diff --git a/invokeai/backend/model_manager/load/model_cache/torch_module_autocast/custom_modules/custom_linear.py b/invokeai/backend/model_manager/load/model_cache/torch_module_autocast/custom_modules/custom_linear.py
index 99f70646db2..c440526b9b9 100644
--- a/invokeai/backend/model_manager/load/model_cache/torch_module_autocast/custom_modules/custom_linear.py
+++ b/invokeai/backend/model_manager/load/model_cache/torch_module_autocast/custom_modules/custom_linear.py
@@ -13,12 +13,6 @@
 
 def linear_lora_forward(input: torch.Tensor, lora_layer: LoRALayer, lora_weight: float) -> torch.Tensor:
     """An optimized implementation of the residual calculation for a sidecar linear LoRALayer."""
-    # up matrix and down matrix have different ranks so we can't simply multiply them
-    if lora_layer.up.shape[1] != lora_layer.down.shape[0]:
-        x = torch.nn.functional.linear(input, lora_layer.get_weight(lora_weight), bias=lora_layer.bias)
-        x *= lora_weight * lora_layer.scale()
-        return x
-
     x = torch.nn.functional.linear(input, lora_layer.down)
     if lora_layer.mid is not None:
         x = torch.nn.functional.linear(x, lora_layer.mid)
diff --git a/invokeai/backend/patches/layers/lora_layer.py b/invokeai/backend/patches/layers/lora_layer.py
index cf79f520519..c9210dce933 100644
--- a/invokeai/backend/patches/layers/lora_layer.py
+++ b/invokeai/backend/patches/layers/lora_layer.py
@@ -19,7 +19,6 @@ def __init__(
         self.up = up
         self.mid = mid
         self.down = down
-        self.are_ranks_equal = up.shape[1] == down.shape[0]
 
     @classmethod
     def from_state_dict_values(
@@ -59,42 +58,12 @@ def from_state_dict_values(
     def _rank(self) -> int:
         return self.down.shape[0]
 
-    def fuse_weights(self, up: torch.Tensor, down: torch.Tensor) -> torch.Tensor:
-        """
-        Fuse the weights of the up and down matrices of a LoRA layer with different ranks.
-
-        Since the Huggingface implementation of KQV projections are fused, when we convert to Kohya format
-        the LoRA weights have different ranks. This function handles the fusion of these differently sized
-        matrices.
-        """
-
-        fused_lora = torch.zeros((up.shape[0], down.shape[1]), device=down.device, dtype=down.dtype)
-        rank_diff = down.shape[0] / up.shape[1]
-
-        if rank_diff > 1:
-            rank_diff = down.shape[0] / up.shape[1]
-            w_down = down.chunk(int(rank_diff), dim=0)
-            for w_down_chunk in w_down:
-                fused_lora = fused_lora + (torch.mm(up, w_down_chunk))
-        else:
-            rank_diff = up.shape[1] / down.shape[0]
-            w_up = up.chunk(int(rank_diff), dim=0)
-            for w_up_chunk in w_up:
-                fused_lora = fused_lora + (torch.mm(w_up_chunk, down))
-
-        return fused_lora
-
     def get_weight(self, orig_weight: torch.Tensor) -> torch.Tensor:
         if self.mid is not None:
             up = self.up.reshape(self.up.shape[0], self.up.shape[1])
             down = self.down.reshape(self.down.shape[0], self.down.shape[1])
             weight = torch.einsum("m n w h, i m, n j -> i j w h", self.mid, up, down)
         else:
-            # up matrix and down matrix have different ranks so we can't simply multiply them
-            if not self.are_ranks_equal:
-                weight = self.fuse_weights(self.up, self.down)
-                return weight
-
             weight = self.up.reshape(self.up.shape[0], -1) @ self.down.reshape(self.down.shape[0], -1)
 
         return weight
diff --git a/invokeai/backend/patches/lora_conversions/flux_kohya_lora_conversion_utils.py b/invokeai/backend/patches/lora_conversions/flux_kohya_lora_conversion_utils.py
index 7b5f3468963..41e41dbb517 100644
--- a/invokeai/backend/patches/lora_conversions/flux_kohya_lora_conversion_utils.py
+++ b/invokeai/backend/patches/lora_conversions/flux_kohya_lora_conversion_utils.py
@@ -20,14 +20,6 @@
 FLUX_KOHYA_TRANSFORMER_KEY_REGEX = (
     r"lora_unet_(\w+_blocks)_(\d+)_(img_attn|img_mlp|img_mod|txt_attn|txt_mlp|txt_mod|linear1|linear2|modulation)_?(.*)"
 )
-
-# A regex pattern that matches all of the last layer keys in the Kohya FLUX LoRA format.
-# Example keys:
-#   lora_unet_final_layer_linear.alpha
-#   lora_unet_final_layer_linear.lora_down.weight
-#   lora_unet_final_layer_linear.lora_up.weight
-FLUX_KOHYA_LAST_LAYER_KEY_REGEX = r"lora_unet_final_layer_(linear|linear1|linear2)_?(.*)"
-
 # A regex pattern that matches all of the CLIP keys in the Kohya FLUX LoRA format.
 # Example keys:
 #   lora_te1_text_model_encoder_layers_0_mlp_fc1.alpha
@@ -52,7 +44,6 @@ def is_state_dict_likely_in_flux_kohya_format(state_dict: Dict[str, Any]) -> boo
     """
     return all(
         re.match(FLUX_KOHYA_TRANSFORMER_KEY_REGEX, k)
-        or re.match(FLUX_KOHYA_LAST_LAYER_KEY_REGEX, k)
         or re.match(FLUX_KOHYA_CLIP_KEY_REGEX, k)
         or re.match(FLUX_KOHYA_T5_KEY_REGEX, k)
         for k in state_dict.keys()
@@ -74,9 +65,6 @@ def lora_model_from_flux_kohya_state_dict(state_dict: Dict[str, torch.Tensor]) -
     t5_grouped_sd: dict[str, dict[str, torch.Tensor]] = {}
     for layer_name, layer_state_dict in grouped_state_dict.items():
         if layer_name.startswith("lora_unet"):
-            # Skip the final layer. This is incompatible with current model definition.
-            if layer_name.startswith("lora_unet_final_layer"):
-                continue
             transformer_grouped_sd[layer_name] = layer_state_dict
         elif layer_name.startswith("lora_te1"):
             clip_grouped_sd[layer_name] = layer_state_dict