diff --git a/test/expect/ModelTester.test_crestereo_base_expect.pkl b/test/expect/ModelTester.test_crestereo_base_expect.pkl
new file mode 100644
index 00000000000..e5b8cd8f666
Binary files /dev/null and b/test/expect/ModelTester.test_crestereo_base_expect.pkl differ
diff --git a/test/test_prototype_models.py b/test/test_prototype_models.py
index fc71ef17fa9..6d9f22c1543 100644
--- a/test/test_prototype_models.py
+++ b/test/test_prototype_models.py
@@ -5,7 +5,7 @@
 from torchvision.prototype import models
 
 
-@pytest.mark.parametrize("model_fn", TM.list_model_fns(models.depth.stereo))
+@pytest.mark.parametrize("model_fn", (models.depth.stereo.raft_stereo_base,))
 @pytest.mark.parametrize("model_mode", ("standard", "scripted"))
 @pytest.mark.parametrize("dev", cpu_and_gpu())
 def test_raft_stereo(model_fn, model_mode, dev):
@@ -35,4 +35,50 @@ def test_raft_stereo(model_fn, model_mode, dev):
     ), f"The output shape of depth_pred should be [1, 1, 64, 64] but instead it is {preds[0].shape}"
 
     # Test against expected file output
-    TM._assert_expected(depth_pred.cpu(), name=model_fn.__name__, atol=1e-2, rtol=1e-2)
+    TM._assert_expected(depth_pred, name=model_fn.__name__, atol=1e-2, rtol=1e-2)
+
+
+@pytest.mark.parametrize("model_fn", (models.depth.stereo.crestereo_base,))
+@pytest.mark.parametrize("model_mode", ("standard", "scripted"))
+@pytest.mark.parametrize("dev", cpu_and_gpu())
+def test_crestereo(model_fn, model_mode, dev):
+    set_rng_seed(0)
+
+    model = model_fn().eval().to(dev)
+
+    if model_mode == "scripted":
+        model = torch.jit.script(model)
+
+    img1 = torch.rand(1, 3, 64, 64).to(dev)
+    img2 = torch.rand(1, 3, 64, 64).to(dev)
+    iterations = 3
+
+    preds = model(img1, img2, flow_init=None, num_iters=iterations)
+    disparity_pred = preds[-1]
+
+    # all the pyramid levels except the highest res make only half the number of iterations
+    expected_iterations = (iterations // 2) * (len(model.resolutions) - 1)
+    expected_iterations += iterations
+    assert (
+        len(preds) == expected_iterations
+    ), "Number of predictions should be the number of iterations multiplied by the number of pyramid levels"
+
+    assert disparity_pred.shape == torch.Size(
+        [1, 2, 64, 64]
+    ), f"Predicted disparity should have the same spatial shape as the input. Inputs shape {img1.shape[2:]}, Prediction shape {disparity_pred.shape[2:]}"
+
+    assert all(
+        d.shape == torch.Size([1, 2, 64, 64]) for d in preds
+    ), "All predicted disparities are expected to have the same shape"
+
+    # test a backward pass with a dummy loss as well
+    preds = torch.stack(preds, dim=0)
+    targets = torch.ones_like(preds, requires_grad=False)
+    loss = torch.nn.functional.mse_loss(preds, targets)
+
+    try:
+        loss.backward()
+    except Exception as e:
+        assert False, f"Backward pass failed with an unexpected exception: {e.__class__.__name__} {e}"
+
+    TM._assert_expected(disparity_pred, name=model_fn.__name__, atol=1e-2, rtol=1e-2)
diff --git a/torchvision/models/optical_flow/_utils.py b/torchvision/models/optical_flow/_utils.py
index 2b6068b4fbb..fa2454a2731 100644
--- a/torchvision/models/optical_flow/_utils.py
+++ b/torchvision/models/optical_flow/_utils.py
@@ -19,8 +19,9 @@ def grid_sample(img: Tensor, absolute_grid: Tensor, mode: str = "bilinear", alig
     return F.grid_sample(img, normalized_grid, mode=mode, align_corners=align_corners)
 
 
-def make_coords_grid(batch_size: int, h: int, w: int):
-    coords = torch.meshgrid(torch.arange(h), torch.arange(w), indexing="ij")
+def make_coords_grid(batch_size: int, h: int, w: int, device: str = "cpu"):
+    device = torch.device(device)
+    coords = torch.meshgrid(torch.arange(h, device=device), torch.arange(w, device=device), indexing="ij")
     coords = torch.stack(coords[::-1], dim=0).float()
     return coords[None].repeat(batch_size, 1, 1, 1)
 
diff --git a/torchvision/models/optical_flow/raft.py b/torchvision/models/optical_flow/raft.py
index 04076c96032..1773f3d5983 100644
--- a/torchvision/models/optical_flow/raft.py
+++ b/torchvision/models/optical_flow/raft.py
@@ -27,7 +27,7 @@
 class ResidualBlock(nn.Module):
     """Slightly modified Residual block with extra relu and biases."""
 
-    def __init__(self, in_channels, out_channels, *, norm_layer, stride=1):
+    def __init__(self, in_channels, out_channels, *, norm_layer, stride=1, always_project: bool = False):
         super().__init__()
 
         # Note regarding bias=True:
@@ -43,7 +43,10 @@ def __init__(self, in_channels, out_channels, *, norm_layer, stride=1):
             out_channels, out_channels, norm_layer=norm_layer, kernel_size=3, bias=True
         )
 
-        if stride == 1:
+        # make mypy happy
+        self.downsample: nn.Module
+
+        if stride == 1 and not always_project:
             self.downsample = nn.Identity()
         else:
             self.downsample = Conv2dNormActivation(
@@ -144,6 +147,10 @@ def __init__(
                 if m.bias is not None:
                     nn.init.constant_(m.bias, 0)
 
+        num_downsamples = len(list(filter(lambda s: s == 2, strides)))
+        self.output_dim = layers[-1]
+        self.downsample_factor = 2**num_downsamples
+
     def _make_2_blocks(self, block, in_channels, out_channels, norm_layer, first_stride):
         block1 = block(in_channels, out_channels, norm_layer=norm_layer, stride=first_stride)
         block2 = block(out_channels, out_channels, norm_layer=norm_layer, stride=1)
diff --git a/torchvision/prototype/models/depth/stereo/__init__.py b/torchvision/prototype/models/depth/stereo/__init__.py
index 18679026683..cd075ca2b9e 100644
--- a/torchvision/prototype/models/depth/stereo/__init__.py
+++ b/torchvision/prototype/models/depth/stereo/__init__.py
@@ -1 +1,2 @@
 from .raft_stereo import *
+from .crestereo import *
diff --git a/torchvision/prototype/models/depth/stereo/crestereo.py b/torchvision/prototype/models/depth/stereo/crestereo.py
new file mode 100644
index 00000000000..5a7b97a4714
--- /dev/null
+++ b/torchvision/prototype/models/depth/stereo/crestereo.py
@@ -0,0 +1,1095 @@
+import math
+from functools import partial
+from typing import Callable, Dict, Iterable, List, Optional, Tuple
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchvision.models.optical_flow.raft as raft
+from torch import Tensor
+from torchvision.models._api import WeightsEnum
+from torchvision.models.optical_flow._utils import grid_sample, make_coords_grid, upsample_flow
+from torchvision.ops import Conv2dNormActivation
+
+all = (
+    "CREStereo",
+    "CREStereo_Base_Weights",
+    "crestereo_base",
+)
+
+
+class ConvexMaskPredictor(nn.Module):
+    def __init__(
+        self,
+        *,
+        in_channels: int,
+        hidden_size: int,
+        upsample_factor: int,
+        multiplier: float = 0.25,
+    ) -> None:
+
+        super().__init__()
+        self.mask_head = nn.Sequential(
+            Conv2dNormActivation(in_channels, hidden_size, norm_layer=None, kernel_size=3),
+            # https://arxiv.org/pdf/2003.12039.pdf (Annex section B) for the
+            # following convolution output size
+            nn.Conv2d(hidden_size, upsample_factor**2 * 9, 1, padding=0),
+        )
+
+        self.multiplier = multiplier
+
+    def forward(self, x: Tensor) -> Tensor:
+        x = self.mask_head(x) * self.multiplier
+        return x
+
+
+def get_correlation(
+    left_feature: Tensor,
+    right_feature: Tensor,
+    window_size: Tuple[int, int] = (3, 3),
+    dilate: Tuple[int, int] = (1, 1),
+) -> Tensor:
+    """Function that computes a correlation product between the left and right features.
+
+    The correlation is computed in a sliding window fashion, namely the the left features are fixed
+    and for each ``(i, j)`` location we compute the correlation with a sliding window anchored in
+    ``(i, j)`` from the right feature map. The sliding window selects pixels obtained in the range of the sliding
+    window; i.e ``(i - window_size // 2, i + window_size // 2)`` respectively ``(j - window_size // 2, j + window_size // 2)``.
+    """
+
+    B, C, H, W = left_feature.shape
+
+    di_y, di_x = dilate[0], dilate[1]
+    pad_y, pad_x = window_size[0] // 2 * di_y, window_size[1] // 2 * di_x
+
+    right_padded = F.pad(right_feature, (pad_x, pad_x, pad_y, pad_y), mode="replicate")
+    # in order to vectorize the correlation computation over all pixel candidates
+    # we create multiple shifted right images which we stack on an extra dimension
+    right_padded = F.unfold(right_padded, kernel_size=(H, W), dilation=dilate).detach()
+    # torch unfold returns a tensor of shape [B, flattened_values, n_selections]
+    right_padded = right_padded.permute(0, 2, 1)
+    # we consider rehsape back into [B, n_views, C, H, W]
+    right_padded = right_padded.reshape(B, (window_size[0] * window_size[1]), C, H, W)
+    # we expand the left features for broadcasting
+    left_feature = left_feature.unsqueeze(1)
+    # this will compute an element product of between [B, 1, C, H, W] * [B, n_views, C, H, W]
+    # to obtain correlations over the pixel canditates we perform a mean on the C dimension
+    correlation = torch.mean(left_feature * right_padded, dim=2, keepdim=False)
+    # the final correlation tensor shape will be [B, n_views, H, W]
+    # where on the i-th position of the n_views dimension we will have
+    # the correlation value between the left pixel
+    # and the i-th candidate on the right feature map
+    return correlation
+
+
+def _check_window_specs(
+    search_window_1d: Tuple[int, int] = (1, 9),
+    search_dilate_1d: Tuple[int, int] = (1, 1),
+    search_window_2d: Tuple[int, int] = (3, 3),
+    search_dilate_2d: Tuple[int, int] = (1, 1),
+) -> None:
+
+    if not np.prod(search_window_1d) == np.prod(search_window_2d):
+        raise ValueError(
+            f"The 1D and 2D windows should contain the same number of elements. "
+            f"1D shape: {search_window_1d} 2D shape: {search_window_2d}"
+        )
+    if not np.prod(search_window_1d) % 2 == 1:
+        raise ValueError(
+            f"Search windows should contain an odd number of elements in them."
+            f"Window of shape {search_window_1d} has {np.prod(search_window_1d)} elements."
+        )
+    if not any(size == 1 for size in search_window_1d):
+        raise ValueError(f"The 1D search window should have at least one size equal to 1. 1D shape: {search_window_1d}")
+    if any(size == 1 for size in search_window_2d):
+        raise ValueError(
+            f"The 2D search window should have all dimensions greater than 1. 2D shape: {search_window_2d}"
+        )
+    if any(dilate < 1 for dilate in search_dilate_1d):
+        raise ValueError(
+            f"The 1D search dilation should have all elements equal or greater than 1. 1D shape: {search_dilate_1d}"
+        )
+    if any(dilate < 1 for dilate in search_dilate_2d):
+        raise ValueError(
+            f"The 2D search dilation should have all elements equal greater than 1. 2D shape: {search_dilate_2d}"
+        )
+
+
+class IterativeCorrelationLayer(nn.Module):
+    def __init__(
+        self,
+        groups: int = 4,
+        search_window_1d: Tuple[int, int] = (1, 9),
+        search_dilate_1d: Tuple[int, int] = (1, 1),
+        search_window_2d: Tuple[int, int] = (3, 3),
+        search_dilate_2d: Tuple[int, int] = (1, 1),
+    ) -> None:
+
+        super().__init__()
+        _check_window_specs(
+            search_window_1d=search_window_1d,
+            search_dilate_1d=search_dilate_1d,
+            search_window_2d=search_window_2d,
+            search_dilate_2d=search_dilate_2d,
+        )
+        self.search_pixels = np.prod(search_window_1d)
+        self.groups = groups
+
+        # two selection tables for dealing withh the small_patch argument in the forward function
+        self.patch_sizes = {
+            "2d": [search_window_2d for _ in range(self.groups)],
+            "1d": [search_window_1d for _ in range(self.groups)],
+        }
+
+        self.dilate_sizes = {
+            "2d": [search_dilate_2d for _ in range(self.groups)],
+            "1d": [search_dilate_1d for _ in range(self.groups)],
+        }
+
+    def forward(self, left_feature: Tensor, right_feature: Tensor, flow: Tensor, window_type: str = "1d") -> Tensor:
+        """Function that computes 1 pass of non-offsetted Group-Wise correlation"""
+        coords = make_coords_grid(
+            left_feature.shape[0], left_feature.shape[2], left_feature.shape[3], device=str(left_feature.device)
+        )
+
+        # we offset the coordinate grid in the flow direction
+        coords = coords + flow
+        coords = coords.permute(0, 2, 3, 1)
+        # resample right features according to off-setted grid
+        right_feature = grid_sample(right_feature, coords, mode="bilinear", align_corners=True)
+
+        # use_small_patch is a flag by which we decide on how many axes
+        # we perform candidate search. See section 3.1 ``Deformable search window`` & Figure 4 in the paper.
+        patch_size_list = self.patch_sizes[window_type]
+        dilate_size_list = self.dilate_sizes[window_type]
+
+        # chunking the left and right feature to perform group-wise correlation
+        # mechanism simillar to GroupNorm. See section 3.1 ``Group-wise correlation``.
+        left_groups = torch.chunk(left_feature, self.groups, dim=1)
+        right_groups = torch.chunk(right_feature, self.groups, dim=1)
+
+        correlations = []
+        # this boils down to rather than performing the correlation product
+        # over the entire C dimensions, we use subsets of C to get multiple correlation sets
+        for i in range(len(patch_size_list)):
+            correlation = get_correlation(left_groups[i], right_groups[i], patch_size_list[i], dilate_size_list[i])
+            correlations.append(correlation)
+        final_correlations = torch.cat(correlations, dim=1)
+        return final_correlations
+
+
+class AttentionOffsetCorrelationLayer(nn.Module):
+    def __init__(
+        self,
+        groups: int = 4,
+        attention_module: Optional[nn.Module] = None,
+        search_window_1d: Tuple[int, int] = (1, 9),
+        search_dilate_1d: Tuple[int, int] = (1, 1),
+        search_window_2d: Tuple[int, int] = (3, 3),
+        search_dilate_2d: Tuple[int, int] = (1, 1),
+    ) -> None:
+        super().__init__()
+        _check_window_specs(
+            search_window_1d=search_window_1d,
+            search_dilate_1d=search_dilate_1d,
+            search_window_2d=search_window_2d,
+            search_dilate_2d=search_dilate_2d,
+        )
+        # convert to python scalar
+        self.search_pixels = int(np.prod(search_window_1d))
+        self.groups = groups
+
+        # two selection tables for dealing withh the small_patch argument in the forward function
+        self.patch_sizes = {
+            "2d": [search_window_2d for _ in range(self.groups)],
+            "1d": [search_window_1d for _ in range(self.groups)],
+        }
+
+        self.dilate_sizes = {
+            "2d": [search_dilate_2d for _ in range(self.groups)],
+            "1d": [search_dilate_1d for _ in range(self.groups)],
+        }
+
+        self.attention_module = attention_module
+
+    def forward(
+        self,
+        left_feature: Tensor,
+        right_feature: Tensor,
+        flow: Tensor,
+        extra_offset: Tensor,
+        window_type: str = "1d",
+    ) -> Tensor:
+        """Function that computes 1 pass of offsetted Group-Wise correlation
+
+        If the class was provided with an attention layer, the left and right feature maps
+        will be passed through a transformer first
+        """
+        B, C, H, W = left_feature.shape
+
+        if self.attention_module is not None:
+            # prepare for transformer required input shapes
+            left_feature = left_feature.permute(0, 2, 3, 1).reshape(B, H * W, C)
+            right_feature = right_feature.permute(0, 2, 3, 1).reshape(B, H * W, C)
+            # this can be either self attention or cross attention, hence the tupple return
+            left_feature, right_feature = self.attention_module(left_feature, right_feature)
+            left_feature = left_feature.reshape(B, H, W, C).permute(0, 3, 1, 2)
+            right_feature = right_feature.reshape(B, H, W, C).permute(0, 3, 1, 2)
+
+        left_groups = torch.chunk(left_feature, self.groups, dim=1)
+        right_groups = torch.chunk(right_feature, self.groups, dim=1)
+
+        num_search_candidates = self.search_pixels
+        # for each pixel (i, j) we have a number of search candidates
+        # thus, for each candidate we should have an X-axis and Y-axis offset value
+        extra_offset = extra_offset.reshape(B, num_search_candidates, 2, H, W).permute(0, 1, 3, 4, 2)
+
+        patch_size_list = self.patch_sizes[window_type]
+        dilate_size_list = self.dilate_sizes[window_type]
+
+        group_channels = C // self.groups
+        correlations = []
+
+        for i in range(len(patch_size_list)):
+            left_group, right_group = left_groups[i], right_groups[i]
+            patch_size, dilate = patch_size_list[i], dilate_size_list[i]
+
+            di_y, di_x = dilate
+            ps_y, ps_x = patch_size
+            # define the search based on the window patch shape
+            ry, rx = ps_y // 2 * di_y, ps_x // 2 * di_x
+
+            # base offsets for search (i.e. where to look on the search index)
+            x_grid, y_grid = torch.meshgrid(
+                torch.arange(-rx, rx + 1, di_x), torch.arange(-ry, ry + 1, di_y), indexing="xy"
+            )
+            x_grid, y_grid = x_grid.to(flow.device), y_grid.to(flow.device)
+            offsets = torch.stack((x_grid, y_grid))
+            offsets = offsets.reshape(2, -1).permute(1, 0)
+
+            for d in (0, 2, 3):
+                offsets = offsets.unsqueeze(d)
+            # extra offsets for search (i.e. deformed search indexes. Simillar concept to deformable convolutions)
+            offsets = offsets + extra_offset
+
+            coords = (
+                make_coords_grid(
+                    left_feature.shape[0], left_feature.shape[2], left_feature.shape[3], device=str(left_feature.device)
+                )
+                + flow
+            )
+            coords = coords.permute(0, 2, 3, 1).unsqueeze(1)
+            coords = coords + offsets
+            coords = coords.reshape(B, -1, W, 2)
+
+            right_group = grid_sample(right_group, coords, mode="bilinear", align_corners=True)
+            # we do not need to perform any window shifting because the grid sample op
+            # will return a multi-view right based on the num_search_candidates dimension in the offsets
+            right_group = right_group.reshape(B, group_channels, -1, H, W)
+            left_group = left_group.reshape(B, group_channels, -1, H, W)
+            correlation = torch.mean(left_group * right_group, dim=1)
+            correlations.append(correlation)
+
+        final_correlation = torch.cat(correlations, dim=1)
+        return final_correlation
+
+
+class AdaptiveGroupCorrelationLayer(nn.Module):
+    """
+    Container for computing various correlation types between a left and right feature map.
+    This module does not contain any optimisable parameters, it's solely a collection of ops.
+    We wrap in a nn.Module for torch.jit.script compatibility
+
+    Adaptive Group Correlation operations from: https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Practical_Stereo_Matching_via_Cascaded_Recurrent_Network_With_Adaptive_Correlation_CVPR_2022_paper.pdf
+
+    Canonical reference implementation: https://github.com/megvii-research/CREStereo/blob/master/nets/corr.py
+    """
+
+    def __init__(
+        self,
+        iterative_correlation_layer: IterativeCorrelationLayer,
+        attention_offset_correlation_layer: AttentionOffsetCorrelationLayer,
+    ) -> None:
+        super().__init__()
+
+        self.iterative_correlation_layer = iterative_correlation_layer
+        self.attention_offset_correlation_layer = attention_offset_correlation_layer
+
+    def forward(
+        self,
+        left_features: Tensor,
+        right_features: Tensor,
+        flow: torch.Tensor,
+        extra_offset: Optional[Tensor],
+        window_type: str = "1d",
+        iter_mode: bool = False,
+    ) -> Tensor:
+        if iter_mode or extra_offset is None:
+            corr = self.iterative_correlation_layer(left_features, right_features, flow, window_type)
+        else:
+            corr = self.attention_offset_correlation_layer(
+                left_features, right_features, flow, extra_offset, window_type
+            )  # type: ignore
+        return corr
+
+
+def elu_feature_map(x: Tensor) -> Tensor:
+    """Elu feature map operation from: https://arxiv.org/pdf/2006.16236.pdf"""
+    return F.elu(x) + 1
+
+
+class LinearAttention(nn.Module):
+    """
+    Linear attention operation from: https://arxiv.org/pdf/2006.16236.pdf
+    Cannonical implementation reference: https://github.com/idiap/fast-transformers/blob/master/fast_transformers/attention/linear_attention.py
+    LoFTR implementation reference: https://github.com/zju3dv/LoFTR/blob/2122156015b61fbb650e28b58a958e4d632b1058/src/loftr/loftr_module/linear_attention.py
+    """
+
+    def __init__(self, eps: float = 1e-6, feature_map_fn: Callable[[Tensor], Tensor] = elu_feature_map) -> None:
+        super().__init__()
+        self.eps = eps
+        self.feature_map_fn = feature_map_fn
+
+    def forward(
+        self,
+        queries: Tensor,
+        keys: Tensor,
+        values: Tensor,
+        q_mask: Optional[Tensor] = None,
+        kv_mask: Optional[Tensor] = None,
+    ) -> Tensor:
+        """
+        Args:
+            queries (torch.Tensor): [N, S1, H, D]
+            keys (torch.Tensor): [N, S2, H, D]
+            values (torch.Tensor): [N, S2, H, D]
+            q_mask (torch.Tensor): [N, S1] (optional)
+            kv_mask (torch.Tensor): [N, S2] (optional)
+        Returns:
+            queried_values (torch.Tensor): [N, S1, H, D]
+        """
+        queries = self.feature_map_fn(queries)
+        keys = self.feature_map_fn(keys)
+
+        if q_mask is not None:
+            queries = queries * q_mask[:, :, None, None]
+        if kv_mask is not None:
+            keys = keys * kv_mask[:, :, None, None]
+            values = values * kv_mask[:, :, None, None]
+
+        # mitigates fp16 overflows
+        values_length = values.shape[1]
+        values = values / values_length
+        kv = torch.einsum("NSHD, NSHV -> NHDV", keys, values)
+        z = 1 / (torch.einsum("NLHD, NHD -> NLH", queries, keys.sum(dim=1)) + self.eps)
+        # rescale at the end to account for fp16 mitigation
+        queried_values = torch.einsum("NLHD, NHDV, NLH -> NLHV", queries, kv, z) * values_length
+        return queried_values
+
+
+class SoftmaxAttention(nn.Module):
+    """
+    A simple softmax attention  operation
+    LoFTR implementation reference: https://github.com/zju3dv/LoFTR/blob/2122156015b61fbb650e28b58a958e4d632b1058/src/loftr/loftr_module/linear_attention.py
+    """
+
+    def __init__(self, dropout: float = 0.0) -> None:
+        super().__init__()
+        self.dropout = nn.Dropout(dropout) if dropout else nn.Identity()
+
+    def forward(
+        self,
+        queries: Tensor,
+        keys: Tensor,
+        values: Tensor,
+        q_mask: Optional[Tensor] = None,
+        kv_mask: Optional[Tensor] = None,
+    ) -> Tensor:
+        """
+        Computes classical softmax full-attention between all queries and keys.
+
+        Args:
+            queries (torch.Tensor): [N, S1, H, D]
+            keys (torch.Tensor): [N, S2, H, D]
+            values (torch.Tensor): [N, S2, H, D]
+            q_mask (torch.Tensor): [N, S1] (optional)
+            kv_mask (torch.Tensor): [N, S2] (optional)
+        Returns:
+            queried_values: [N, S1, H, D]
+        """
+
+        scale_factor = 1.0 / queries.shape[3] ** 0.5  # irsqrt(D) scaling
+        queries = queries * scale_factor
+
+        qk = torch.einsum("NLHD, NSHD -> NLSH", queries, keys)
+        if kv_mask is not None and q_mask is not None:
+            qk.masked_fill_(~(q_mask[:, :, None, None] * kv_mask[:, None, :, None]), float("-inf"))
+
+        attention = torch.softmax(qk, dim=2)
+        attention = self.dropout(attention)
+
+        queried_values = torch.einsum("NLSH, NSHD -> NLHD", attention, values)
+        return queried_values
+
+
+class PositionalEncodingSine(nn.Module):
+    """
+    Sinusoidal positonal encodings
+
+    Using the scaling term from https://github.com/megvii-research/CREStereo/blob/master/nets/attention/position_encoding.py
+    Reference implementation from https://github.com/facebookresearch/detr/blob/8a144f83a287f4d3fece4acdf073f387c5af387d/models/position_encoding.py#L28-L48
+    """
+
+    def __init__(self, dim_model: int, max_size: int = 256) -> None:
+        super().__init__()
+        self.dim_model = dim_model
+        self.max_size = max_size
+        # pre-registered for memory efficiency during forward pass
+        pe = self._make_pe_of_size(self.max_size)
+        self.register_buffer("pe", pe)
+
+    def _make_pe_of_size(self, size: int) -> Tensor:
+        pe = torch.zeros((self.dim_model, *(size, size)), dtype=torch.float32)
+        y_positions = torch.ones((size, size)).cumsum(0).float().unsqueeze(0)
+        x_positions = torch.ones((size, size)).cumsum(1).float().unsqueeze(0)
+        div_term = torch.exp(torch.arange(0.0, self.dim_model // 2, 2) * (-math.log(10000.0) / self.dim_model // 2))
+        div_term = div_term[:, None, None]
+        pe[0::4, :, :] = torch.sin(x_positions * div_term)
+        pe[1::4, :, :] = torch.cos(x_positions * div_term)
+        pe[2::4, :, :] = torch.sin(y_positions * div_term)
+        pe[3::4, :, :] = torch.cos(y_positions * div_term)
+        pe = pe.unsqueeze(0)
+        return pe
+
+    def forward(self, x: Tensor) -> Tensor:
+        """
+        Args:
+            x: [B, C, H, W]
+
+        Returns:
+            x: [B, C, H, W]
+        """
+        torch._assert(
+            len(x.shape) == 4,
+            f"PositionalEncodingSine requires a 4-D dimensional input. Provided tensor is of shape {x.shape}",
+        )
+
+        B, C, H, W = x.shape
+        return x + self.pe[:, :, :H, :W]  # type: ignore
+
+
+class LocalFeatureEncoderLayer(nn.Module):
+    """
+    LoFTR transformer module from: https://arxiv.org/pdf/2104.00680.pdf
+    Cannonical implementations at: https://github.com/zju3dv/LoFTR/blob/master/src/loftr/loftr_module/transformer.py
+    """
+
+    def __init__(
+        self,
+        *,
+        dim_model: int,
+        num_heads: int,
+        attention_module: Callable[..., nn.Module] = LinearAttention,
+    ) -> None:
+        super().__init__()
+
+        self.attention_op = attention_module()
+
+        if not isinstance(self.attention_op, (LinearAttention, SoftmaxAttention)):
+            raise ValueError(
+                f"attention_module must be an instance of LinearAttention or SoftmaxAttention. Got {type(self.attention_op)}"
+            )
+
+        self.dim_head = dim_model // num_heads
+        self.num_heads = num_heads
+
+        # multi-head attention
+        self.query_proj = nn.Linear(dim_model, dim_model, bias=False)
+        self.key_proj = nn.Linear(dim_model, dim_model, bias=False)
+        self.value_proj = nn.Linear(dim_model, dim_model, bias=False)
+        self.merge = nn.Linear(dim_model, dim_model, bias=False)
+
+        # feed forward network
+        self.ffn = nn.Sequential(
+            nn.Linear(dim_model * 2, dim_model * 2, bias=False),
+            nn.ReLU(),
+            nn.Linear(dim_model * 2, dim_model, bias=False),
+        )
+
+        # norm layers
+        self.attention_norm = nn.LayerNorm(dim_model)
+        self.ffn_norm = nn.LayerNorm(dim_model)
+
+    def forward(
+        self, x: Tensor, source: Tensor, x_mask: Optional[Tensor] = None, source_mask: Optional[Tensor] = None
+    ) -> Tensor:
+        """
+        Args:
+            x (torch.Tensor): [B, S1, D]
+            source (torch.Tensor): [B, S2, D]
+            x_mask (torch.Tensor): [B, S1] (optional)
+            source_mask (torch.Tensor): [B, S2] (optional)
+        """
+        B, S, D = x.shape
+        queries, keys, values = x, source, source
+
+        queries = self.query_proj(queries).reshape(B, S, self.num_heads, self.dim_head)
+        keys = self.key_proj(keys).reshape(B, S, self.num_heads, self.dim_head)
+        values = self.value_proj(values).reshape(B, S, self.num_heads, self.dim_head)
+
+        # attention operation
+        message = self.attention_op(queries, keys, values, x_mask, source_mask)
+        # concatenating attention heads together before passing throught projection layer
+        message = self.merge(message.reshape(B, S, D))
+        message = self.attention_norm(message)
+
+        # ffn operation
+        message = self.ffn(torch.cat([x, message], dim=2))
+        message = self.ffn_norm(message)
+
+        return x + message
+
+
+class LocalFeatureTransformer(nn.Module):
+    """
+    LoFTR transformer module from: https://arxiv.org/pdf/2104.00680.pdf
+    Cannonical implementations at: https://github.com/zju3dv/LoFTR/blob/master/src/loftr/loftr_module/transformer.py
+    """
+
+    def __init__(
+        self,
+        *,
+        dim_model: int,
+        num_heads: int,
+        attention_directions: List[str],
+        attention_module: Callable[..., nn.Module] = LinearAttention,
+    ) -> None:
+        super(LocalFeatureTransformer, self).__init__()
+
+        self.attention_module = attention_module
+        self.attention_directions = attention_directions
+        for direction in attention_directions:
+            if direction not in ["self", "cross"]:
+                raise ValueError(
+                    f"Attention direction {direction} unsupported. LocalFeatureTransformer accepts only ``attention_type`` in ``[self, cross]``."
+                )
+
+        self.layers = nn.ModuleList(
+            [
+                LocalFeatureEncoderLayer(dim_model=dim_model, num_heads=num_heads, attention_module=attention_module)
+                for _ in attention_directions
+            ]
+        )
+
+    def forward(
+        self,
+        left_features: Tensor,
+        right_features: Tensor,
+        left_mask: Optional[Tensor] = None,
+        right_mask: Optional[Tensor] = None,
+    ) -> Tuple[Tensor, Tensor]:
+        """
+        Args:
+            left_features (torch.Tensor): [N, S1, D]
+            right_features (torch.Tensor): [N, S2, D]
+            left_mask (torch.Tensor): [N, S1] (optional)
+            right_mask (torch.Tensor): [N, S2] (optional)
+        Returns:
+            left_features (torch.Tensor): [N, S1, D]
+            right_features (torch.Tensor): [N, S2, D]
+        """
+
+        torch._assert(
+            left_features.shape[2] == right_features.shape[2],
+            f"left_features and right_features should have the same embedding dimensions. left_features: {left_features.shape[2]} right_features: {right_features.shape[2]}",
+        )
+
+        for idx, layer in enumerate(self.layers):
+            attention_direction = self.attention_directions[idx]
+
+            if attention_direction == "self":
+                left_features = layer(left_features, left_features, left_mask, left_mask)
+                right_features = layer(right_features, right_features, right_mask, right_mask)
+
+            elif attention_direction == "cross":
+                left_features = layer(left_features, right_features, left_mask, right_mask)
+                right_features = layer(right_features, left_features, right_mask, left_mask)
+
+        return left_features, right_features
+
+
+class PyramidDownsample(nn.Module):
+    """
+    A simple wrapper that return and Avg Pool feature pyramid based on the provided scales.
+    Implicitly returns the input as well.
+    """
+
+    def __init__(self, factors: Iterable[int]) -> None:
+        super().__init__()
+        self.factors = factors
+
+    def forward(self, x: torch.Tensor) -> List[Tensor]:
+        results = [x]
+        for factor in self.factors:
+            results.append(F.avg_pool2d(x, kernel_size=factor, stride=factor))
+        return results
+
+
+class CREStereo(nn.Module):
+    """
+    Implements CREStereo from the `"Practical Stereo Matching via Cascaded Recurrent Network
+    With Adaptive Correlation" <https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Practical_Stereo_Matching_via_Cascaded_Recurrent_Network_With_Adaptive_Correlation_CVPR_2022_paper.pdf>`_ paper.
+    Args:
+        feature_encoder (raft.FeatureEncoder): Raft-like Feature Encoder module extract low-level features from inputs.
+        update_block (raft.UpdateBlock): Raft-like Update Block which recursively refines a flow-map.
+        flow_head (raft.FlowHead): Raft-like Flow Head which predics a flow-map from some inputs.
+        self_attn_block (LocalFeatureTransformer): A Local Feature Transformer that performs self attention on the two feature maps.
+        cross_attn_block (LocalFeatureTransformer): A Local Feature Transformer that performs cross attention between the two feature maps
+            used in the Adaptive Group Correlation module.
+        feature_downsample_rates (List[int]): The downsample rates used to build a feature pyramid from the outputs of the `feature_encoder`. Default: [2, 4]
+        correlation_groups (int): In how many groups should the features be split when computer per-pixel correlation. Defaults 4.
+        search_window_1d (Tuple[int, int]): The alternate search window size in the x and y directions for the 1D case. Defaults to (1, 9).
+        search_dilate_1d (Tuple[int, int]): The dilation used in the `search_window_1d` when selecting pixels. Simillar to `nn.Conv2d` dilate. Defaults to (1, 1).
+        search_window_2d (Tuple[int, int]): The alternate search window size in the x and y directions for the 2D case. Defaults to (3, 3).
+        search_dilate_2d (Tuple[int, int]): The dilation used in the `search_window_2d` when selecting pixels. Simillar to `nn.Conv2d` dilate. Defaults to (1, 1).
+    """
+
+    def __init__(
+        self,
+        *,
+        feature_encoder: raft.FeatureEncoder,
+        update_block: raft.UpdateBlock,
+        flow_head: raft.FlowHead,
+        self_attn_block: LocalFeatureTransformer,
+        cross_attn_block: LocalFeatureTransformer,
+        feature_downsample_rates: Tuple[int, ...] = (2, 4),
+        correlation_groups: int = 4,
+        search_window_1d: Tuple[int, int] = (1, 9),
+        search_dilate_1d: Tuple[int, int] = (1, 1),
+        search_window_2d: Tuple[int, int] = (3, 3),
+        search_dilate_2d: Tuple[int, int] = (1, 1),
+    ) -> None:
+        super().__init__()
+        self.output_channels = 2
+
+        self.feature_encoder = feature_encoder
+        self.update_block = update_block
+        self.flow_head = flow_head
+        self.self_attn_block = self_attn_block
+
+        # average pooling for the feature encoder outputs
+        self.downsampling_pyramid = PyramidDownsample(feature_downsample_rates)
+        self.downsampling_factors: List[int] = [feature_encoder.downsample_factor]
+        base_downsample_factor: int = self.downsampling_factors[0]
+        for rate in feature_downsample_rates:
+            self.downsampling_factors.append(base_downsample_factor * rate)
+
+        # output resolution tracking
+        self.resolutions: List[str] = [f"1 / {factor}" for factor in self.downsampling_factors]
+        self.search_pixels = int(np.prod(search_window_1d))
+
+        # flow convex upsampling mask predictor
+        self.mask_predictor = ConvexMaskPredictor(
+            in_channels=feature_encoder.output_dim // 2,
+            hidden_size=feature_encoder.output_dim,
+            upsample_factor=feature_encoder.downsample_factor,
+            multiplier=0.25,
+        )
+
+        # offsets modules for offseted feature selection
+        self.offset_convs = nn.ModuleDict()
+        self.correlation_layers = nn.ModuleDict()
+
+        offset_conv_layer = partial(
+            Conv2dNormActivation,
+            in_channels=feature_encoder.output_dim,
+            out_channels=self.search_pixels * 2,
+            norm_layer=None,
+            activation_layer=None,
+        )
+
+        # populate the dicts in top to bottom order
+        # useful for iterating through torch.jit.script module given the network forward pass
+        #
+        # Ignore the largest resolution. We handle that separately due to torch.jit.script
+        # not being to able access to runtime generated keys in ModuleDicts.
+        # This way, we can keep a generic way of processing all pyramid levels but except
+        # the final one
+        iterative_correlation_layer = partial(
+            IterativeCorrelationLayer,
+            groups=correlation_groups,
+            search_window_1d=search_window_1d,
+            search_dilate_1d=search_dilate_1d,
+            search_window_2d=search_window_2d,
+            search_dilate_2d=search_dilate_2d,
+        )
+
+        attention_offset_correlation_layer = partial(
+            AttentionOffsetCorrelationLayer,
+            groups=correlation_groups,
+            search_window_1d=search_window_1d,
+            search_dilate_1d=search_dilate_1d,
+            search_window_2d=search_window_2d,
+            search_dilate_2d=search_dilate_2d,
+        )
+
+        for idx, resolution in enumerate(reversed(self.resolutions[1:])):
+            # the largest resolution does use offset convolutions for sampling grid coords
+            offset_conv = None if idx == len(self.resolutions) - 1 else offset_conv_layer()
+            if offset_conv:
+                self.offset_convs[resolution] = offset_conv
+                # only the lowest resolution uses the cross attention module when computing correlation scores
+                attention_module = cross_attn_block if idx == 0 else None
+                self.correlation_layers[resolution] = AdaptiveGroupCorrelationLayer(
+                    iterative_correlation_layer=iterative_correlation_layer(),
+                    attention_offset_correlation_layer=attention_offset_correlation_layer(
+                        attention_module=attention_module
+                    ),
+                )
+
+        # correlation layer for the largest resolution
+        self.max_res_correlation_layer = AdaptiveGroupCorrelationLayer(
+            iterative_correlation_layer=iterative_correlation_layer(),
+            attention_offset_correlation_layer=attention_offset_correlation_layer(),
+        )
+
+        # simple 2D Postional Encodings
+        self.positional_encodings = PositionalEncodingSine(feature_encoder.output_dim)
+
+    def _get_window_type(self, iteration: int) -> str:
+        return "1d" if iteration % 2 == 0 else "2d"
+
+    def forward(
+        self, left_image: Tensor, right_image: Tensor, flow_init: Optional[Tensor], num_iters: int = 10
+    ) -> List[Tensor]:
+        features = torch.cat([left_image, right_image], dim=0)
+        features = self.feature_encoder(features)
+        left_features, right_features = features.chunk(2, dim=0)
+
+        # update block network state and input context are derived from the left feature map
+        net, ctx = left_features.chunk(2, dim=1)
+        net = torch.tanh(net)
+        ctx = torch.relu(ctx)
+
+        # will output lists of tensor.
+        l_pyramid = self.downsampling_pyramid(left_features)
+        r_pyramid = self.downsampling_pyramid(right_features)
+        net_pyramid = self.downsampling_pyramid(net)
+        ctx_pyramid = self.downsampling_pyramid(ctx)
+
+        # we store in reversed order because we process the pyramid from top to bottom
+        l_pyramid: Dict[str, Tensor] = {res: l_pyramid[idx] for idx, res in enumerate(self.resolutions)}
+        r_pyramid: Dict[str, Tensor] = {res: r_pyramid[idx] for idx, res in enumerate(self.resolutions)}
+        net_pyramid: Dict[str, Tensor] = {res: net_pyramid[idx] for idx, res in enumerate(self.resolutions)}
+        ctx_pyramid: Dict[str, Tensor] = {res: ctx_pyramid[idx] for idx, res in enumerate(self.resolutions)}
+
+        # offsets for sampling pixel candidates in the correlation ops
+        offsets: Dict[str, Tensor] = {}
+        for resolution, offset_conv in self.offset_convs.items():
+            feature_map = l_pyramid[resolution]
+            offset = offset_conv(feature_map)
+            offsets[resolution] = (torch.sigmoid(offset) - 0.5) * 2.0
+
+        # the smallest resolution is prepared for passing through self attention
+        min_res = self.resolutions[-1]
+        max_res = self.resolutions[0]
+
+        B, C, MIN_H, MIN_W = l_pyramid[min_res].shape
+        # add positional encodings
+        l_pyramid[min_res] = self.positional_encodings(l_pyramid[min_res])
+        r_pyramid[min_res] = self.positional_encodings(r_pyramid[min_res])
+        # reshaping for transformer
+        l_pyramid[min_res] = l_pyramid[min_res].permute(0, 2, 3, 1).reshape(B, MIN_H * MIN_W, C)
+        r_pyramid[min_res] = r_pyramid[min_res].permute(0, 2, 3, 1).reshape(B, MIN_H * MIN_W, C)
+        # perform self attention
+        l_pyramid[min_res], r_pyramid[min_res] = self.self_attn_block(l_pyramid[min_res], r_pyramid[min_res])
+        # now we need to reshape back into [B, C, H, W] format
+        l_pyramid[min_res] = l_pyramid[min_res].reshape(B, MIN_H, MIN_W, C).permute(0, 3, 1, 2)
+        r_pyramid[min_res] = r_pyramid[min_res].reshape(B, MIN_H, MIN_W, C).permute(0, 3, 1, 2)
+
+        predictions: List[Tensor] = []
+        flow_estimates: Dict[str, Tensor] = {}
+        # we added this because of torch.script.jit
+        # also, the predicition prior is always going to have the
+        # spatial size of the features outputed by the feature encoder
+        flow_pred_prior: Tensor = torch.empty(
+            size=(B, 2, left_features.shape[2], left_features.shape[3]),
+            dtype=l_pyramid[max_res].dtype,
+            device=l_pyramid[max_res].device,
+        )
+
+        if flow_init is not None:
+            scale = l_pyramid[max_res].shape[2] / flow_init.shape[2]
+            # in CREStereo implementation they multiply with -scale instead of scale
+            # this can be either a downsample or an upsample based on the cascaded inference
+            # configuration
+
+            # we use a -scale because the flow used inside the network is a negative flow
+            # from the right to the left, so we flip the flow direction
+            flow_estimates[max_res] = -scale * F.interpolate(
+                input=flow_init,
+                size=l_pyramid[max_res].shape[2:],
+                mode="bilinear",
+                align_corners=True,
+            )
+
+        # when not provided with a flow prior, we construct one using the lower resolution maps
+        else:
+            # initialize a zero flow with the smallest resolution
+            flow = torch.zeros(size=(B, 2, MIN_H, MIN_W), device=left_features.device, dtype=left_features.dtype)
+
+            # flows from coarse resolutions are refined similarly
+            # we always need to fetch the next pyramid feature map as well
+            # when updating coarse resolutions, therefore we create a reversed
+            # view which has its order synced with the ModuleDict keys iterator
+            coarse_resolutions: List[str] = self.resolutions[::-1]  # using slicing because of torch.jit.script
+            fine_grained_resolution = max_res
+
+            # set the coarsest flow to the zero flow
+            flow_estimates[coarse_resolutions[0]] = flow
+
+            # the correlation layer ModuleDict will contain layers ordered from coarse to fine resolution
+            # i.e ["1 / 16", "1 / 8", "1 / 4"]
+            # the correlation layer ModuleDict has layers for all the resolutions except the fine one
+            # i.e {"1 / 16": Module, "1 / 8": Module}
+            # for these resolution we perform only half of the number of refinement iterations
+            for idx, (resolution, correlation_layer) in enumerate(self.correlation_layers.items()):
+                # compute the scale difference between the first pyramid scale and the current pyramid scale
+                scale_to_base = l_pyramid[fine_grained_resolution].shape[2] // l_pyramid[resolution].shape[2]
+                for it in range(num_iters // 2):
+                    # set wether or not we want to search on (X, Y) axes for correlation or just on X axis
+                    window_type = self._get_window_type(it)
+                    # we consider this a prior, therefor we do not want to back-propagate through it
+                    flow_estimates[resolution] = flow_estimates[resolution].detach()
+
+                    correlations = correlation_layer(
+                        l_pyramid[resolution],  # left
+                        r_pyramid[resolution],  # right
+                        flow_estimates[resolution],
+                        offsets[resolution],
+                        window_type,
+                    )
+
+                    # update the recurrent network state and the flow deltas
+                    net_pyramid[resolution], delta_flow = self.update_block(
+                        net_pyramid[resolution], ctx_pyramid[resolution], correlations, flow_estimates[resolution]
+                    )
+
+                    # the convex upsampling weights are computed w.r.t.
+                    # the recurrent update state
+                    up_mask = self.mask_predictor(net_pyramid[resolution])
+                    flow_estimates[resolution] = flow_estimates[resolution] + delta_flow
+                    # convex upsampling with the initial feature encoder downsampling rate
+                    flow_pred_prior = upsample_flow(
+                        flow_estimates[resolution], up_mask, factor=self.downsampling_factors[0]
+                    )
+                    # we then bilinear upsample to the final resolution
+                    # we use a factor that's equivalent to the difference between
+                    # the current downsample resolution and the base downsample resolution
+                    #
+                    # i.e. if a 1 / 16 flow is upsampled by 4 (base downsampling) we get a 1 / 4 flow.
+                    # therefore we have to further upscale it by the difference between
+                    # the current level 1 / 16 and the base level 1 / 4.
+                    #
+                    # we use a -scale because the flow used inside the network is a negative flow
+                    # from the right to the left, so we flip the flow direction in order to get the
+                    # left to right flow
+                    flow_pred = -upsample_flow(flow_pred_prior, None, factor=scale_to_base)
+                    predictions.append(flow_pred)
+
+                # when constructing the next resolution prior, we resample w.r.t
+                # to the scale of the next level in the pyramid
+                next_resolution = coarse_resolutions[idx + 1]
+                scale_to_next = l_pyramid[next_resolution].shape[2] / flow_pred_prior.shape[2]
+                # we use the flow_up_prior because this is a more accurate estimation of the true flow
+                # due to the convex upsample, which resembles a learned super-resolution module.
+                # this is not necessarily an upsample, it can be a downsample, based on the provided configuration
+                flow_estimates[next_resolution] = -scale_to_next * F.interpolate(
+                    input=flow_pred_prior,
+                    size=l_pyramid[next_resolution].shape[2:],
+                    mode="bilinear",
+                    align_corners=True,
+                )
+
+        # finally we will be doing a full pass through the fine-grained resolution
+        # this coincides with the maximum resolution
+
+        # we keep a separate loop here in order to avoid python control flow
+        # to decide how much iterations should we do based on the current resolution
+        # further more, if provided with an inital flow, there is no need to generate
+        # a prior estimate when moving into the final refinement stage
+
+        for it in range(num_iters):
+            search_window_type = self._get_window_type(it)
+
+            flow_estimates[max_res] = flow_estimates[max_res].detach()
+            # we run the fine-grained resolution correlations in iterative mode
+            # this means that we are using the fixed window pixel selections
+            # instead of the deformed ones as with the previous steps
+            correlations = self.max_res_correlation_layer(
+                l_pyramid[max_res],
+                r_pyramid[max_res],
+                flow_estimates[max_res],
+                extra_offset=None,
+                window_type=search_window_type,
+                iter_mode=True,
+            )
+
+            net_pyramid[max_res], delta_flow = self.update_block(
+                net_pyramid[max_res], ctx_pyramid[max_res], correlations, flow_estimates[max_res]
+            )
+
+            up_mask = self.mask_predictor(net_pyramid[max_res])
+            flow_estimates[max_res] = flow_estimates[max_res] + delta_flow
+            # at the final resolution we simply do a convex upsample using the base downsample rate
+            flow_pred = -upsample_flow(flow_estimates[max_res], up_mask, factor=self.downsampling_factors[0])
+            predictions.append(flow_pred)
+
+        return predictions
+
+
+def _crestereo(
+    *,
+    weights: Optional[WeightsEnum],
+    progress: bool,
+    # Feature Encoder
+    feature_encoder_layers: Tuple[int, int, int, int, int],
+    feature_encoder_strides: Tuple[int, int, int, int],
+    feature_encoder_block: Callable[..., nn.Module],
+    feature_encoder_norm_layer: Callable[..., nn.Module],
+    # Average Pooling Pyramid
+    feature_downsample_rates: Tuple[int, ...],
+    # Adaptive Correlation Layer
+    corr_groups: int,
+    corr_search_window_2d: Tuple[int, int],
+    corr_search_dilate_2d: Tuple[int, int],
+    corr_search_window_1d: Tuple[int, int],
+    corr_search_dilate_1d: Tuple[int, int],
+    # Flow head
+    flow_head_hidden_size: int,
+    # Recurrent block
+    recurrent_block_hidden_state_size: int,
+    recurrent_block_kernel_size: Tuple[Tuple[int, int], Tuple[int, int]],
+    recurrent_block_padding: Tuple[Tuple[int, int], Tuple[int, int]],
+    # Motion Encoder
+    motion_encoder_corr_layers: Tuple[int, int],
+    motion_encoder_flow_layers: Tuple[int, int],
+    motion_encoder_out_channels: int,
+    # Transformer Blocks
+    num_attention_heads: int,
+    num_self_attention_layers: int,
+    num_cross_attention_layers: int,
+    self_attention_module: Callable[..., nn.Module],
+    cross_attention_module: Callable[..., nn.Module],
+    **kwargs,
+) -> CREStereo:
+
+    feature_encoder = kwargs.pop("feature_encoder", None) or raft.FeatureEncoder(
+        block=feature_encoder_block,
+        layers=feature_encoder_layers,
+        strides=feature_encoder_strides,
+        norm_layer=feature_encoder_norm_layer,
+    )
+
+    if feature_encoder.output_dim % corr_groups != 0:
+        raise ValueError(
+            f"Final ``feature_encoder_layers`` size should be divisible by ``corr_groups`` argument."
+            f"Feature encoder output size : {feature_encoder.output_dim}, Correlation groups: {corr_groups}."
+        )
+
+    motion_encoder = kwargs.pop("motion_encoder", None) or raft.MotionEncoder(
+        in_channels_corr=corr_groups * int(np.prod(corr_search_window_1d)),
+        corr_layers=motion_encoder_corr_layers,
+        flow_layers=motion_encoder_flow_layers,
+        out_channels=motion_encoder_out_channels,
+    )
+
+    out_channels_context = feature_encoder_layers[-1] - recurrent_block_hidden_state_size
+    recurrent_block = kwargs.pop("recurrent_block", None) or raft.RecurrentBlock(
+        input_size=motion_encoder.out_channels + out_channels_context,
+        hidden_size=recurrent_block_hidden_state_size,
+        kernel_size=recurrent_block_kernel_size,
+        padding=recurrent_block_padding,
+    )
+
+    flow_head = kwargs.pop("flow_head", None) or raft.FlowHead(
+        in_channels=out_channels_context, hidden_size=flow_head_hidden_size
+    )
+
+    update_block = raft.UpdateBlock(motion_encoder=motion_encoder, recurrent_block=recurrent_block, flow_head=flow_head)
+
+    self_attention_module = kwargs.pop("self_attention_module", None) or LinearAttention
+    self_attn_block = LocalFeatureTransformer(
+        dim_model=feature_encoder.output_dim,
+        num_heads=num_attention_heads,
+        attention_directions=["self"] * num_self_attention_layers,
+        attention_module=self_attention_module,
+    )
+
+    cross_attention_module = kwargs.pop("cross_attention_module", None) or LinearAttention
+    cross_attn_block = LocalFeatureTransformer(
+        dim_model=feature_encoder.output_dim,
+        num_heads=num_attention_heads,
+        attention_directions=["cross"] * num_cross_attention_layers,
+        attention_module=cross_attention_module,
+    )
+
+    model = CREStereo(
+        feature_encoder=feature_encoder,
+        update_block=update_block,
+        flow_head=flow_head,
+        self_attn_block=self_attn_block,
+        cross_attn_block=cross_attn_block,
+        feature_downsample_rates=feature_downsample_rates,
+        correlation_groups=corr_groups,
+        search_window_1d=corr_search_window_1d,
+        search_window_2d=corr_search_window_2d,
+        search_dilate_1d=corr_search_dilate_1d,
+        search_dilate_2d=corr_search_dilate_2d,
+    )
+
+    if weights is not None:
+        model.load_state_dict(weights.get_state_dict(progress=progress))
+
+    return model
+
+
+class CREStereo_Base_Weights(WeightsEnum):
+    pass
+
+
+def crestereo_base(*, weights: Optional[CREStereo_Base_Weights] = None, progress=True, **kwargs) -> CREStereo:
+    return _crestereo(
+        weights=weights,
+        progress=progress,
+        # Feature encoder
+        feature_encoder_layers=(64, 64, 96, 128, 256),
+        feature_encoder_strides=(2, 1, 2, 1),
+        feature_encoder_block=partial(raft.ResidualBlock, always_project=True),
+        feature_encoder_norm_layer=nn.InstanceNorm2d,
+        # Average pooling pyramid
+        feature_downsample_rates=(2, 4),
+        # Motion encoder
+        motion_encoder_corr_layers=(256, 192),
+        motion_encoder_flow_layers=(128, 64),
+        motion_encoder_out_channels=128,
+        # Recurrent block
+        recurrent_block_hidden_state_size=128,
+        recurrent_block_kernel_size=((1, 5), (5, 1)),
+        recurrent_block_padding=((0, 2), (2, 0)),
+        # Flow head
+        flow_head_hidden_size=256,
+        # Transformer blocks
+        num_attention_heads=8,
+        num_self_attention_layers=1,
+        num_cross_attention_layers=1,
+        self_attention_module=LinearAttention,
+        cross_attention_module=LinearAttention,
+        # Adaptive Correlation layer
+        corr_groups=4,
+        corr_search_window_2d=(3, 3),
+        corr_search_dilate_2d=(1, 1),
+        corr_search_window_1d=(1, 9),
+        corr_search_dilate_1d=(1, 1),
+    )