diff --git a/extensions-builtin/Lora/network_oft.py b/extensions-builtin/Lora/network_oft.py
deleted file mode 100644
index 7821a8a7..00000000
--- a/extensions-builtin/Lora/network_oft.py
+++ /dev/null
@@ -1,118 +0,0 @@
-import torch
-import network
-from einops import rearrange
-
-
-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"]) or all(x in weights.w for x in ["oft_diag"]):
-            return NetworkModuleOFT(net, weights)
-
-        return None
-
-# Supports both kohya-ss' implementation of COFT  https://github.com/kohya-ss/sd-scripts/blob/main/networks/oft.py
-# and KohakuBlueleaf's implementation of OFT/COFT https://github.com/KohakuBlueleaf/LyCORIS/blob/dev/lycoris/modules/diag_oft.py
-class NetworkModuleOFT(network.NetworkModule):
-    def __init__(self,  net: network.Network, weights: network.NetworkWeights):
-
-        super().__init__(net, weights)
-
-        self.lin_module = None
-        self.org_module: list[torch.Module] = [self.sd_module]
-
-        self.scale = 1.0
-        self.is_R = False
-        self.is_boft = False
-
-        # kohya-ss/New LyCORIS OFT/BOFT
-        if "oft_blocks" in weights.w.keys():
-            self.oft_blocks = weights.w["oft_blocks"] # (num_blocks, block_size, block_size)
-            self.alpha = weights.w.get("alpha", None) # alpha is constraint
-            self.dim = self.oft_blocks.shape[0] # lora dim
-        # Old LyCORIS OFT
-        elif "oft_diag" in weights.w.keys():
-            self.is_R = True
-            self.oft_blocks = weights.w["oft_diag"]
-            # self.alpha is unused
-            self.dim = self.oft_blocks.shape[1] # (num_blocks, block_size, block_size)
-
-        # LyCORIS BOFT
-        if self.oft_blocks.dim() == 4:
-            self.is_boft = True
-        self.rescale = weights.w.get('rescale', None)
-        if self.rescale is not None:
-            self.rescale = self.rescale.reshape(-1, *[1]*(self.org_module[0].weight.dim() - 1))
-
-        is_linear = type(self.sd_module) in [torch.nn.Linear, torch.nn.modules.linear.NonDynamicallyQuantizableLinear]
-        is_conv = type(self.sd_module) in [torch.nn.Conv2d]
-        is_other_linear = type(self.sd_module) in [torch.nn.MultiheadAttention] # unsupported
-
-        if is_linear:
-            self.out_dim = self.sd_module.out_features
-        elif is_conv:
-            self.out_dim = self.sd_module.out_channels
-        elif is_other_linear:
-            self.out_dim = self.sd_module.embed_dim
-
-        self.num_blocks = self.dim
-        self.block_size = self.out_dim // self.dim
-        self.constraint = (0 if self.alpha is None else self.alpha) * self.out_dim
-        if self.is_R:
-            self.constraint = None
-            self.block_size = self.dim
-            self.num_blocks = self.out_dim // self.dim
-        elif self.is_boft:
-            self.boft_m = self.oft_blocks.shape[0]
-            self.num_blocks = self.oft_blocks.shape[1]
-            self.block_size = self.oft_blocks.shape[2]
-            self.boft_b = self.block_size
-
-    def calc_updown(self, orig_weight):
-        oft_blocks = self.oft_blocks.to(orig_weight.device)
-        eye = torch.eye(self.block_size, device=oft_blocks.device)
-
-        if not self.is_R:
-            block_Q = oft_blocks - oft_blocks.transpose(-1, -2) # ensure skew-symmetric orthogonal matrix
-            if self.constraint != 0:
-                norm_Q = torch.norm(block_Q.flatten())
-                new_norm_Q = torch.clamp(norm_Q, max=self.constraint.to(oft_blocks.device))
-                block_Q = block_Q * ((new_norm_Q + 1e-8) / (norm_Q + 1e-8))
-            oft_blocks = torch.matmul(eye + block_Q, (eye - block_Q).float().inverse())
-
-        R = oft_blocks.to(orig_weight.device)
-
-        if not self.is_boft:
-            # 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 = torch.einsum(
-                'k n m, k n ... -> k m ...',
-                R,
-                merged_weight
-            )
-            merged_weight = rearrange(merged_weight, 'k m ... -> (k m) ...')
-        else:
-            # TODO: determine correct value for scale
-            scale = 1.0
-            m = self.boft_m
-            b = self.boft_b
-            r_b = b // 2
-            inp = orig_weight
-            for i in range(m):
-                bi = R[i] # b_num, b_size, b_size
-                if i == 0:
-                    # Apply multiplier/scale and rescale into first weight
-                    bi = bi * scale + (1 - scale) * eye
-                inp = rearrange(inp, "(c g k) ... -> (c k g) ...", g=2, k=2**i * r_b)
-                inp = rearrange(inp, "(d b) ... -> d b ...", b=b)
-                inp = torch.einsum("b i j, b j ... -> b i ...", bi, inp)
-                inp = rearrange(inp, "d b ... -> (d b) ...")
-                inp = rearrange(inp, "(c k g) ... -> (c g k) ...", g=2, k=2**i * r_b)
-            merged_weight = inp
-
-        # Rescale mechanism
-        if self.rescale is not None:
-            merged_weight = self.rescale.to(merged_weight) * merged_weight
-
-        updown = merged_weight.to(orig_weight.device) - orig_weight.to(merged_weight.dtype)
-        output_shape = orig_weight.shape
-        return self.finalize_updown(updown, orig_weight, output_shape)