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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}.")