diff --git a/attn_gym/masks/__init__.py b/attn_gym/masks/__init__.py
index bf597bf..683df8f 100644
--- a/attn_gym/masks/__init__.py
+++ b/attn_gym/masks/__init__.py
@@ -2,3 +2,4 @@
 from attn_gym.masks.sliding_window import generate_sliding_window
 from attn_gym.masks.prefix_lm import generate_prefix_lm_mask
 from attn_gym.masks.document_mask import generate_doc_mask_mod
+from attn_gym.masks.dilated_sliding_window import generate_dilated_sliding_window
diff --git a/attn_gym/masks/dilated_sliding_window.py b/attn_gym/masks/dilated_sliding_window.py
new file mode 100644
index 0000000..bb34a08
--- /dev/null
+++ b/attn_gym/masks/dilated_sliding_window.py
@@ -0,0 +1,58 @@
+import torch
+from torch.nn.attention.flex_attention import _mask_mod_signature
+
+
+def generate_dilated_sliding_window(window_size: int, dilation: int) -> _mask_mod_signature:
+    """Generates a dilated sliding window attention mask.
+    Args:
+        window_size: The size of the sliding window.
+        dilation: The dilation factor for the sliding window.
+
+    Note:
+        Query at position i can only attend to keys within a window of size `window_size`
+        centered around i, where the keys are at positions j such that:
+        * abs(i - j) <= window_size
+        * abs(i - j) % dilation == 0
+    """
+
+    def dilated_sliding_window(b, h, q_idx, kv_idx):
+        diff = torch.abs(q_idx - kv_idx)
+        in_window = diff <= window_size
+        is_dilated = (diff % dilation) == 0
+        return in_window & is_dilated
+
+    dilated_sliding_window.__name__ = f"dilated_sliding_window_{window_size}_dilation_{dilation}"
+    return dilated_sliding_window
+
+
+def main(device: str = "cpu"):
+    """Visualize the attention scores of dilated sliding window mask mod.
+
+    Args:
+        device (str): Device to use for computation.
+    """
+    from attn_gym import visualize_attention_scores
+
+    B, H, SEQ_LEN, HEAD_DIM = 1, 1, 24, 8
+
+    def make_tensor():
+        return torch.ones(B, H, SEQ_LEN, HEAD_DIM, device=device)
+
+    query, key = make_tensor(), make_tensor()
+
+    dilated_sliding_window_mask = generate_dilated_sliding_window(window_size=4, dilation=2)
+    visualize_attention_scores(
+        query,
+        key,
+        mask_mod=dilated_sliding_window_mask,
+        device=device,
+        name="dilated_sliding_window_mask",
+    )
+
+
+if __name__ == "__main__":
+    try:
+        from jsonargparse import CLI
+    except ImportError:
+        raise ImportError("Be sure to run: pip install -e .'[viz]'")
+    CLI(main)