diffusers/src/diffusers/image_processor.py at main · dfl/diffusers · GitHub

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# 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 List, Optional, Union

import numpy as np

import PIL.Image

import torch

from PIL import Image

from .configuration_utils import ConfigMixin, register_to_config

from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate

PipelineImageInput = Union[

PIL.Image.Image,

np.ndarray,

torch.FloatTensor,

List[PIL.Image.Image],

List[np.ndarray],

List[torch.FloatTensor],

]

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,

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`",

)

self.config.do_convert_rgb = False

@staticmethod

def numpy_to_pil(images: np.ndarray) -> 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.FloatTensor:

"""

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.FloatTensor) -> 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):

"""

Normalize an image array to [-1,1].

"""

return 2.0 * images - 1.0

@staticmethod

def denormalize(images):

"""

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

def get_default_height_width(

self,

image: [PIL.Image.Image, np.ndarray, torch.Tensor],

height: Optional[int] = None,

width: Optional[int] = None,

"""

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 resize(

self,

image: [PIL.Image.Image, np.ndarray, torch.Tensor],

height: Optional[int] = None,

width: Optional[int] = None,

) -> [PIL.Image.Image, np.ndarray, torch.Tensor]:

"""

Resize image.

"""

if isinstance(image, PIL.Image.Image):

image = image.resize((width, height), resample=PIL_INTERPOLATION[self.config.resample])

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

"""

image[image < 0.5] = 0

image[image >= 0.5] = 1

return image

def preprocess(

self,

image: Union[torch.FloatTensor, PIL.Image.Image, np.ndarray],

height: Optional[int] = None,

width: 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(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. channnel 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, 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.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]

if self.config.do_resize:

height, width = self.get_default_height_width(image[0], height, width)

image = [self.resize(i, height, width) 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 == 4:

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 image.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 [{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.FloatTensor,

output_type: str = "pil",

do_denormalize: Optional[List[bool]] = None,

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)

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):

"""

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 rgblike_to_depthmap(image):

"""

Args:

image: RGB-like depth image

Returns: depth map

"""

return image[:, :, 1] * 2**8 + image[:, :, 2]

def numpy_to_depth(self, images):

"""

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.FloatTensor,

output_type: str = "pil",

do_denormalize: Optional[List[bool]] = None,

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")

diffusers/src/diffusers/image_processor.py at main · dfl/diffusers · GitHub

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