wip incorrect OFT implementation

import torch

import network

class ModuleTypeOFT(network.ModuleType):

    def create_module(self, net: network.Network, weights: network.NetworkWeights):

        if all(x in weights.w for x in ["oft_blocks"]):

            return NetworkModuleOFT(net, weights)

        return None

# adapted from https://github.com/kohya-ss/sd-scripts/blob/main/networks/oft.py

class NetworkModuleOFT(network.NetworkModule):

    def __init__(self,  net: network.Network, weights: network.NetworkWeights):

        super().__init__(net, weights)

        self.oft_blocks = weights.w["oft_blocks"]

        self.alpha = weights.w["alpha"]

        self.dim = self.oft_blocks.shape[0]

        self.num_blocks = self.dim

        #if type(self.alpha) == torch.Tensor:

        #    self.alpha = self.alpha.detach().numpy()

        if "Linear" in self.sd_module.__class__.__name__:

            self.out_dim = self.sd_module.out_features

        elif "Conv" in self.sd_module.__class__.__name__:

            self.out_dim = self.sd_module.out_channels

        self.constraint = self.alpha * self.out_dim

        self.block_size = self.out_dim // self.num_blocks

        self.oft_multiplier = self.multiplier()

        # replace forward method of original linear rather than replacing the module

        # self.org_forward = self.sd_module.forward

        # self.sd_module.forward = self.forward

    def get_weight(self):

        block_Q = self.oft_blocks - self.oft_blocks.transpose(1, 2)

        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))

        I = torch.eye(self.block_size, device=self.oft_blocks.device).unsqueeze(0).repeat(self.num_blocks, 1, 1)

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

        block_R_weighted = self.oft_multiplier * block_R + (1 - self.oft_multiplier) * I

        R = torch.block_diag(*block_R_weighted)

        return R

    def calc_updown(self, orig_weight):

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

        block_Q = oft_blocks - oft_blocks.transpose(1, 2)

        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))

        I = torch.eye(self.block_size, device=oft_blocks.device).unsqueeze(0).repeat(self.num_blocks, 1, 1)

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

        block_R_weighted = self.oft_multiplier * block_R + (1 - self.oft_multiplier) * I

        R = torch.block_diag(*block_R_weighted)

        #R = self.get_weight().to(orig_weight.device, dtype=orig_weight.dtype)

        # W = R*W_0

        updown = orig_weight + R

        output_shape = [R.size(0), orig_weight.size(1)]

        return self.finalize_updown(updown, orig_weight, output_shape)

    # def forward(self, x, y=None):

    #     x = self.org_forward(x)

    #     if self.oft_multiplier == 0.0:

    #         return x

    #     R = self.get_weight().to(x.device, dtype=x.dtype)

    #     if x.dim() == 4:

    #         x = x.permute(0, 2, 3, 1)

    #         x = torch.matmul(x, R)

    #         x = x.permute(0, 3, 1, 2)

    #     else:

    #         x = torch.matmul(x, R)

    #     return x

import network_lokr

import network_full

import network_norm

import network_oft

import torch

from typing import Union

    network_full.ModuleTypeFull(),

    network_norm.ModuleTypeNorm(),

    network_glora.ModuleTypeGLora(),

    network_oft.ModuleTypeOFT(),

]

        elif sd_module is None and "lora_te1_text_model" in key_network_without_network_parts:

            key = key_network_without_network_parts.replace("lora_te1_text_model", "0_transformer_text_model")

            sd_module = shared.sd_model.network_layer_mapping.get(key, None)

        elif sd_module is None and "oft_unet" in key_network_without_network_parts:

            key = key_network_without_network_parts.replace("oft_unet", "diffusion_model")

            sd_module = shared.sd_model.network_layer_mapping.get(key, None)

            # some SD1 Loras also have correct compvis keys

            if sd_module is None: