diff --git a/docs/source/api_ref_modules.rst b/docs/source/api_ref_modules.rst
index ba6297a744..f6e8f93b38 100644
--- a/docs/source/api_ref_modules.rst
+++ b/docs/source/api_ref_modules.rst
@@ -95,3 +95,4 @@ Functions used for preprocessing images.
     transforms.resize_with_pad
     transforms.tile_crop
     transforms.find_supported_resolutions
+    transforms.VisionCrossAttentionMask
diff --git a/tests/torchtune/modules/transforms/test_transforms.py b/tests/torchtune/modules/transforms/test_transforms.py
new file mode 100644
index 0000000000..0436a34ec1
--- /dev/null
+++ b/tests/torchtune/modules/transforms/test_transforms.py
@@ -0,0 +1,80 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import pytest
+import torch
+from torchtune.modules.transforms import VisionCrossAttentionMask
+
+
+IMAGE_TOKEN_ID = 1
+
+
+class TestVisionCrossAttentionMask:
+    @pytest.fixture
+    def num_tiles(self):
+        return 2
+
+    @pytest.fixture
+    def tile_size(self):
+        return 4
+
+    @pytest.fixture
+    def patch_size(self):
+        return 2
+
+    @pytest.fixture
+    def image_num_tokens(self, num_tiles, tile_size, patch_size):
+        return ((tile_size // patch_size) ** 2 + 1) * num_tiles
+
+    @pytest.fixture
+    def tokens(self):
+        # This tests image tokens not at start, consecutive images, and image
+        # with text until end.
+        # text = 2, image = 1
+        return [2, 2, IMAGE_TOKEN_ID, IMAGE_TOKEN_ID, 2, 2, IMAGE_TOKEN_ID, 2, 2]
+
+    @pytest.fixture
+    def images(self, num_tiles, tokens):
+        n_img = len([token_id for token_id in tokens if token_id == IMAGE_TOKEN_ID])
+        n_channels = 3
+        tile_size = 2
+        return [
+            torch.ones(num_tiles, n_channels, tile_size, tile_size)
+            for _ in range(n_img)
+        ]
+
+    @pytest.fixture
+    def cross_attn_mask_transform(self, tile_size, patch_size):
+        # patches per tile = 4
+        return VisionCrossAttentionMask(
+            tile_size=tile_size,
+            patch_size=patch_size,
+            image_token_id=IMAGE_TOKEN_ID,
+        )
+
+    def test_get_image_attention_intervals(self, cross_attn_mask_transform, tokens):
+        actual = cross_attn_mask_transform._get_image_attention_intervals(tokens)
+        expected = [[2, 6], [3, 6], [6, 9]]
+        assert actual == expected
+
+    def test_call(self, cross_attn_mask_transform, tokens, images, image_num_tokens):
+        sample = {"tokens": tokens, "images": images}
+        dummy_kwargs = {"hello": 8}
+        sample.update(dummy_kwargs)
+        actual = cross_attn_mask_transform(sample)
+        expected = [
+            torch.zeros(len(tokens), image_num_tokens, dtype=torch.bool)
+            for _ in range(len(images))
+        ]
+        expected[0][2:6, :] = True
+        expected[1][3:6, :] = True
+        expected[2][6:9, :] = True
+        for i in range(len(images)):
+            torch.testing.assert_close(actual["encoder_mask"][i], expected[i])
+            torch.testing.assert_close(actual["images"][i], images[i])
+
+        assert actual["tokens"] == tokens
+        assert actual["hello"] == dummy_kwargs["hello"]
diff --git a/torchtune/modules/transforms/__init__.py b/torchtune/modules/transforms/__init__.py
index afc203812f..c317e7d7ce 100644
--- a/torchtune/modules/transforms/__init__.py
+++ b/torchtune/modules/transforms/__init__.py
@@ -4,16 +4,21 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
-from .vision_utils.get_canvas_best_fit import (  # noqa
+from torchtune.modules.transforms._transforms import Transform, VisionCrossAttentionMask
+from torchtune.modules.transforms.vision_utils.get_canvas_best_fit import (  # noqa
     find_supported_resolutions,
     get_canvas_best_fit,
 )
-from .vision_utils.resize_with_pad import resize_with_pad  # noqa
-from .vision_utils.tile_crop import tile_crop  # noqa
+from torchtune.modules.transforms.vision_utils.resize_with_pad import (  # noqa
+    resize_with_pad,
+)
+from torchtune.modules.transforms.vision_utils.tile_crop import tile_crop  # noqa
 
 __all__ = [
+    "Transform",
     "get_canvas_best_fit",
     "resize_with_pad",
     "tile_crop",
     "find_supported_resolutions",
+    "VisionCrossAttentionMask",
 ]
diff --git a/torchtune/modules/transforms/_transforms.py b/torchtune/modules/transforms/_transforms.py
new file mode 100644
index 0000000000..68142686fb
--- /dev/null
+++ b/torchtune/modules/transforms/_transforms.py
@@ -0,0 +1,165 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from typing import Any, List, Mapping, Protocol
+
+import torch
+
+
+class Transform(Protocol):
+    """
+    Loose interface for all data and model transforms. Transforms operate at the
+    sample level and perform operations on a sample dict, returning the updated dict.
+    """
+
+    def __call__(self, sample: Mapping[str, Any]) -> Mapping[str, Any]:
+        pass
+
+
+class VisionCrossAttentionMask(Transform):
+    """
+    Computes the cross-attention mask for text + image inputs. Text tokens that
+    participate in cross-attention with an image token will show True in the mask
+    and follow the interleaved structure laid out in Fig. 7 of the Flamingo paper
+    (https://arxiv.org/pdf/2204.14198):
+
+        (1) Text tokens immediately following the image token up until the next image token
+        (2) Consecutive image tokens attend to subsequent text tokens
+
+    ::
+
+             ┌───┐ ┌───┐ ┌───┐ ┌───┐ ┌───┐ ┌───┐ ┌───┐ ┌───┐ ┌───┐ ┌───┐ ┌───┐
+        img1 │ ■ │ │ ■ │ │ ■ │ │ ■ │ │ ■ │ │ ■ │ │   │ │   │ │   │ │   │ │   │
+             └───┘ └───┘ └───┘ └───┘ └───┘ └───┘ └───┘ └───┘ └───┘ └───┘ └───┘
+             ┌───┐ ┌───┐ ┌───┐ ┌───┐ ┌───┐ ┌───┐ ┌───┐ ┌───┐ ┌───┐ ┌───┐ ┌───┐
+        img2 │   │ │ ■ │ │ ■ │ │ ■ │ │ ■ │ │ ■ │ │   │ │   │ │   │ │   │ │   │
+             └───┘ └───┘ └───┘ └───┘ └───┘ └───┘ └───┘ └───┘ └───┘ └───┘ └───┘
+             ┌───┐ ┌───┐ ┌───┐ ┌───┐ ┌───┐ ┌───┐ ┌───┐ ┌───┐ ┌───┐ ┌───┐ ┌───┐
+        img3 │   │ │   │ │   │ │   │ │   │ │   │ │ ■ │ │ ■ │ │ ■ │ │ ■ │ │ ■ │
+             └───┘ └───┘ └───┘ └───┘ └───┘ └───┘ └───┘ └───┘ └───┘ └───┘ └───┘
+            <img1> <img2>These  are   two  dogs. <img3> This   is    a    cat.
+
+
+
+    Resultant mask is constructed per image and is of shape (text_seq_len, image_seq_len),
+    where True indicates that the token outputted from the image encoder attends
+    to the token in the text sequence in cross-attention. A list of these masks
+    are returned with length equal to number of images in the sample.
+
+    Args:
+        tile_size (int): The size of the image tiles from the image transform
+        patch_size (int): The size of each patch. Used to divide the tiles into patches.
+            E.g. for patch_size = 40, a tile of shape (400, 400) will have 10x10 grid of patches
+            with shape (40, 40) each.
+        image_token_id (int): Token ID of the image special token.
+    """
+
+    def __init__(self, tile_size: int, patch_size: int, image_token_id: int):
+        patch_grid_size = tile_size // patch_size
+        self.patches_per_tile = patch_grid_size**2
+        self.image_token_id = image_token_id
+
+    def _get_image_attention_intervals(self, tokens: List[int]) -> List[List[int]]:
+        """
+        Returns a list of lists of the form [start, end) where start is the index
+        of the current image token and end is the index of the next image token, exclusive.
+
+        Args:
+            tokens (List[int]): List of token IDs in the text sequence
+
+        Returns:
+            List[List[int]]: List of lists of the form [start, end) indicating
+                range of positions in text sequence that should attend to the image
+
+        Example:
+            >>> text = "<img1><img2>These are two dogs. <img3>This is a cat."
+            >>> image_token_id = 1
+            >>> tokens = [1, 1, 9673, 527, 1403, 12875, 13, 1, 1115, 374, 264, 8415]
+            >>> transform = VisionCrossAttentionMask(tile_size=400, patch_size=40, image_token_id=1)
+            >>> intervals = transform._get_image_attention_intervals(tokens)
+            >>> print(intervals)
+            [[0, 7], [1, 7], [7, 12]]
+        """
+        end = len(tokens)
+        vision_token_locations = [
+            i for i, token in enumerate(tokens) if token == self.image_token_id
+        ]
+        # Return empty list if there are no images
+        if len(vision_token_locations) == 0:
+            return []
+        # If there is only one image, it will attend to subsequent text until end
+        if len(vision_token_locations) == 1:
+            return [[vision_token_locations[0], end]]
+
+        # Construct intervals from previous image token to next image token
+        vision_masks = [
+            [tok_idx_prev, tok_idx_next]
+            # Offset by one to get consecutive indices
+            for tok_idx_prev, tok_idx_next in zip(
+                vision_token_locations[:-1], vision_token_locations[1:]
+            )
+        ]
+        # Last image will attend to subsequent text until end
+        vision_masks.append([vision_token_locations[-1], end])
+
+        # If there are consecutive vision tokens, they should all attend to the
+        # same subsequent text
+        last_mask_end = vision_masks[-1][1]
+        for vision_mask in vision_masks[::-1]:
+            if vision_mask[0] == vision_mask[1] - 1:
+                vision_mask[1] = last_mask_end
+            last_mask_end = vision_mask[1]
+        return vision_masks
+
+    def __call__(self, sample: Mapping[str, Any]) -> Mapping[str, Any]:
+        """
+        Generates the vision cross-attention mask for the given sample based on
+        the image token locations interleaved in the text sequence.
+
+        Args:
+            sample (Mapping[str, Any]): Sample dict containing the following keys:
+                - tokens (List[int]): List of token IDs in the text sequence. Number of
+                    image token IDs in the sequence must match the number of images.
+                - images (List[torch.Tensor]): List of image Tensors post-tiling of shape
+                    (n_tiles, c, h, w) each.
+
+        Returns:
+            Mapping[str, Any]: updated sample with the following keys:
+                - encoder_mask (List[torch.Tensor]): list of masks with shape (text_seq_len, image_seq_len),
+                    where length of list == number of images in sample
+                - tokens (List[int]): original tokens
+                - images (List[torch.Tensor]): original images
+        """
+        tokens, images = sample["tokens"], sample["images"]
+        # One sample can have multiple images - verify the number of image tokens
+        # is the same
+        n_img = len(images)
+        intervals = self._get_image_attention_intervals(tokens)
+        if len(intervals) != n_img:
+            raise RuntimeError(
+                f"The number of image tokens ({len(intervals)}) does not match the number of images ({n_img})."
+            )
+
+        # Create mask for each individual image based on its number of tokens,
+        # which can vary based on number of tiles since they are not yet tile padded.
+        # The masks are padded and concatenated together in the batch collator
+        text_seq_len = len(tokens)
+        masks = []
+        for image_num, interval in enumerate(intervals):
+            # Identify what part of text sequence should be attended
+            start, end = interval
+            # Compute this image's number of tokens based on num tiles, patches per tile
+            n_tiles = images[image_num].shape[0]
+            image_seq_len = n_tiles * (self.patches_per_tile + 1)  # +1 for CLS token
+            # Mask will be block of 1s at the corresponding interval in the text.
+            # It is not a causal block because all the image tokens correspond
+            # to a single image, so text tokens attend to all the image's tokens
+            mask = torch.zeros(text_seq_len, image_seq_len, dtype=torch.bool)
+            mask[start:end, :] = True
+            masks.append(mask)
+
+        sample.update({"encoder_mask": masks})
+        return sample