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# Copyright 2023 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
warnings
from
typing
import
Union
import
numpy
as
np
import
PIL
import
torch
from
PIL
import
Image
from
.
configuration_utils
import
ConfigMixin
,
register_to_config
from
.
utils
import
CONFIG_NAME
,
PIL_INTERPOLATION
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`.
vae_scale_factor (`int`, *optional*, defaults to `8`):
VAE scale factor. If `do_resize` is True, the image will be 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
):
"""
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
numpy_to_pt
(
images
):
"""
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
):
"""
Convert a numpy image to a pytorch tensor
"""
images
=
images
.
cpu
().
permute
(
0
,
2
,
3
,
1
).
float
().
numpy
()
return
images
@
staticmethod
def
normalize
(
images
):
"""
Normalize an image array to [-1,1]
"""
return
2.0
*
images
-
1.0
def
resize
(
self
,
images
:
PIL
.
Image
.
Image
)
->
PIL
.
Image
.
Image
:
"""
Resize a PIL image. Both height and width will be downscaled to the next integer multiple of `vae_scale_factor`
"""
w
,
h
=
images
.
size
w
,
h
=
(
x
-
x
%
self
.
vae_scale_factor
for
x
in
(
w
,
h
))
# resize to integer multiple of vae_scale_factor
images
=
images
.
resize
((
w
,
h
),
resample
=
PIL_INTERPOLATION
[
self
.
resample
])
return
images
def
preprocess
(
self
,
image
:
Union
[
torch
.
FloatTensor
,
PIL
.
Image
.
Image
,
np
.
ndarray
],
)
->
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
)
if
isinstance
(
image
,
supported_formats
):
image
=
[
image
]
elif
not
(
isinstance
(
image
,
list
)
and
all
(
isinstance
(
i
,
supported_formats
)
for
i
in
image
)):
raise
ValueError
(
f"Input is in incorrect format:
{
[
type
(
i
)
for
i
in
image
]
}
. Currently, we only support
{
', '
.
join
(
supported_formats
)
}
"
)
if
isinstance
(
image
[
0
],
PIL
.
Image
.
Image
):
if
self
.
do_resize
:
image
=
[
self
.
resize
(
i
)
for
i
in
image
]
image
=
[
np
.
array
(
i
).
astype
(
np
.
float32
)
/
255.0
for
i
in
image
]
image
=
np
.
stack
(
image
,
axis
=
0
)
# 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
=
image
.
shape
if
self
.
do_resize
and
(
height
%
self
.
vae_scale_factor
!=
0
or
width
%
self
.
vae_scale_factor
!=
0
):
raise
ValueError
(
f"Currently we only support resizing for PIL image - please resize your numpy array to be divisible by
{
self
.
vae_scale_factor
}
"
f"currently the sizes are
{
height
}
and
{
width
}
. You can also pass a PIL image instead to use resize option in VAEImageProcessor"
)
elif
isinstance
(
image
[
0
],
torch
.
Tensor
):
image
=
torch
.
cat
(
image
,
axis
=
0
)
if
image
[
0
].
ndim
==
4
else
torch
.
stack
(
image
,
axis
=
0
)
_
,
_
,
height
,
width
=
image
.
shape
if
self
.
do_resize
and
(
height
%
self
.
vae_scale_factor
!=
0
or
width
%
self
.
vae_scale_factor
!=
0
):
raise
ValueError
(
f"Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by
{
self
.
vae_scale_factor
}
"
f"currently the sizes are
{
height
}
and
{
width
}
. You can also pass a PIL image instead to use resize option in VAEImageProcessor"
)
# expected range [0,1], normalize to [-1,1]
do_normalize
=
self
.
do_normalize
if
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
)
return
image
def
postprocess
(
self
,
image
,
output_type
:
str
=
"pil"
,
):
if
isinstance
(
image
,
torch
.
Tensor
)
and
output_type
==
"pt"
:
return
image
image
=
self
.
pt_to_numpy
(
image
)
if
output_type
==
"np"
:
return
image
elif
output_type
==
"pil"
:
return
self
.
numpy_to_pil
(
image
)
else
:
raise
ValueError
(
f"Unsupported output_type
{
output_type
}
."
)
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