diff --git a/extensions-builtin/Lora/network_lora.py b/extensions-builtin/Lora/network_lora.py
index a7a088949..2bc6af5d2 100644
--- a/extensions-builtin/Lora/network_lora.py
+++ b/extensions-builtin/Lora/network_lora.py
@@ -2,6 +2,7 @@ import torch
 
 import lyco_helpers
 import modules.models.sd3.mmdit
+import modules.models.flux.modules.layers
 import network
 from modules import devices
 
@@ -37,7 +38,7 @@ class NetworkModuleLora(network.NetworkModule):
         if weight is None and none_ok:
             return None
 
-        is_linear = type(self.sd_module) in [torch.nn.Linear, torch.nn.modules.linear.NonDynamicallyQuantizableLinear, torch.nn.MultiheadAttention, modules.models.sd3.mmdit.QkvLinear]
+        is_linear = type(self.sd_module) in [torch.nn.Linear, torch.nn.modules.linear.NonDynamicallyQuantizableLinear, torch.nn.MultiheadAttention, modules.models.sd3.mmdit.QkvLinear, modules.models.flux.modules.layers.QkvLinear ]
         is_conv = type(self.sd_module) in [torch.nn.Conv2d]
 
         if is_linear:
diff --git a/extensions-builtin/Lora/networks.py b/extensions-builtin/Lora/networks.py
index e58e1fb56..46c9ac1ff 100644
--- a/extensions-builtin/Lora/networks.py
+++ b/extensions-builtin/Lora/networks.py
@@ -37,7 +37,7 @@ module_types = [
 
 
 re_digits = re.compile(r"\d+")
-re_x_proj = re.compile(r"(.*)_([qkv]_proj)$")
+re_x_proj = re.compile(r"(.*)_((?:[qkv]|mlp)_proj)$")
 re_compiled = {}
 
 suffix_conversion = {
@@ -460,7 +460,7 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
 
         for net in loaded_networks:
             module = net.modules.get(network_layer_name, None)
-            if module is not None and hasattr(self, 'weight') and not isinstance(module, modules.models.sd3.mmdit.QkvLinear):
+            if module is not None and hasattr(self, 'weight') and not all(isinstance(module, linear) for linear in (modules.models.sd3.mmdit.QkvLinear, modules.models.flux.modules.layers.QkvLinear)):
                 try:
                     with torch.no_grad():
                         if getattr(self, 'fp16_weight', None) is None:
@@ -520,7 +520,9 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
 
                 continue
 
-            if isinstance(self, modules.models.sd3.mmdit.QkvLinear) and module_q and module_k and module_v:
+            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:
                 try:
                     with torch.no_grad():
                         # Send "real" orig_weight into MHA's lora module
@@ -531,6 +533,26 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
                         del qw, kw, vw
                         updown_qkv = torch.vstack([updown_q, updown_k, updown_v])
                         self.weight += updown_qkv
+                        del updown_qkv
+
+                except RuntimeError as e:
+                    logging.debug(f"Network {net.name} layer {network_layer_name}: {e}")
+                    extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1
+
+                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:
+                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)
+                        del qw, kw, vw, mlp
+                        updown_qkv_mlp = torch.vstack([updown_q, updown_k, updown_v, updown_mlp])
+                        self.weight += updown_qkv_mlp
+                        del updown_qkv_mlp
 
                 except RuntimeError as e:
                     logging.debug(f"Network {net.name} layer {network_layer_name}: {e}")