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@@ -297,7 +297,7 @@
                                 the U-Net’s cross-attention layers and a mask derived from
                                 the noise estimate - yielding a mask both focused on relevant image
                                 regions and of fine granularity.
-                                We empirically demonstrate that these maps can also capture regions 290
+                                We empirically demonstrate that these maps can also capture regions
                                 of an image relevant to an editing concept that is not already present.
                                 Specifically for multiple edits, calculating a
                                 dedicated mask for each edit prompt ensures that the corresponding