Fix network_oft

            self.constraint = None

            self.block_size, self.num_blocks = factorization(self.out_dim, self.dim)

    def calc_updown_kb(self, orig_weight, multiplier):

    def calc_updown(self, orig_weight):

        I = torch.eye(self.block_size, device=self.oft_blocks.device)

        oft_blocks = self.oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype)

        oft_blocks = oft_blocks - oft_blocks.transpose(1, 2) # ensure skew-symmetric orthogonal matrix

        if self.is_kohya:

            block_Q = oft_blocks - oft_blocks.transpose(1, 2) # ensure skew-symmetric orthogonal matrix

            norm_Q = torch.norm(block_Q.flatten())

            new_norm_Q = torch.clamp(norm_Q, max=self.constraint)

            block_Q = block_Q * ((new_norm_Q + 1e-8) / (norm_Q + 1e-8))

            oft_blocks = torch.matmul(I + block_Q, (I - block_Q).float().inverse())

        R = oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype)

        R = R * multiplier + torch.eye(self.block_size, device=orig_weight.device)

        # This errors out for MultiheadAttention, might need to be handled up-stream

        merged_weight = rearrange(orig_weight, '(k n) ... -> k n ...', k=self.num_blocks, n=self.block_size)

        merged_weight = rearrange(merged_weight, 'k m ... -> (k m) ...')

        updown = merged_weight.to(orig_weight.device, dtype=orig_weight.dtype) - orig_weight

        print(torch.norm(updown))

        output_shape = orig_weight.shape

        return self.finalize_updown(updown, orig_weight, output_shape)

    def calc_updown(self, orig_weight):

        # 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()

        return self.calc_updown_kb(orig_weight, multiplier)

    # override to remove the multiplier/scale factor; it's already multiplied in get_weight

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

        if self.bias is not None:

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

        if ex_bias is not None:

            ex_bias = ex_bias * self.multiplier()

        return updown, ex_bias

        # Ignore calc_scale, which is not used in OFT.

        return updown * self.multiplier(), ex_bias