diff --git a/networks/lora_control_net.py b/networks/lora_control_net.py
index 9259389f..11b4db90 100644
--- a/networks/lora_control_net.py
+++ b/networks/lora_control_net.py
@@ -9,8 +9,8 @@ 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
+ATTN1_ETC_ONLY = False # True
+TRANSFORMER_MAX_BLOCK_INDEX = None # 3  # None  # 2  # None for all blocks
 
 
 class LoRAModuleControlNet(LoRAModule):
@@ -77,11 +77,11 @@ 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
+        if not self.batch_cond_only and lx.shape[0] // 2 == cx.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
+        # print(f"C {self.lora_name}, lx.shape={lx.shape}, cx.shape={cx.shape}")
 
         cx = torch.cat([cx, lx], dim=1 if self.is_conv2d else 2)
         cx = self.conditioning2(cx)
@@ -303,7 +303,7 @@ if __name__ == "__main__":
     unet.to("cuda").to(torch.float16)
 
     print("create LoRA controlnet")
-    control_net = LoRAControlNet(unet, 256, 64, 1)
+    control_net = LoRAControlNet(unet, 128, 64, 1)
     control_net.apply_to()
     control_net.to("cuda")