Repository: huggingface/transformers
Pull request: Add Fast Image Processor for Flava

# What does this PR do?

<!--
Congratulations! You've made it this far! You're not quite done yet though.

Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution.

Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change.

Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost.
-->

<!-- Remove if not applicable -->

Related #36978


## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
      Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @

 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

Models:

- text models: @ArthurZucker
- vision models: @amyeroberts, @qubvel
- speech models: @eustlb
- graph models: @clefourrier

Library:

- flax: @gante and @Rocketknight1
- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- pipelines: @Rocketknight1
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @zach-huggingface and @SunMarc
- chat templates: @Rocketknight1

Integrations:

- deepspeed: HF Trainer/Accelerate: @SunMarc @zach-huggingface
- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization (bitsandbytes, autogpt): @SunMarc @MekkCyber

Documentation: @stevhliu

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @Rocketknight1
- PyTorch: See Models above and tag the person corresponding to the modality of the example.
- TensorFlow: @Rocketknight1

 -->


Changed files:
filename: docs/source/en/model_doc/flava.md
patch: @@ -72,6 +72,11 @@ This model was contributed by [aps](https://huggingface.co/aps). The original co
 [[autodoc]] FlavaImageProcessor
     - preprocess
 
+## FlavaImageProcessorFast
+
+[[autodoc]] FlavaImageProcessorFast
+    - preprocess
+
 ## FlavaForPreTraining
 
 [[autodoc]] FlavaForPreTraining

filename: src/transformers/models/auto/image_processing_auto.py
patch: @@ -84,7 +84,7 @@
             ("dpt", ("DPTImageProcessor",)),
             ("efficientformer", ("EfficientFormerImageProcessor",)),
             ("efficientnet", ("EfficientNetImageProcessor",)),
-            ("flava", ("FlavaImageProcessor",)),
+            ("flava", ("FlavaImageProcessor", "FlavaImageProcessorFast")),
             ("focalnet", ("BitImageProcessor",)),
             ("fuyu", ("FuyuImageProcessor",)),
             ("gemma3", ("Gemma3ImageProcessor", "Gemma3ImageProcessorFast")),

filename: src/transformers/models/flava/__init__.py
patch: @@ -21,6 +21,7 @@
     from .configuration_flava import *
     from .feature_extraction_flava import *
     from .image_processing_flava import *
+    from .image_processing_flava_fast import *
     from .modeling_flava import *
     from .processing_flava import *
 else:

filename: src/transformers/models/flava/image_processing_flava_fast.py
patch: @@ -0,0 +1,549 @@
+# coding=utf-8
+# Copyright 2025 The HuggingFace Inc. 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.
+"""Fast Image processor class for Flava."""
+
+import math
+import random
+from functools import lru_cache
+from typing import Any, Dict, Iterable, Optional, Tuple, Union
+
+from ...image_processing_utils_fast import (
+    BASE_IMAGE_PROCESSOR_FAST_DOCSTRING,
+    BASE_IMAGE_PROCESSOR_FAST_DOCSTRING_PREPROCESS,
+    BaseImageProcessorFast,
+    BatchFeature,
+    DefaultFastImageProcessorKwargs,
+    get_size_dict,
+)
+from ...image_transforms import ChannelDimension, group_images_by_shape, reorder_images
+from ...image_utils import ImageInput, PILImageResampling, SizeDict
+from ...processing_utils import Unpack
+from ...utils import (
+    TensorType,
+    add_start_docstrings,
+    is_torch_available,
+    is_torchvision_available,
+    is_torchvision_v2_available,
+)
+from .image_processing_flava import (
+    FLAVA_CODEBOOK_MEAN,
+    FLAVA_CODEBOOK_STD,
+    FLAVA_IMAGE_MEAN,
+    FLAVA_IMAGE_STD,
+    LOGIT_LAPLACE_EPS,
+)
+
+
+if is_torch_available():
+    import torch
+
+if is_torchvision_available():
+    from ...image_utils import pil_torch_interpolation_mapping
+
+    if is_torchvision_v2_available():
+        from torchvision.transforms.v2 import functional as F
+    else:
+        from torchvision.transforms import functional as F
+
+
+class FlavaMaskingGenerator:
+    def __init__(
+        self,
+        input_size: Union[int, Tuple[int, int]] = 14,
+        total_mask_patches: int = 75,
+        mask_group_max_patches: Optional[int] = None,
+        mask_group_min_patches: int = 16,
+        mask_group_min_aspect_ratio: Optional[float] = 0.3,
+        mask_group_max_aspect_ratio: float = None,
+    ):
+        if not isinstance(input_size, tuple):
+            input_size = (input_size,) * 2
+        self.height, self.width = input_size
+
+        self.num_patches = self.height * self.width
+        self.total_mask_patches = total_mask_patches
+
+        self.mask_group_min_patches = mask_group_min_patches
+        self.mask_group_max_patches = total_mask_patches if mask_group_max_patches is None else mask_group_max_patches
+
+        mask_group_max_aspect_ratio = mask_group_max_aspect_ratio or 1 / mask_group_min_aspect_ratio
+        self.log_aspect_ratio = (math.log(mask_group_min_aspect_ratio), math.log(mask_group_max_aspect_ratio))
+
+    def __repr__(self):
+        repr_str = "MaskingGenerator(%d, %d -> [%d ~ %d], max = %d, %.3f ~ %.3f)" % (
+            self.height,
+            self.width,
+            self.mask_group_min_patches,
+            self.mask_group_max_patches,
+            self.total_mask_patches,
+            self.log_aspect_ratio[0],
+            self.log_aspect_ratio[1],
+        )
+        return repr_str
+
+    def get_shape(self):
+        return self.height, self.width
+
+    def _mask(self, mask, max_mask_patches):
+        delta = 0
+        for _attempt in range(10):
+            target_area = random.uniform(self.mask_group_min_patches, max_mask_patches)
+            aspect_ratio = math.exp(random.uniform(*self.log_aspect_ratio))
+            height = int(round(math.sqrt(target_area * aspect_ratio)))
+            width = int(round(math.sqrt(target_area / aspect_ratio)))
+            if width < self.width and height < self.height:
+                top = random.randint(0, self.height - height)
+                left = random.randint(0, self.width - width)
+
+                num_masked = mask[top : top + height, left : left + width].sum()
+                # Overlap
+                if 0 < height * width - num_masked <= max_mask_patches:
+                    zeros_pos = mask[top : top + height, left : left + width] == 0
+                    mask[top : top + height, left : left + width][zeros_pos] = 1
+                    delta += zeros_pos.sum()
+
+                if delta > 0:
+                    break
+        return delta
+
+    def __call__(self):
+        mask = torch.zeros(self.get_shape(), dtype=torch.int)
+        mask_count = 0
+        while mask_count < self.total_mask_patches:
+            max_mask_patches = self.total_mask_patches - mask_count
+            max_mask_patches = min(max_mask_patches, self.mask_group_max_patches)
+
+            delta = self._mask(mask, max_mask_patches)
+            if delta == 0:
+                break
+            else:
+                mask_count += delta
+
+        return mask
+
+
+class FlavaFastImageProcessorKwargs(DefaultFastImageProcessorKwargs):
+    # Mask related params
+    return_image_mask: Optional[bool]
+    input_size_patches: Optional[int]
+    total_mask_patches: Optional[int]
+    mask_group_min_patches: Optional[int]
+    mask_group_max_patches: Optional[int]
+    mask_group_min_aspect_ratio: Optional[float]
+    mask_group_max_aspect_ratio: Optional[float]
+    # Codebook related params
+    return_codebook_pixels: Optional[bool]
+    codebook_do_resize: Optional[bool]
+    codebook_size: Optional[bool]
+    codebook_resample: Optional[int]
+    codebook_do_center_crop: Optional[bool]
+    codebook_crop_size: Optional[int]
+    codebook_do_rescale: Optional[bool]
+    codebook_rescale_factor: Optional[Union[int, float]]
+    codebook_do_map_pixels: Optional[bool]
+    codebook_do_normalize: Optional[bool]
+    codebook_image_mean: Optional[Union[float, Iterable[float]]]
+    codebook_image_std: Optional[Union[float, Iterable[float]]]
+
+
+@add_start_docstrings(
+    "Constructs a fast Flava image processor.",
+    BASE_IMAGE_PROCESSOR_FAST_DOCSTRING,
+    """
+        return_image_mask (`bool`, *optional*, defaults to `False`):
+            Whether to return the image mask. Can be overridden by the `return_image_mask` parameter in `preprocess`.
+        input_size_patches (`int`, *optional*, defaults to 14):
+            Number of patches in the image in height and width direction. 14x14 = 196 total patches. Can be overridden
+            by the `input_size_patches` parameter in `preprocess`.
+        total_mask_patches (`int`, *optional*, defaults to 75):
+            Total number of patches that should be masked. Can be overridden by the `total_mask_patches` parameter in
+            `preprocess`.
+        mask_group_min_patches (`int`, *optional*, defaults to 16):
+            Minimum number of patches that should be masked. Can be overridden by the `mask_group_min_patches`
+            parameter in `preprocess`.
+        mask_group_max_patches (`int`, *optional*):
+            Maximum number of patches that should be masked. Can be overridden by the `mask_group_max_patches`
+            parameter in `preprocess`.
+        mask_group_min_aspect_ratio (`float`, *optional*, defaults to 0.3):
+            Minimum aspect ratio of the mask window. Can be overridden by the `mask_group_min_aspect_ratio` parameter
+            in `preprocess`.
+        mask_group_max_aspect_ratio (`float`, *optional*):
+            Maximum aspect ratio of the mask window. Can be overridden by the `mask_group_max_aspect_ratio` parameter
+            in `preprocess`.
+        codebook_do_resize (`bool`, *optional*, defaults to `True`):
+            Whether to resize the input for codebook to a certain. Can be overridden by the `codebook_do_resize`
+            parameter in `preprocess`. `codebook_size`.
+        codebook_size (`Dict[str, int]`, *optional*, defaults to `{"height": 224, "width": 224}`):
+            Resize the input for codebook to the given size. Can be overridden by the `codebook_size` parameter in
+            `preprocess`.
+        codebook_resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.LANCZOS`):
+            Resampling filter to use if resizing the codebook image. Can be overridden by the `codebook_resample`
+            parameter in `preprocess`.
+        codebook_do_center_crop (`bool`, *optional*, defaults to `True`):
+            Whether to crop the input for codebook at the center. If the input size is smaller than
+            `codebook_crop_size` along any edge, the image is padded with 0's and then center cropped. Can be
+            overridden by the `codebook_do_center_crop` parameter in `preprocess`.
+        codebook_crop_size (`Dict[str, int]`, *optional*, defaults to `{"height": 224, "width": 224}`):
+            Desired output size for codebook input when applying center-cropping. Can be overridden by the
+            `codebook_crop_size` parameter in `preprocess`.
+        codebook_do_rescale (`bool`, *optional*, defaults to `True`):
+            Whether to rescale the input for codebook by the specified scale `codebook_rescale_factor`. Can be
+            overridden by the `codebook_do_rescale` parameter in `preprocess`.
+        codebook_rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
+            Defines the scale factor to use if rescaling the codebook image. Can be overridden by the
+            `codebook_rescale_factor` parameter in `preprocess`.
+        codebook_do_map_pixels (`bool`, *optional*, defaults to `True`):
+            Whether to map the pixel values of the codebook input to (1 - 2e)x + e. Can be overridden by the
+            `codebook_do_map_pixels` parameter in `preprocess`.
+        codebook_do_normalize (`bool`, *optional*, defaults to `True`):
+            Whether or not to normalize the input for codebook with `codebook_image_mean` and `codebook_image_std`. Can
+            be overridden by the `codebook_do_normalize` parameter in `preprocess`.
+        codebook_image_mean (`Optional[Union[float, Iterable[float]]]`, *optional*, defaults to `[0, 0, 0]`):
+            The sequence of means for each channel, to be used when normalizing images for codebook. Can be overridden
+            by the `codebook_image_mean` parameter in `preprocess`.
+        codebook_image_std (`Optional[Union[float, Iterable[float]]]`, *optional*, defaults to `[0.5, 0.5, 0.5]`):
+            The sequence of standard deviations for each channel, to be used when normalizing images for codebook. Can
+            be overridden by the `codebook_image_std` parameter in `preprocess`.
+    """,
+)
+class FlavaImageProcessorFast(BaseImageProcessorFast):
+    resample = PILImageResampling.BICUBIC
+    image_mean = FLAVA_IMAGE_MEAN
+    image_std = FLAVA_IMAGE_STD
+    size = {"height": 224, "width": 224}
+    crop_size = {"height": 224, "width": 224}
+    do_resize = True
+    do_center_crop = True
+    do_rescale = True
+    do_normalize = True
+
+    # Mask related params
+    return_image_mask = False
+    input_size_patches = 14
+    total_mask_patches = 75
+    mask_group_min_patches = 16
+    mask_group_max_patches = None
+    mask_group_min_aspect_ratio = 0.3
+    mask_group_max_aspect_ratio = None
+    # Codebook related params
+    return_codebook_pixels = False
+    codebook_do_resize = True
+    codebook_size = {"height": 112, "width": 112}
+    # LANCZOS resample does not support torch Tensor. Use BICUBIC as closest alternative
+    codebook_resample = PILImageResampling.BICUBIC
+    codebook_do_center_crop = True
+    codebook_crop_size = {"height": 112, "width": 112}
+    codebook_do_rescale = True
+    codebook_rescale_factor = 1 / 255
+    codebook_do_map_pixels = True
+    codebook_do_normalize = True
+    codebook_image_mean = FLAVA_CODEBOOK_MEAN
+    codebook_image_std = FLAVA_CODEBOOK_STD
+    valid_kwargs = FlavaFastImageProcessorKwargs
+
+    def __init__(self, **kwargs: Unpack[FlavaFastImageProcessorKwargs]):
+        super().__init__(**kwargs)
+
+    @add_start_docstrings(
+        BASE_IMAGE_PROCESSOR_FAST_DOCSTRING_PREPROCESS,
+        """
+            return_image_mask (`bool`, *optional*, defaults to `False`):
+                Whether to return the image mask. Can be overridden by the `return_image_mask` parameter in `preprocess`.
+            input_size_patches (`int`, *optional*, defaults to 14):
+                Number of patches in the image in height and width direction. 14x14 = 196 total patches. Can be overridden
+                by the `input_size_patches` parameter in `preprocess`.
+            total_mask_patches (`int`, *optional*, defaults to 75):
+                Total number of patches that should be masked. Can be overridden by the `total_mask_patches` parameter in
+                `preprocess`.
+            mask_group_min_patches (`int`, *optional*, defaults to 16):
+                Minimum number of patches that should be masked. Can be overridden by the `mask_group_min_patches`
+                parameter in `preprocess`.
+            mask_group_max_patches (`int`, *optional*):
+                Maximum number of patches that should be masked. Can be overridden by the `mask_group_max_patches`
+                parameter in `preprocess`.
+            mask_group_min_aspect_ratio (`float`, *optional*, defaults to 0.3):
+                Minimum aspect ratio of the mask window. Can be overridden by the `mask_group_min_aspect_ratio` parameter
+                in `preprocess`.
+            mask_group_max_aspect_ratio (`float`, *optional*):
+                Maximum aspect ratio of the mask window. Can be overridden by the `mask_group_max_aspect_ratio` parameter
+                in `preprocess`.
+            codebook_do_resize (`bool`, *optional*, defaults to `True`):
+                Whether to resize the input for codebook to a certain. Can be overridden by the `codebook_do_resize`
+                parameter in `preprocess`. `codebook_size`.
+            codebook_size (`Dict[str, int]`, *optional*, defaults to `{"height": 224, "width": 224}`):
+                Resize the input for codebook to the given size. Can be overridden by the `codebook_size` parameter in
+                `preprocess`.
+            codebook_resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.LANCZOS`):
+                Resampling filter to use if resizing the codebook image. Can be overridden by the `codebook_resample`
+                parameter in `preprocess`.
+            codebook_do_center_crop (`bool`, *optional*, defaults to `True`):
+                Whether to crop the input for codebook at the center. If the input size is smaller than
+                `codebook_crop_size` along any edge, the image is padded with 0's and then center cropped. Can be
+                overridden by the `codebook_do_center_crop` parameter in `preprocess`.
+            codebook_crop_size (`Dict[str, int]`, *optional*, defaults to `{"height": 224, "width": 224}`):
+                Desired output size for codebook input when applying center-cropping. Can be overridden by the
+                `codebook_crop_size` parameter in `preprocess`.
+            codebook_do_rescale (`bool`, *optional*, defaults to `True`):
+                Whether to rescale the input for codebook by the specified scale `codebook_rescale_factor`. Can be
+                overridden by the `codebook_do_rescale` parameter in `preprocess`.
+            codebook_rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
+                Defines the scale factor to use if rescaling the codebook image. Can be overridden by the
+                `codebook_rescale_factor` parameter in `preprocess`.
+            codebook_do_map_pixels (`bool`, *optional*, defaults to `True`):
+                Whether to map the pixel values of the codebook input to (1 - 2e)x + e. Can be overridden by the
+                `codebook_do_map_pixels` parameter in `preprocess`.
+            codebook_do_normalize (`bool`, *optional*, defaults to `True`):
+                Whether or not to normalize the input for codebook with `codebook_image_mean` and `codebook_image_std`. Can
+                be overridden by the `codebook_do_normalize` parameter in `preprocess`.
+            codebook_image_mean (`Optional[Union[float, Iterable[float]]]`, *optional*, defaults to `[0, 0, 0]`):
+                The sequence of means for each channel, to be used when normalizing images for codebook. Can be overridden
+                by the `codebook_image_mean` parameter in `preprocess`.
+            codebook_image_std (`Optional[Union[float, Iterable[float]]]`, *optional*, defaults to `[0.5, 0.5, 0.5]`):
+                The sequence of standard deviations for each channel, to be used when normalizing images for codebook. Can
+                be overridden by the `codebook_image_std` parameter in `preprocess`.
+        """,
+    )
+    def preprocess(self, images: ImageInput, **kwargs: Unpack[DefaultFastImageProcessorKwargs]) -> BatchFeature:
+        return super().preprocess(images, **kwargs)
+
+    @classmethod
+    def from_dict(cls, image_processor_dict: Dict[str, Any], **kwargs):
+        """
+        Overrides the `from_dict` method from the base class to make sure parameters are updated if image processor is
+        created using from_dict and kwargs e.g. `FlavaImageProcessor.from_pretrained(checkpoint, codebook_size=600)`
+        """
+        image_processor_dict = image_processor_dict.copy()
+        if "codebook_size" in kwargs:
+            image_processor_dict["codebook_size"] = kwargs.pop("codebook_size")
+        if "codebook_crop_size" in kwargs:
+            image_processor_dict["codebook_crop_size"] = kwargs.pop("codebook_crop_size")
+        return super().from_dict(image_processor_dict, **kwargs)
+
+    @lru_cache()
+    def masking_generator(
+        self,
+        input_size_patches,
+        total_mask_patches,
+        mask_group_min_patches,
+        mask_group_max_patches,
+        mask_group_min_aspect_ratio,
+        mask_group_max_aspect_ratio,
+    ) -> FlavaMaskingGenerator:
+        return FlavaMaskingGenerator(
+            input_size=input_size_patches,
+            total_mask_patches=total_mask_patches,
+            mask_group_min_patches=mask_group_min_patches,
+            mask_group_max_patches=mask_group_max_patches,
+            mask_group_min_aspect_ratio=mask_group_min_aspect_ratio,
+            mask_group_max_aspect_ratio=mask_group_max_aspect_ratio,
+        )
+
+    def map_pixels(self, image: "torch.Tensor") -> "torch.Tensor":
+        return (1 - 2 * LOGIT_LAPLACE_EPS) * image + LOGIT_LAPLACE_EPS
+
+    def _further_process_kwargs(
+        self,
+        size: Optional[SizeDict] = None,
+        crop_size: Optional[SizeDict] = None,
+        default_to_square: Optional[bool] = None,
+        image_mean: Optional[Union[float, list[float]]] = None,
+        image_std: Optional[Union[float, list[float]]] = None,
+        codebook_size: Optional[SizeDict] = None,
+        codebook_crop_size: Optional[SizeDict] = None,
+        codebook_image_mean: Optional[Union[float, list[float]]] = None,
+        codebook_image_std: Optional[Union[float, list[float]]] = None,
+        codebook_resample: Optional[PILImageResampling] = None,
+        data_format: Optional[ChannelDimension] = None,
+        **kwargs,
+    ) -> dict:
+        """
+        Update kwargs that need further processing before being validated
+        Can be overridden by subclasses to customize the processing of kwargs.
+        """
+        if kwargs is None:
+            kwargs = {}
+        if size is not None:
+            size = SizeDict(**get_size_dict(size=size, default_to_square=default_to_square))
+        if crop_size is not None:
+            crop_size = SizeDict(**get_size_dict(crop_size, param_name="crop_size"))
+        if isinstance(image_mean, list):
+            image_mean = tuple(image_mean)
+        if isinstance(image_std, list):
+            image_std = tuple(image_std)
+        if data_format is None:
+            data_format = ChannelDimension.FIRST
+        if codebook_size is not None:
+            codebook_size = SizeDict(**get_size_dict(size=codebook_size, default_to_square=default_to_square))
+        if codebook_crop_size is not None:
+            codebook_crop_size = SizeDict(**get_size_dict(codebook_crop_size, param_name="codebook_crop_size"))
+        if isinstance(codebook_image_mean, list):
+            codebook_image_mean = tuple(codebook_image_mean)
+        if isinstance(codebook_image_std, list):
+            codebook_image_std = tuple(codebook_image_std)
+
+        kwargs["size"] = size
+        kwargs["crop_size"] = crop_size
+        kwargs["default_to_square"] = default_to_square
+        kwargs["image_mean"] = image_mean
+        kwargs["image_std"] = image_std
+        kwargs["codebook_size"] = codebook_size
+        kwargs["codebook_crop_size"] = codebook_crop_size
+        kwargs["codebook_image_mean"] = codebook_image_mean
+        kwargs["codebook_image_std"] = codebook_image_std
+        kwargs["data_format"] = data_format
+        kwargs["codebook_interpolation"] = (
+            pil_torch_interpolation_mapping[codebook_resample]
+            if isinstance(codebook_resample, (PILImageResampling, int))
+            else codebook_resample
+        )
+
+        return kwargs
+
+    def _preprocess_image(
+        self,
+        images: list["torch.Tensor"],
+        do_resize: bool,
+        size: SizeDict,
+        interpolation: Optional["F.InterpolationMode"],
+        do_center_crop: bool,
+        crop_size: SizeDict,
+        do_rescale: bool,
+        rescale_factor: float,
+        do_normalize: bool,
+        do_map_pixels: bool,
+        image_mean: Optional[Union[float, list[float]]],
+        image_std: Optional[Union[float, list[float]]],
+        return_tensors: Optional[Union[str, TensorType]],
+    ) -> "torch.Tensor":
+        # Group images by size for batched resizing
+        grouped_images, grouped_images_index = group_images_by_shape(images)
+        resized_images_grouped = {}
+        for shape, stacked_images in grouped_images.items():
+            if do_resize:
+                stacked_images = self.resize(image=stacked_images, size=size, interpolation=interpolation)
+            resized_images_grouped[shape] = stacked_images
+        resized_images = reorder_images(resized_images_grouped, grouped_images_index)
+
+        # Group images by size for further processing
+        # Needed in case do_resize is False, or resize returns images with different sizes
+        grouped_images, grouped_images_index = group_images_by_shape(resized_images)
+        processed_images_grouped = {}
+        for shape, stacked_images in grouped_images.items():
+            if do_center_crop:
+                stacked_images = self.center_crop(stacked_images, crop_size)
+            # Fused rescale and normalize
+            stacked_images = self.rescale_and_normalize(
+                stacked_images, do_rescale, rescale_factor, do_normalize, image_mean, image_std
+            )
+            if do_map_pixels:
+                stacked_images = self.map_pixels(image=stacked_images)
+            processed_images_grouped[shape] = stacked_images
+
+        processed_images = reorder_images(processed_images_grouped, grouped_images_index)
+        processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
+
+        return processed_images
+
+    def _preprocess(
+        self,
+        images: list["torch.Tensor"],
+        do_resize: bool,
+        size: SizeDict,
+        interpolation: Optional["F.InterpolationMode"],
+        do_center_crop: bool,
+        crop_size: SizeDict,
+        do_rescale: bool,
+        rescale_factor: float,
+        do_normalize: bool,
+        image_mean: Optional[Union[float, list[float]]],
+        image_std: Optional[Union[float, list[float]]],
+        # Mask related params
+        return_image_mask: Optional[bool],
+        input_size_patches: Optional[int],
+        total_mask_patches: Optional[int],
+        mask_group_min_patches: Optional[int],
+        mask_group_max_patches: Optional[int],
+        mask_group_min_aspect_ratio: Optional[float],
+        mask_group_max_aspect_ratio: Optional[float],
+        # Codebook related params
+        return_codebook_pixels: Optional[bool],
+        codebook_do_resize: Optional[bool],
+        codebook_size: Optional[SizeDict],
+        codebook_interpolation: Optional["F.InterpolationMode"],
+        codebook_do_center_crop: Optional[bool],
+        codebook_crop_size: Optional[SizeDict],
+        codebook_do_rescale: Optional[bool],
+        codebook_rescale_factor: Optional[float],
+        codebook_do_map_pixels: Optional[bool],
+        codebook_do_normalize: Optional[bool],
+        codebook_image_mean: Optional[Union[float, list[float]]],
+        codebook_image_std: Optional[Union[float, list[float]]],
+        return_tensors: Optional[Union[str, TensorType]],
+        **kwargs,
+    ) -> BatchFeature:
+        processed_images = self._preprocess_image(
+            images=images,
+            do_resize=do_resize,
+            size=size,
+            interpolation=interpolation,
+            do_center_crop=do_center_crop,
+            crop_size=crop_size,
+            do_rescale=do_rescale,
+            rescale_factor=rescale_factor,
+            do_normalize=do_normalize,
+            do_map_pixels=False,
+            image_mean=image_mean,
+            image_std=image_std,
+            return_tensors=return_tensors,
+        )
+        data = {
+            "pixel_values": processed_images,
+        }
+
+        if return_codebook_pixels:
+            codebook_processed_images = self._preprocess_image(
+                images=images,
+                do_resize=codebook_do_resize,
+                size=codebook_size,
+                interpolation=codebook_interpolation,
+                do_center_crop=codebook_do_center_crop,
+                crop_size=codebook_crop_size,
+                do_rescale=codebook_do_rescale,
+                rescale_factor=codebook_rescale_factor,
+                do_normalize=codebook_do_normalize,
+                do_map_pixels=codebook_do_map_pixels,
+                image_mean=codebook_image_mean,
+                image_std=codebook_image_std,
+                return_tensors=return_tensors,
+            )
+            data["codebook_pixel_values"] = codebook_processed_images
+
+        if return_image_mask:
+            mask_generator = self.masking_generator(
+                input_size_patches=input_size_patches,
+                total_mask_patches=total_mask_patches,
+                mask_group_min_patches=mask_group_min_patches,
+                mask_group_max_patches=mask_group_max_patches,
+                mask_group_min_aspect_ratio=mask_group_min_aspect_ratio,
+                mask_group_max_aspect_ratio=mask_group_max_aspect_ratio,
+            )
+            masks = [mask_generator() for _ in range(len(images))]
+            masks = torch.stack(masks, dim=0) if return_tensors else masks
+            data["bool_masked_pos"] = masks
+
+        return BatchFeature(data=data, tensor_type=return_tensors)
+
+
+__all__ = ["FlavaImageProcessorFast"]

filename: tests/models/flava/test_image_processing_flava.py
patch: @@ -16,9 +16,11 @@
 import unittest
 
 import numpy as np
+import requests
+from PIL import Image
 
 from transformers.testing_utils import require_torch, require_vision
-from transformers.utils import is_torch_available, is_vision_available
+from transformers.utils import is_torch_available, is_torchvision_available, is_vision_available
 
 from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
 
@@ -30,6 +32,9 @@
     import PIL
 
     from transformers import FlavaImageProcessor
+
+    if is_torchvision_available():
+        from transformers import FlavaImageProcessorFast
     from transformers.image_utils import PILImageResampling
     from transformers.models.flava.image_processing_flava import (
         FLAVA_CODEBOOK_MEAN,
@@ -105,7 +110,8 @@ def __init__(
 
         self.codebook_do_resize = codebook_do_resize
         self.codebook_size = codebook_size
-        self.codebook_resample = codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
+        # LANCZOS resample does not support torch Tensor. Use BICUBIC as closest alternative
+        self.codebook_resample = codebook_resample if codebook_resample is not None else PILImageResampling.BICUBIC
         self.codebook_do_center_crop = codebook_do_center_crop
         self.codebook_crop_size = codebook_crop_size
         self.codebook_do_map_pixels = codebook_do_map_pixels
@@ -171,6 +177,7 @@ def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=F
 @require_vision
 class FlavaImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
     image_processing_class = FlavaImageProcessor if is_vision_available() else None
+    fast_image_processing_class = FlavaImageProcessorFast if is_torchvision_available() else None
     maxDiff = None
 
     def setUp(self):
@@ -182,157 +189,161 @@ def image_processor_dict(self):
         return self.image_processor_tester.prepare_image_processor_dict()
 
     def test_image_processor_properties(self):
-        image_processing = self.image_processing_class(**self.image_processor_dict)
-        self.assertTrue(hasattr(image_processing, "image_mean"))
-        self.assertTrue(hasattr(image_processing, "image_std"))
-        self.assertTrue(hasattr(image_processing, "do_normalize"))
-        self.assertTrue(hasattr(image_processing, "do_resize"))
-        self.assertTrue(hasattr(image_processing, "resample"))
-        self.assertTrue(hasattr(image_processing, "crop_size"))
-        self.assertTrue(hasattr(image_processing, "do_center_crop"))
-        self.assertTrue(hasattr(image_processing, "do_rescale"))
-        self.assertTrue(hasattr(image_processing, "rescale_factor"))
-        self.assertTrue(hasattr(image_processing, "masking_generator"))
-        self.assertTrue(hasattr(image_processing, "codebook_do_resize"))
-        self.assertTrue(hasattr(image_processing, "codebook_size"))
-        self.assertTrue(hasattr(image_processing, "codebook_resample"))
-        self.assertTrue(hasattr(image_processing, "codebook_do_center_crop"))
-        self.assertTrue(hasattr(image_processing, "codebook_crop_size"))
-        self.assertTrue(hasattr(image_processing, "codebook_do_map_pixels"))
-        self.assertTrue(hasattr(image_processing, "codebook_do_normalize"))
-        self.assertTrue(hasattr(image_processing, "codebook_image_mean"))
-        self.assertTrue(hasattr(image_processing, "codebook_image_std"))
+        for image_processing_class in self.image_processor_list:
+            image_processing = image_processing_class(**self.image_processor_dict)
+            self.assertTrue(hasattr(image_processing, "image_mean"))
+            self.assertTrue(hasattr(image_processing, "image_std"))
+            self.assertTrue(hasattr(image_processing, "do_normalize"))
+            self.assertTrue(hasattr(image_processing, "do_resize"))
+            self.assertTrue(hasattr(image_processing, "resample"))
+            self.assertTrue(hasattr(image_processing, "crop_size"))
+            self.assertTrue(hasattr(image_processing, "do_center_crop"))
+            self.assertTrue(hasattr(image_processing, "do_rescale"))
+            self.assertTrue(hasattr(image_processing, "rescale_factor"))
+            self.assertTrue(hasattr(image_processing, "masking_generator"))
+            self.assertTrue(hasattr(image_processing, "codebook_do_resize"))
+            self.assertTrue(hasattr(image_processing, "codebook_size"))
+            self.assertTrue(hasattr(image_processing, "codebook_resample"))
+            self.assertTrue(hasattr(image_processing, "codebook_do_center_crop"))
+            self.assertTrue(hasattr(image_processing, "codebook_crop_size"))
+            self.assertTrue(hasattr(image_processing, "codebook_do_map_pixels"))
+            self.assertTrue(hasattr(image_processing, "codebook_do_normalize"))
+            self.assertTrue(hasattr(image_processing, "codebook_image_mean"))
+            self.assertTrue(hasattr(image_processing, "codebook_image_std"))
 
     def test_image_processor_from_dict_with_kwargs(self):
-        image_processor = self.image_processing_class.from_dict(self.image_processor_dict)
-        self.assertEqual(image_processor.size, {"height": 224, "width": 224})
-        self.assertEqual(image_processor.crop_size, {"height": 224, "width": 224})
-        self.assertEqual(image_processor.codebook_size, {"height": 112, "width": 112})
-        self.assertEqual(image_processor.codebook_crop_size, {"height": 112, "width": 112})
-
-        image_processor = self.image_processing_class.from_dict(
-            self.image_processor_dict, size=42, crop_size=84, codebook_size=33, codebook_crop_size=66
-        )
-        self.assertEqual(image_processor.size, {"height": 42, "width": 42})
-        self.assertEqual(image_processor.crop_size, {"height": 84, "width": 84})
-        self.assertEqual(image_processor.codebook_size, {"height": 33, "width": 33})
-        self.assertEqual(image_processor.codebook_crop_size, {"height": 66, "width": 66})
+        for image_processing_class in self.image_processor_list:
+            image_processor = image_processing_class.from_dict(self.image_processor_dict)
+            self.assertEqual(image_processor.size, {"height": 224, "width": 224})
+            self.assertEqual(image_processor.crop_size, {"height": 224, "width": 224})
+            self.assertEqual(image_processor.codebook_size, {"height": 112, "width": 112})
+            self.assertEqual(image_processor.codebook_crop_size, {"height": 112, "width": 112})
+
+            image_processor = self.image_processing_class.from_dict(
+                self.image_processor_dict, size=42, crop_size=84, codebook_size=33, codebook_crop_size=66
+            )
+            self.assertEqual(image_processor.size, {"height": 42, "width": 42})
+            self.assertEqual(image_processor.crop_size, {"height": 84, "width": 84})
+            self.assertEqual(image_processor.codebook_size, {"height": 33, "width": 33})
+            self.assertEqual(image_processor.codebook_crop_size, {"height": 66, "width": 66})
 
     def test_call_pil(self):
-        # Initialize image_processing
-        image_processing = self.image_processing_class(**self.image_processor_dict)
-        # create random PIL images
-        image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False)
-        for image in image_inputs:
-            self.assertIsInstance(image, PIL.Image.Image)
-
-        # Test not batched input
-        encoded_images = image_processing(image_inputs[0], return_tensors="pt")
-
-        # Test no bool masked pos
-        self.assertFalse("bool_masked_pos" in encoded_images)
-
-        expected_height, expected_width = self.image_processor_tester.get_expected_image_size()
-
-        self.assertEqual(
-            encoded_images.pixel_values.shape,
-            (1, self.image_processor_tester.num_channels, expected_height, expected_width),
-        )
-
-        # Test batched
-        encoded_images = image_processing(image_inputs, return_tensors="pt")
-        expected_height, expected_width = self.image_processor_tester.get_expected_image_size()
-
-        # Test no bool masked pos
-        self.assertFalse("bool_masked_pos" in encoded_images)
-
-        self.assertEqual(
-            encoded_images.pixel_values.shape,
-            (
-                self.image_processor_tester.batch_size,
-                self.image_processor_tester.num_channels,
-                expected_height,
-                expected_width,
-            ),
-        )
+        for image_processing_class in self.image_processor_list:
+            # Initialize image_processing
+            image_processing = image_processing_class(**self.image_processor_dict)
+            # create random PIL images
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False)
+            for image in image_inputs:
+                self.assertIsInstance(image, PIL.Image.Image)
+
+            # Test not batched input
+            encoded_images = image_processing(image_inputs[0], return_tensors="pt")
+
+            # Test no bool masked pos
+            self.assertFalse("bool_masked_pos" in encoded_images)
+
+            expected_height, expected_width = self.image_processor_tester.get_expected_image_size()
+
+            self.assertEqual(
+                encoded_images.pixel_values.shape,
+                (1, self.image_processor_tester.num_channels, expected_height, expected_width),
+            )
+
+            # Test batched
+            encoded_images = image_processing(image_inputs, return_tensors="pt")
+            expected_height, expected_width = self.image_processor_tester.get_expected_image_size()
+
+            # Test no bool masked pos
+            self.assertFalse("bool_masked_pos" in encoded_images)
+
+            self.assertEqual(
+                encoded_images.pixel_values.shape,
+                (
+                    self.image_processor_tester.batch_size,
+                    self.image_processor_tester.num_channels,
+                    expected_height,
+                    expected_width,
+                ),
+            )
 
     def _test_call_framework(self, instance_class, prepare_kwargs):
-        # Initialize image_processing
-        image_processing = self.image_processing_class(**self.image_processor_dict)
-        # create random tensors
-        image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, **prepare_kwargs)
-        for image in image_inputs:
-            self.assertIsInstance(image, instance_class)
-
-        # Test not batched input
-        encoded_images = image_processing(image_inputs[0], return_tensors="pt")
-
-        expected_height, expected_width = self.image_processor_tester.get_expected_image_size()
-        self.assertEqual(
-            encoded_images.pixel_values.shape,
-            (1, self.image_processor_tester.num_channels, expected_height, expected_width),
-        )
-
-        encoded_images = image_processing(image_inputs, return_image_mask=True, return_tensors="pt")
-
-        expected_height, expected_width = self.image_processor_tester.get_expected_image_size()
-        self.assertEqual(
-            encoded_images.pixel_values.shape,
-            (
-                self.image_processor_tester.batch_size,
-                self.image_processor_tester.num_channels,
-                expected_height,
-                expected_width,
-            ),
-        )
-
-        expected_height, expected_width = self.image_processor_tester.get_expected_mask_size()
-        self.assertEqual(
-            encoded_images.bool_masked_pos.shape,
-            (
-                self.image_processor_tester.batch_size,
-                expected_height,
-                expected_width,
-            ),
-        )
-
-        # Test batched
-        encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
-
-        expected_height, expected_width = self.image_processor_tester.get_expected_image_size()
-        self.assertEqual(
-            encoded_images.shape,
-            (
-                self.image_processor_tester.batch_size,
-                self.image_processor_tester.num_channels,
-                expected_height,
-                expected_width,
-            ),
-        )
-
-        # Test masking
-        encoded_images = image_processing(image_inputs, return_image_mask=True, return_tensors="pt")
-
-        expected_height, expected_width = self.image_processor_tester.get_expected_image_size()
-        self.assertEqual(
-            encoded_images.pixel_values.shape,
-            (
-                self.image_processor_tester.batch_size,
-                self.image_processor_tester.num_channels,
-                expected_height,
-                expected_width,
-            ),
-        )
-
-        expected_height, expected_width = self.image_processor_tester.get_expected_mask_size()
-        self.assertEqual(
-            encoded_images.bool_masked_pos.shape,
-            (
-                self.image_processor_tester.batch_size,
-                expected_height,
-                expected_width,
-            ),
-        )
+        for image_processing_class in self.image_processor_list:
+            # Initialize image_processing
+            image_processing = image_processing_class(**self.image_processor_dict)
+            # create random tensors
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, **prepare_kwargs)
+            for image in image_inputs:
+                self.assertIsInstance(image, instance_class)
+
+            # Test not batched input
+            encoded_images = image_processing(image_inputs[0], return_tensors="pt")
+
+            expected_height, expected_width = self.image_processor_tester.get_expected_image_size()
+            self.assertEqual(
+                encoded_images.pixel_values.shape,
+                (1, self.image_processor_tester.num_channels, expected_height, expected_width),
+            )
+
+            encoded_images = image_processing(image_inputs, return_image_mask=True, return_tensors="pt")
+
+            expected_height, expected_width = self.image_processor_tester.get_expected_image_size()
+            self.assertEqual(
+                encoded_images.pixel_values.shape,
+                (
+                    self.image_processor_tester.batch_size,
+                    self.image_processor_tester.num_channels,
+                    expected_height,
+                    expected_width,
+                ),
+            )
+
+            expected_height, expected_width = self.image_processor_tester.get_expected_mask_size()
+            self.assertEqual(
+                encoded_images.bool_masked_pos.shape,
+                (
+                    self.image_processor_tester.batch_size,
+                    expected_height,
+                    expected_width,
+                ),
+            )
+
+            # Test batched
+            encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
+
+            expected_height, expected_width = self.image_processor_tester.get_expected_image_size()
+            self.assertEqual(
+                encoded_images.shape,
+                (
+                    self.image_processor_tester.batch_size,
+                    self.image_processor_tester.num_channels,
+                    expected_height,
+                    expected_width,
+                ),
+            )
+
+            # Test masking
+            encoded_images = image_processing(image_inputs, return_image_mask=True, return_tensors="pt")
+
+            expected_height, expected_width = self.image_processor_tester.get_expected_image_size()
+            self.assertEqual(
+                encoded_images.pixel_values.shape,
+                (
+                    self.image_processor_tester.batch_size,
+                    self.image_processor_tester.num_channels,
+                    expected_height,
+                    expected_width,
+                ),
+            )
+
+            expected_height, expected_width = self.image_processor_tester.get_expected_mask_size()
+            self.assertEqual(
+                encoded_images.bool_masked_pos.shape,
+                (
+                    self.image_processor_tester.batch_size,
+                    expected_height,
+                    expected_width,
+                ),
+            )
 
     def test_call_numpy(self):
         self._test_call_framework(np.ndarray, prepare_kwargs={"numpify": True})
@@ -346,40 +357,76 @@ def test_call_pytorch(self):
         self._test_call_framework(torch.Tensor, prepare_kwargs={"torchify": True})
 
     def test_masking(self):
-        # Initialize image_processing
-        random.seed(1234)
-        image_processing = self.image_processing_class(**self.image_processor_dict)
-        image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
+        for image_processing_class in self.image_processor_list:
+            # Initialize image_processing
+            random.seed(1234)
+            image_processing = image_processing_class(**self.image_processor_dict)
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
 
-        # Test not batched input
-        encoded_images = image_processing(image_inputs[0], return_image_mask=True, return_tensors="pt")
-        self.assertEqual(encoded_images.bool_masked_pos.sum().item(), 75)
+            # Test not batched input
+            encoded_images = image_processing(image_inputs[0], return_image_mask=True, return_tensors="pt")
+            self.assertEqual(encoded_images.bool_masked_pos.sum().item(), 75)
 
     def test_codebook_pixels(self):
-        # Initialize image_processing
-        image_processing = self.image_processing_class(**self.image_processor_dict)
-        # create random PIL images
-        image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False)
-        for image in image_inputs:
-            self.assertIsInstance(image, PIL.Image.Image)
-
-        # Test not batched input
-        encoded_images = image_processing(image_inputs[0], return_codebook_pixels=True, return_tensors="pt")
-        expected_height, expected_width = self.image_processor_tester.get_expected_codebook_image_size()
-        self.assertEqual(
-            encoded_images.codebook_pixel_values.shape,
-            (1, self.image_processor_tester.num_channels, expected_height, expected_width),
+        for image_processing_class in self.image_processor_list:
+            # Initialize image_processing
+            image_processing = image_processing_class(**self.image_processor_dict)
+            # create random PIL images
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False)
+            for image in image_inputs:
+                self.assertIsInstance(image, PIL.Image.Image)
+
+            # Test not batched input
+            encoded_images = image_processing(image_inputs[0], return_codebook_pixels=True, return_tensors="pt")
+            expected_height, expected_width = self.image_processor_tester.get_expected_codebook_image_size()
+            self.assertEqual(
+                encoded_images.codebook_pixel_values.shape,
+                (1, self.image_processor_tester.num_channels, expected_height, expected_width),
+            )
+
+            # Test batched
+            encoded_images = image_processing(image_inputs, return_codebook_pixels=True, return_tensors="pt")
+            expected_height, expected_width = self.image_processor_tester.get_expected_codebook_image_size()
+            self.assertEqual(
+                encoded_images.codebook_pixel_values.shape,
+                (
+                    self.image_processor_tester.batch_size,
+                    self.image_processor_tester.num_channels,
+                    expected_height,
+                    expected_width,
+                ),
+            )
+
+    @require_vision
+    @require_torch
+    def test_slow_fast_equivalence(self):
+        if not self.test_slow_image_processor or not self.test_fast_image_processor:
+            self.skipTest(reason="Skipping slow/fast equivalence test")
+
+        if self.image_processing_class is None or self.fast_image_processing_class is None:
+            self.skipTest(reason="Skipping slow/fast equivalence test as one of the image processors is not defined")
+
+        dummy_image = Image.open(
+            requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw
+        )
+        image_processor_slow = self.image_processing_class(**self.image_processor_dict)
+        image_processor_fast = self.fast_image_processing_class(**self.image_processor_dict)
+
+        encoding_slow = image_processor_slow(
+            dummy_image, return_tensors="pt", return_codebook_pixels=True, return_image_mask=True
+        )
+        encoding_fast = image_processor_fast(
+            dummy_image, return_tensors="pt", return_codebook_pixels=True, return_image_mask=True
+        )
+        self.assertTrue(torch.allclose(encoding_slow.pixel_values, encoding_fast.pixel_values, atol=1e-1))
+        self.assertLessEqual(
+            torch.mean(torch.abs(encoding_slow.pixel_values - encoding_fast.pixel_values)).item(), 1e-3
         )
 
-        # Test batched
-        encoded_images = image_processing(image_inputs, return_codebook_pixels=True, return_tensors="pt")
-        expected_height, expected_width = self.image_processor_tester.get_expected_codebook_image_size()
-        self.assertEqual(
-            encoded_images.codebook_pixel_values.shape,
-            (
-                self.image_processor_tester.batch_size,
-                self.image_processor_tester.num_channels,
-                expected_height,
-                expected_width,
-            ),
+        self.assertTrue(
+            torch.allclose(encoding_slow.codebook_pixel_values, encoding_fast.codebook_pixel_values, atol=1e-1)
+        )
+        self.assertLessEqual(
+            torch.mean(torch.abs(encoding_slow.codebook_pixel_values - encoding_fast.codebook_pixel_values)).item(),
+            1e-3,
         )