diff --git a/networks/lora_control_net.py b/networks/lora_control_net.py
index 7a026eba..9259389f 100644
--- a/networks/lora_control_net.py
+++ b/networks/lora_control_net.py
@@ -5,11 +5,12 @@ from networks.lora import LoRAModule, LoRANetwork
 from library import sdxl_original_unet
 
 
-SKIP_INPUT_BLOCKS = True
-SKIP_OUTPUT_BLOCKS = False
+SKIP_INPUT_BLOCKS = False
+SKIP_OUTPUT_BLOCKS = True
 SKIP_CONV2D = False
 TRANSFORMER_ONLY = True  # if True, SKIP_CONV2D is ignored
 ATTN1_ETC_ONLY = True
+TRANSFORMER_MAX_BLOCK_INDEX = 3  # None  # 2  # None for all blocks
 
 
 class LoRAModuleControlNet(LoRAModule):
@@ -48,15 +49,17 @@ class LoRAModuleControlNet(LoRAModule):
 
         self.depth = depth
         self.cond_emb = None
-        self.batch_cond_uncond_enabled = False
+        self.batch_cond_only = False
+        self.use_zeros_for_batch_uncond = False
 
     def set_cond_embs(self, cond_embs_4d, cond_embs_3d):
         cond_embs = cond_embs_4d if self.is_conv2d else cond_embs_3d
         cond_emb = cond_embs[self.depth - 1]
         self.cond_emb = self.conditioning1(cond_emb)
 
-    def set_batch_cond_uncond_enabled(self, enabled):
-        self.batch_cond_uncond_enabled = enabled
+    def set_batch_cond_only(self, cond_only, zeros):
+        self.batch_cond_only = cond_only
+        self.use_zeros_for_batch_uncond = zeros
 
     def forward(self, x):
         if self.cond_emb is None:
@@ -64,7 +67,7 @@ class LoRAModuleControlNet(LoRAModule):
 
         # LoRA
         lx = x
-        if self.batch_cond_uncond_enabled:
+        if self.batch_cond_only:
             lx = lx[1::2]  # cond only
 
         lx = self.lora_down(lx)
@@ -75,6 +78,10 @@ class LoRAModuleControlNet(LoRAModule):
         # conditioning image
         cx = self.cond_emb
         # print(f"C {self.lora_name}, lx.shape={lx.shape}, cx.shape={cx.shape}")
+        if not self.batch_cond_only and cx.shape[0] // 2 == lx.shape[0]:  # inference only
+            cx = cx.repeat(2, 1, 1, 1) if self.is_conv2d else cx.repeat(2, 1, 1)
+            if self.use_zeros_for_batch_uncond:
+                cx[0::2] = 0.0  # uncond is zero
 
         cx = torch.cat([cx, lx], dim=1 if self.is_conv2d else 2)
         cx = self.conditioning2(cx)
@@ -84,7 +91,7 @@ class LoRAModuleControlNet(LoRAModule):
 
         x = self.org_forward(x)
 
-        if self.batch_cond_uncond_enabled:
+        if self.batch_cond_only:
             x[1::2] += lx * self.multiplier * self.scale
         else:
             x += lx * self.multiplier * self.scale
@@ -141,6 +148,13 @@ class LoRAControlNet(torch.nn.Module):
                             lora_name = prefix + "." + name + "." + child_name
                             lora_name = lora_name.replace(".", "_")
 
+                            if TRANSFORMER_MAX_BLOCK_INDEX is not None:
+                                p = lora_name.find("transformer_blocks")
+                                if p >= 0:
+                                    tf_index = int(lora_name[p:].split("_")[2])
+                                    if tf_index > TRANSFORMER_MAX_BLOCK_INDEX:
+                                        continue
+
                             # skip time emb or clip emb
                             if "emb_layers" in lora_name or ("attn2" in lora_name and ("to_k" in lora_name or "to_v" in lora_name)):
                                 continue
@@ -215,9 +229,9 @@ class LoRAControlNet(torch.nn.Module):
         for lora in self.unet_loras:
             lora.set_cond_embs(cond_embs_4d, cond_embs_3d)
 
-    def set_batch_cond_uncond_enabled(self, enabled):
+    def set_batch_cond_only(self, cond_only, zeros):
         for lora in self.unet_loras:
-            lora.set_batch_cond_uncond_enabled(enabled)
+            lora.set_batch_cond_only(cond_only, zeros)
 
     def load_weights(self, file):
         if os.path.splitext(file)[1] == ".safetensors":
@@ -294,11 +308,12 @@ if __name__ == "__main__":
     control_net.to("cuda")
 
     print(control_net)
-    input()
 
     # print number of parameters
     print("number of parameters", sum(p.numel() for p in control_net.parameters() if p.requires_grad))
 
+    input()
+
     unet.set_use_memory_efficient_attention(True, False)
     unet.set_gradient_checkpointing(True)
     unet.train()  # for gradient checkpointing
diff --git a/sdxl_gen_img_lora_ctrl_test.py b/sdxl_gen_img_lora_ctrl_test.py
index e8b22ee1..b1cab576 100644
--- a/sdxl_gen_img_lora_ctrl_test.py
+++ b/sdxl_gen_img_lora_ctrl_test.py
@@ -1571,7 +1571,7 @@ def main(args):
             control_net.apply_to()
             control_net.load_state_dict(state_dict)
             control_net.to(dtype).to(device)
-            control_net.set_batch_cond_uncond_enabled(True)
+            control_net.set_batch_cond_only(False, False)
             control_nets.append(control_net)
 
     if args.opt_channels_last: