[
int
]
=
None
,
width
:
Optional
[
int
]
=
None
,
target_res
:
Optional
[
int
]
=
None
,
)
->
torch
.
Tensor
:
"""
Preprocess the image input. Accepted formats are PIL images, NumPy arrays or PyTorch tensors.
"""
supported_formats
=
(
PIL
.
Image
.
Image
,
np
.
ndarray
,
torch
.
Tensor
)
# Expand the missing dimension for 3-dimensional pytorch tensor or numpy array that represents grayscale image
if
self
.
config
.
do_convert_grayscale
and
isinstance
(
rgb
, (
torch
.
Tensor
,
np
.
ndarray
))
and
rgb
.
ndim
==
3
:
raise
Exception
(
"This is not yet supported"
)
if
isinstance
(
rgb
,
supported_formats
):
rgb
=
[
rgb
]
depth
=
[
depth
]
elif
not
(
isinstance
(
rgb
,
list
)
and
all
(
isinstance
(
i
,
supported_formats
)
for
i
in
rgb
)):
raise
ValueError
(
f"Input is in incorrect format:
{
[
type
(
i
)
for
i
in
rgb
]
}
. Currently, we only support
{
', '
.
join
(
supported_formats
)
}
"
)
if
isinstance
(
rgb
[
0
],
PIL
.
Image
.
Image
):
if
self
.
config
.
do_convert_rgb
:
raise
Exception
(
"This is not yet supported"
)
# rgb = [self.convert_to_rgb(i) for i in rgb]
# depth = [self.convert_to_depth(i) for i in depth] #TODO define convert_to_depth
if
self
.
config
.
do_resize
or
target_res
:
height
,
width
=
self
.
get_default_height_width
(
rgb
[
0
],
height
,
width
)
if
not
target_res
else
target_res
rgb
=
[
self
.
resize
(
i
,
height
,
width
)
for
i
in
rgb
]
depth
=
[
self
.
resize
(
i
,
height
,
width
)
for
i
in
depth
]
rgb
=
self
.
pil_to_numpy
(
rgb
)
# to np
rgb
=
self
.
numpy_to_pt
(
rgb
)
# to pt
depth
=
self
.
depth_pil_to_numpy
(
depth
)
# to np
depth
=
self
.
numpy_to_pt
(
depth
)
# to pt
elif
isinstance
(
rgb
[
0
],
np
.
ndarray
):
rgb
=
np
.
concatenate
(
rgb
,
axis
=
0
)
if
rgb
[
0
].
ndim
==
4
else
np
.
stack
(
rgb
,
axis
=
0
)
rgb
=
self
.
numpy_to_pt
(
rgb
)
height
,
width
=
self
.
get_default_height_width
(
rgb
,
height
,
width
)
if
self
.
config
.
do_resize
:
rgb
=
self
.
resize
(
rgb
,
height
,
width
)
depth
=
np
.
concatenate
(
depth
,
axis
=
0
)
if
rgb
[
0
].
ndim
==
4
else
np
.
stack
(
depth
,
axis
=
0
)
depth
=
self
.
numpy_to_pt
(
depth
)
height
,
width
=
self
.
get_default_height_width
(
depth
,
height
,
width
)
if
self
.
config
.
do_resize
:
depth
=
self
.
resize
(
depth
,
height
,
width
)
elif
isinstance
(
rgb
[
0
],
torch
.
Tensor
):
raise
Exception
(
"This is not yet supported"
)
# rgb = torch.cat(rgb, axis=0) if rgb[0].ndim == 4 else torch.stack(rgb, axis=0)
# if self.config.do_convert_grayscale and rgb.ndim == 3:
# rgb = rgb.unsqueeze(1)
# channel = rgb.shape[1]
# height, width = self.get_default_height_width(rgb, height, width)
# if self.config.do_resize:
# rgb = self.resize(rgb, height, width)
# depth = torch.cat(depth, axis=0) if depth[0].ndim == 4 else torch.stack(depth, axis=0)
# if self.config.do_convert_grayscale and depth.ndim == 3:
# depth = depth.unsqueeze(1)
# channel = depth.shape[1]
# # don't need any preprocess if the image is latents
# if depth == 4:
# return rgb, depth
# height, width = self.get_default_height_width(depth, height, width)
# if self.config.do_resize:
# depth = self.resize(depth, height, width)
# expected range [0,1], normalize to [-1,1]
do_normalize
=
self
.
config
.
do_normalize
if
rgb
.
min
()
<
0
and
do_normalize
:
warnings
.
warn
(
"Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] "
f"when passing as pytorch tensor or numpy Array. You passed `image` with value range [
{
rgb
.
min
()
}
,
{
rgb
.
max
()
}
]"
,
FutureWarning
,
)
do_normalize
=
False
if
do_normalize
:
rgb
=
self
.
normalize
(
rgb
)
depth
=
self
.
normalize
(
depth
)
if
self
.
config
.
do_binarize
:
rgb
=
self
.
binarize
(
rgb
)
depth
=
self
.
binarize
(
depth
)
return
rgb
,
depth
class
IPAdapterMaskProcessor
(
VaeImageProcessor
):
"""
Image processor for IP Adapter image masks.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to downscale the image's (height, width) dimensions to multiples of `vae_scale_factor`.
vae_scale_factor (`int`, *optional*, defaults to `8`):
VAE scale factor. If `do_resize` is `True`, the image is automatically resized to multiples of this factor.
resample (`str`, *optional*, defaults to `lanczos`):
Resampling filter to use when resizing the image.
do_normalize (`bool`, *optional*, defaults to `False`):
Whether to normalize the image to [-1,1].
do_binarize (`bool`, *optional*, defaults to `True`):
Whether to binarize the image to 0/1.
do_convert_grayscale (`bool`, *optional*, defaults to be `True`):
Whether to convert the images to grayscale format.
"""
config_name
=
CONFIG_NAME
@
register_to_config
def
__init__
(
self
,
do_resize
:
bool
=
True
,
vae_scale_factor
:
int
=
8
,
resample
:
str
=
"lanczos"
,
do_normalize
:
bool
=
False
,
do_binarize
:
bool
=
True
,
do_convert_grayscale
:
bool
=
True
,
):
super
().
__init__
(
do_resize
=
do_resize
,
vae_scale_factor
=
vae_scale_factor
,
resample
=
resample
,
do_normalize
=
do_normalize
,
do_binarize
=
do_binarize
,
do_convert_grayscale
=
do_convert_grayscale
,
)
@
staticmethod
def
downsample
(
mask
:
torch
.
Tensor
,
batch_size
:
int
,
num_queries
:
int
,
value_embed_dim
:
int
):
"""
Downsamples the provided mask tensor to match the expected dimensions for scaled dot-product attention. If the
aspect ratio of the mask does not match the aspect ratio of the output image, a warning is issued.
Args:
mask (`torch.Tensor`):
The input mask tensor generated with `IPAdapterMaskProcessor.preprocess()`.
batch_size (`int`):
The batch size.
num_queries (`int`):
The number of queries.
value_embed_dim (`int`):
The dimensionality of the value embeddings.
Returns:
`torch.Tensor`:
The downsampled mask tensor.
"""
o_h
=
mask
.
shape
[
1
]
o_w
=
mask
.
shape
[
2
]
ratio
=
o_w
/
o_h
mask_h
=
int
(
math
.
sqrt
(
num_queries
/
ratio
))
mask_h
=
int
(
mask_h
)
+
int
((
num_queries
%
int
(
mask_h
))
!=
0
)
mask_w
=
num_queries
//
mask_h
mask_downsample
=
F
.
interpolate
(
mask
.
unsqueeze
(
0
),
size
=
(
mask_h
,
mask_w
),
mode
=
"bicubic"
).
squeeze
(
0
)
# Repeat batch_size times
if
mask_downsample
.
shape
[
0
]
<
batch_size
:
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