diff --git a/library/train_util.py b/library/train_util.py
index 631f1cb7..ff1b4a33 100644
--- a/library/train_util.py
+++ b/library/train_util.py
@@ -1770,12 +1770,16 @@ class ControlNetDataset(BaseDataset):
             cond_img = load_image(image_info.cond_img_path)
 
             if self.dreambooth_dataset_delegate.enable_bucket:
-                cond_img = cv2.resize(cond_img, image_info.resized_size, interpolation=cv2.INTER_AREA)  # INTER_AREAでやりたいのでcv2でリサイズ
                 assert (
                     cond_img.shape[0] == original_size_hw[0] and cond_img.shape[1] == original_size_hw[1]
                 ), f"size of conditioning image is not match / 画像サイズが合いません: {image_info.absolute_path}"
-                ct, cl = crop_top_left
+                cond_img = cv2.resize(cond_img, image_info.resized_size, interpolation=cv2.INTER_AREA)  # INTER_AREAでやりたいのでcv2でリサイズ
+
+                # TODO support random crop
+                # 現在サポートしているcropはrandomではなく中央のみ
                 h, w = target_size_hw
+                ct = (cond_img.shape[0] - h) // 2
+                cl = (cond_img.shape[1] - w) // 2
                 cond_img = cond_img[ct : ct + h, cl : cl + w]
             else:
                 # assert (
diff --git a/networks/control_net_lllite.py b/networks/control_net_lllite.py
index 36e36071..3140919c 100644
--- a/networks/control_net_lllite.py
+++ b/networks/control_net_lllite.py
@@ -120,6 +120,7 @@ class LLLiteModule(torch.nn.Module):
         / call the model inside, so if necessary, surround it with torch.no_grad()
         """
         # timestepごとに呼ばれないので、あらかじめ計算しておく / it is not called for each timestep, so calculate it in advance
+        # print(f"C {self.lllite_name}, cond_image.shape={cond_image.shape}")
         cx = self.conditioning1(cond_image)
         if not self.is_conv2d:
             # reshape / b,c,h,w -> b,h*w,c
@@ -146,7 +147,7 @@ class LLLiteModule(torch.nn.Module):
             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.lllite_name}, lx.shape={lx.shape}, cx.shape={cx.shape}")
+        # print(f"C {self.lllite_name}, x.shape={x.shape}, cx.shape={cx.shape}")
 
         # downで入力の次元数を削減し、conditioning image embeddingと結合する
         # 加算ではなくchannel方向に結合することで、うまいこと混ぜてくれることを期待している
diff --git a/sdxl_train_control_net_lllite.py b/sdxl_train_control_net_lllite.py
index 6e5c4232..09cf1643 100644
--- a/sdxl_train_control_net_lllite.py
+++ b/sdxl_train_control_net_lllite.py
@@ -113,6 +113,8 @@ def train(args):
         assert (
             train_dataset_group.is_latent_cacheable()
         ), "when caching latents, either color_aug or random_crop cannot be used / latentをキャッシュするときはcolor_augとrandom_cropは使えません"
+    else:
+        print("WARNING: random_crop is not supported yet for ControlNet training / ControlNetの学習ではrandom_cropはまだサポートされていません")
 
     if args.cache_text_encoder_outputs:
         assert (