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 c440526b9b9..99f70646db2 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
@@ -13,6 +13,12 @@
 
 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 c9210dce933..34183b005b8 100644
--- a/invokeai/backend/patches/layers/lora_layer.py
+++ b/invokeai/backend/patches/layers/lora_layer.py
@@ -19,6 +19,7 @@ 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(
@@ -58,12 +59,44 @@ 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 41e41dbb517..7b5f3468963 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
@@ -20,6 +20,14 @@
 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
@@ -44,6 +52,7 @@ 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()
@@ -65,6 +74,9 @@ 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