diff --git a/extensions-builtin/Lora/networks.py b/extensions-builtin/Lora/networks.py
index 5f00d36e7..12e1c24e1 100644
--- a/extensions-builtin/Lora/networks.py
+++ b/extensions-builtin/Lora/networks.py
@@ -526,7 +526,7 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
 
             module_mlp = net.modules.get(network_layer_name + "_mlp_proj", None)
 
-            if any(isinstance(self, linear) for linear in (modules.models.sd3.mmdit.QkvLinear, modules.models.flux.modules.layers.QkvLinear)) and module_q and module_k and module_v and module_mlp is None:
+            if any(isinstance(self, linear) for linear in (modules.models.sd3.mmdit.QkvLinear, modules.models.flux.modules.layers.QkvLinear)) and module_q and module_k and module_v and module_mlp is None and self.weight.shape[0] // 3 == module_q.up_model.weight.shape[0]:
                 try:
                     with torch.no_grad():
                         # Send "real" orig_weight into MHA's lora module
@@ -545,14 +545,17 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
 
                 continue
 
-            if any(isinstance(self, linear) for linear in (modules.models.flux.modules.layers.QkvLinear,)) and module_q and module_k and module_v and module_mlp:
+            if any(isinstance(self, linear) for linear in (modules.models.flux.modules.layers.QkvLinear,)) and module_q and module_k and module_v:
                 try:
                     with torch.no_grad():
                         qw, kw, vw, mlp = torch.tensor_split(self.weight, (3072, 6144, 9216,), 0)
                         updown_q, _ = module_q.calc_updown(qw)
                         updown_k, _ = module_k.calc_updown(kw)
                         updown_v, _ = module_v.calc_updown(vw)
-                        updown_mlp, _ = module_v.calc_updown(mlp)
+                        if module_mlp is not None:
+                            updown_mlp, _ = module_mlp.calc_updown(mlp)
+                        else:
+                            updown_mlp = torch.zeros(3072 * 4, 3072, dtype=updown_q.dtype, device=updown_q.device)
                         del qw, kw, vw, mlp
                         updown_qkv_mlp = torch.vstack([updown_q, updown_k, updown_v, updown_mlp])
                         self.weight += updown_qkv_mlp