diff --git a/dacapo/experiments/architectures/attention_unet.py b/dacapo/experiments/architectures/attention_unet.py
new file mode 100644
index 000000000..f9c7f767f
--- /dev/null
+++ b/dacapo/experiments/architectures/attention_unet.py
@@ -0,0 +1,71 @@
+
+import torch
+import torch.nn as nn
+from .cnnectome_unet import ConvPass,Downsample,Upsample
+
+class AttentionBlockModule(nn.Module):
+    def __init__(self, F_g, F_l, F_int, dims):
+        """Attention Block Module::
+
+        The attention block takes two inputs: 'g' (gating signal) and 'x' (input features).
+    
+            [g] --> W_g --\                 /--> psi --> * --> [output]
+                            \               /     
+            [x] --> W_x --> [+] --> relu --      
+            
+    Where:
+    - W_g and W_x are 1x1 Convolution followed by Batch Normalization
+    - [+] indicates element-wise addition
+    - relu is the Rectified Linear Unit activation function
+    - psi is a sequence of 1x1 Convolution, Batch Normalization, and Sigmoid activation
+    - * indicates element-wise multiplication between the output of psi and input feature 'x'
+    - [output] has the same dimensions as input 'x', selectively emphasized by attention weights
+
+    Args:
+    F_g (int): The number of feature channels in the gating signal (g). 
+               This is the input channel dimension for the W_g convolutional layer.
+
+    F_l (int): The number of feature channels in the input features (x). 
+               This is the input channel dimension for the W_x convolutional layer.
+
+    F_int (int): The number of intermediate feature channels. 
+                 This represents the output channel dimension of the W_g and W_x convolutional layers 
+                 and the input channel dimension for the psi layer. Typically, F_int is smaller 
+                 than F_g and F_l, as it serves to compress the feature representations before 
+                 applying the attention mechanism.
+
+    The AttentionBlock uses two separate pathways to process 'g' and 'x', combines them,
+    and applies a sigmoid activation to generate an attention map. This map is then used 
+    to scale the input features 'x', resulting in an output that focuses on important 
+    features as dictated by the gating signal 'g'.
+           
+           """
+        
+        
+        super(AttentionBlockModule, self).__init__()
+        self.dims = dims
+        self.kernel_sizes = [(1,) * self.dims, (1,) * self.dims]
+        print("kernel_sizes:",self.kernel_sizes)
+
+        self.W_g = ConvPass(F_g, F_int, kernel_sizes=self.kernel_sizes, activation=None,padding="same")
+        
+        self.W_x = nn.Sequential(
+            ConvPass(F_l, F_int, kernel_sizes=self.kernel_sizes, activation=None,padding="same"),
+            Downsample((2,)*self.dims)
+        )
+
+        self.psi = ConvPass(F_int, 1, kernel_sizes=self.kernel_sizes, activation="Sigmoid",padding="same")
+
+        up_mode = {2: 'bilinear', 3: 'trilinear'}[self.dims]
+
+        self.up = nn.Upsample(scale_factor=2, mode=up_mode, align_corners=True)
+        
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, g, x):
+        g1 = self.W_g(g)
+        x1 = self.W_x(x)
+        psi = self.relu(g1 + x1)
+        psi = self.psi(psi)
+        psi = self.up(psi)
+        return x * psi
\ No newline at end of file