fix: ignore calc_scale() for COFT which has very small alpha

extensions-builtin/Lora/network_oft.py

@@ -99,12 +99,9 @@ class NetworkModuleOFT(network.NetworkModule):
         is_other_linear = type(self.sd_module) in [torch.nn.MultiheadAttention]
 
         if not is_other_linear:
-            #if is_other_linear and orig_weight.shape[0] != orig_weight.shape[1]:
-            #    orig_weight=orig_weight.permute(1, 0)
-
             oft_blocks = self.oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype)
 
-            # without this line the results are significantly worse / less accurate
+            # ensure skew-symmetric matrix
             oft_blocks = oft_blocks - oft_blocks.transpose(1, 2)
 
             R = oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype)
@@ -118,9 +115,6 @@ class NetworkModuleOFT(network.NetworkModule):
             )
             merged_weight = rearrange(merged_weight, 'k m ... -> (k m) ...')
 
-            #if is_other_linear and orig_weight.shape[0] != orig_weight.shape[1]:
-            #    orig_weight=orig_weight.permute(1, 0)
-
             updown = merged_weight.to(orig_weight.device, dtype=orig_weight.dtype) - orig_weight
             output_shape = orig_weight.shape
         else:
@@ -132,10 +126,10 @@ class NetworkModuleOFT(network.NetworkModule):
         return self.finalize_updown(updown, orig_weight, output_shape)
 
     def calc_updown(self, orig_weight):
-        multiplier = self.multiplier() * self.calc_scale()
-        #if self.is_kohya:
-        #    return self.calc_updown_kohya(orig_weight, multiplier)
-        #else:
+        # if alpha is a very small number as in coft, calc_scale will return a almost zero number so we ignore it
+        #multiplier = self.multiplier() * self.calc_scale()
+        multiplier = self.multiplier()
+
         return self.calc_updown_kb(orig_weight, multiplier)
 
     # override to remove the multiplier/scale factor; it's already multiplied in get_weight