diff --git a/networks/lora_sd3.py b/networks/lora_sd3.py
index c1eb68b8..efe20245 100644
--- a/networks/lora_sd3.py
+++ b/networks/lora_sd3.py
@@ -307,6 +307,7 @@ class LoRANetwork(torch.nn.Module):
             target_replace_modules: List[str],
             filter: Optional[str] = None,
             default_dim: Optional[int] = None,
+            include_conv2d_if_filter: bool = False,
         ) -> List[LoRAModule]:
             prefix = (
                 self.LORA_PREFIX_SD3
@@ -332,8 +333,11 @@ class LoRANetwork(torch.nn.Module):
                             lora_name = prefix + "." + (name + "." if name else "") + child_name
                             lora_name = lora_name.replace(".", "_")
 
-                            if filter is not None and not filter in lora_name:
-                                continue
+                            force_incl_conv2d = False
+                            if filter is not None:
+                                if not filter in lora_name:
+                                    continue
+                                force_incl_conv2d = include_conv2d_if_filter
 
                             dim = None
                             alpha = None
@@ -373,6 +377,10 @@ class LoRANetwork(torch.nn.Module):
                                 elif self.conv_lora_dim is not None:
                                     dim = self.conv_lora_dim
                                     alpha = self.conv_alpha
+                                elif force_incl_conv2d:
+                                    # x_embedder
+                                    dim = default_dim if default_dim is not None else self.lora_dim
+                                    alpha = self.alpha
 
                             if dim is None or dim == 0:
                                 # skipした情報を出力
@@ -428,7 +436,7 @@ class LoRANetwork(torch.nn.Module):
             for filter, in_dim in zip(
                 [
                     "context_embedder",
-                    "_t_embedder", # don't use "t_embedder" because it's used in "context_embedder"
+                    "_t_embedder",  # don't use "t_embedder" because it's used in "context_embedder"
                     "x_embedder",
                     "y_embedder",
                     "final_layer_adaLN_modulation",
@@ -436,7 +444,12 @@ class LoRANetwork(torch.nn.Module):
                 ],
                 self.emb_dims,
             ):
-                loras, _ = create_modules(True, None, unet, None, filter=filter, default_dim=in_dim)
+                # x_embedder is conv2d, so we need to include it
+                loras, _ = create_modules(
+                    True, None, unet, None, filter=filter, default_dim=in_dim, include_conv2d_if_filter=filter == "x_embedder"
+                )
+                # if len(loras) > 0:
+                #     logger.info(f"create LoRA for {filter}: {len(loras)} modules.")
                 self.unet_loras.extend(loras)
 
         logger.info(f"create LoRA for SD3 MMDiT: {len(self.unet_loras)} modules.")