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3db1cbc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 | # Copyright 2026 Black Forest Labs and 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 PIL.Image
from ...image_processor import VaeImageProcessor
class Flux3ImageProcessor(VaeImageProcessor):
r"""
Image processor for FLUX.3: preprocesses the reference images of image-to-image generation and postprocesses the
generated images.
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 `16`):
VAE (spatial) scale factor, including the 2x2 packing of the latents. If `do_resize` is `True`, the image
is automatically resized to multiples of this factor.
vae_latent_channels (`int`, *optional*, defaults to `32`):
VAE latent channels.
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 `True`):
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.
"""
# Copied from diffusers.pipelines.flux2.image_processor.Flux2ImageProcessor.__init__
def __init__(
self,
do_resize: bool = True,
vae_scale_factor: int = 16,
vae_latent_channels: int = 32,
do_normalize: bool = True,
do_binarize: bool = False,
do_convert_rgb: bool = True,
do_convert_grayscale: bool = False,
):
super().__init__(
do_resize=do_resize,
vae_scale_factor=vae_scale_factor,
vae_latent_channels=vae_latent_channels,
do_normalize=do_normalize,
do_binarize=do_binarize,
do_convert_rgb=do_convert_rgb,
do_convert_grayscale=do_convert_grayscale,
)
@staticmethod
# Copied from diffusers.pipelines.flux2.image_processor.Flux2ImageProcessor._resize_to_target_area
def _resize_to_target_area(image: PIL.Image.Image, target_area: int = 1024 * 1024) -> PIL.Image.Image:
image_width, image_height = image.size
scale = math.sqrt(target_area / (image_width * image_height))
width = int(image_width * scale)
height = int(image_height * scale)
return image.resize((width, height), PIL.Image.Resampling.LANCZOS)
@staticmethod
# Copied from diffusers.pipelines.flux2.image_processor.Flux2ImageProcessor._resize_if_exceeds_area with Flux2->Flux3
def _resize_if_exceeds_area(image, target_area=1024 * 1024) -> PIL.Image.Image:
image_width, image_height = image.size
pixel_count = image_width * image_height
if pixel_count <= target_area:
return image
return Flux3ImageProcessor._resize_to_target_area(image, target_area)
# Copied from diffusers.pipelines.flux2.image_processor.Flux2ImageProcessor._resize_and_crop
def _resize_and_crop(
self,
image: PIL.Image.Image,
width: int,
height: int,
) -> PIL.Image.Image:
r"""
center crop the image to the specified width and height.
Args:
image (`PIL.Image.Image`):
The image to resize and crop.
width (`int`):
The width to resize the image to.
height (`int`):
The height to resize the image to.
Returns:
`PIL.Image.Image`:
The resized and cropped image.
"""
image_width, image_height = image.size
left = (image_width - width) // 2
top = (image_height - height) // 2
right = left + width
bottom = top + height
return image.crop((left, top, right, bottom))
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