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

        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)

            )

            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:

        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