Use self.scale instead of custom finalize

        self.lin_module = None

        self.org_module: list[torch.Module] = [self.sd_module]

        self.scale = 1.0

        # kohya-ss

        if "oft_blocks" in weights.w.keys():

            self.is_kohya = True

        print(torch.norm(updown))

        output_shape = orig_weight.shape

        return self.finalize_updown(updown, orig_weight, output_shape)

    def finalize_updown(self, updown, orig_weight, output_shape, ex_bias=None):

        if self.bias is not None:

            updown = updown.reshape(self.bias.shape)

            updown += self.bias.to(orig_weight.device, dtype=orig_weight.dtype)

            updown = updown.reshape(output_shape)

        if len(output_shape) == 4:

            updown = updown.reshape(output_shape)

        if orig_weight.size().numel() == updown.size().numel():

            updown = updown.reshape(orig_weight.shape)

        if ex_bias is not None:

            ex_bias = ex_bias * self.multiplier()

        # Ignore calc_scale, which is not used in OFT.

        return updown * self.multiplier(), ex_bias