diff --git a/scripts/fabric.py b/scripts/fabric.py
index 46b09ce..b6754dc 100644
--- a/scripts/fabric.py
+++ b/scripts/fabric.py
@@ -27,7 +27,7 @@ except ImportError:
     from modules.ui import create_refresh_button
 
 
-__version__ = "0.6.2"
+__version__ = "0.6.3"
 
 DEBUG = os.getenv("DEBUG", "false").lower() in ("true", "1")
 
diff --git a/scripts/weighted_attention.py b/scripts/weighted_attention.py
index 4263632..0092845 100644
--- a/scripts/weighted_attention.py
+++ b/scripts/weighted_attention.py
@@ -65,17 +65,18 @@ def weighted_attention(self, attn_fn, x, context=None, weights=None, **kwargs):
 def _get_attn_bias(weights, shape=None, dtype=torch.float32):
     # shape of weights needs to be divisible by 8 in order for xformers attn bias to work
     last_dim = ((weights.shape[-1] - 1) // 8 + 1) * 8
-    w_bias = torch.zeros(weights.shape[:-1] + (last_dim,), device=weights.device, dtype=weights.dtype)
+    w_bias = torch.zeros(weights.shape[:-1] + (last_dim,), device=weights.device, dtype=dtype)
     
     min_val = torch.finfo(dtype).min
-    w_bias[..., :weights.shape[-1]] = weights.log().clamp(min=min_val)
+    w_bias[..., :weights.shape[-1]] = weights.log().to(dtype=dtype).clamp(min=min_val)
     
     if shape is not None:
         assert shape[-1] == weights.shape[-1], "Last dimension of shape must match last dimension of weights (number of keys)"
         w_bias = w_bias.view([1] * (len(shape) - 1) + [-1]).expand(shape[:-1] + (last_dim,))
+    # make sure not to consolidate the tensor after expanding,
+    # as it will lead to a stride overflow for large numbers of feedback images
 
-    # cast first in order to preserve multiple-of-8 stride
-    w_bias = w_bias.to(dtype=dtype)
+    # slice in order to preserve multiple-of-8 stride
     w_bias = w_bias[..., :weights.shape[-1]]
     return w_bias