diff --git a/gen_img_diffusers.py b/gen_img_diffusers.py
index 1f103e31..273c0dd8 100644
--- a/gen_img_diffusers.py
+++ b/gen_img_diffusers.py
@@ -937,6 +937,17 @@ class PipelineLike:
         if self.control_nets:
             guided_hints = original_control_net.get_guided_hints(self.control_nets, num_latent_input, batch_size, clip_guide_images)
 
+        if reginonal_network:
+            num_sub_and_neg_prompts = len(text_embeddings) // batch_size
+            # last subprompt and negative prompt
+            text_emb_last = []
+            for j in range(batch_size):
+                text_emb_last.append(text_embeddings[(j + 1) * num_sub_and_neg_prompts - 2])
+                text_emb_last.append(text_embeddings[(j + 1) * num_sub_and_neg_prompts - 1])
+            text_emb_last = torch.stack(text_emb_last)
+        else:
+            text_emb_last = text_embeddings
+            
         for i, t in enumerate(tqdm(timesteps)):
             # expand the latents if we are doing classifier free guidance
             latent_model_input = latents.repeat((num_latent_input, 1, 1, 1))
@@ -944,11 +955,6 @@ class PipelineLike:
 
             # predict the noise residual
             if self.control_nets and self.control_net_enabled:
-                if reginonal_network:
-                    num_sub_and_neg_prompts = len(text_embeddings) // batch_size
-                    text_emb_last = text_embeddings[num_sub_and_neg_prompts - 2 :: num_sub_and_neg_prompts]  # last subprompt
-                else:
-                    text_emb_last = text_embeddings
                 noise_pred = original_control_net.call_unet_and_control_net(
                     i,
                     num_latent_input,
@@ -958,6 +964,7 @@ class PipelineLike:
                     i / len(timesteps),
                     latent_model_input,
                     t,
+                    text_embeddings,
                     text_emb_last,
                 ).sample
             else:
@@ -2746,6 +2753,10 @@ def main(args):
         print(f"iteration {gen_iter+1}/{args.n_iter}")
         iter_seed = random.randint(0, 0x7FFFFFFF)
 
+        # shuffle prompt list
+        if args.shuffle_prompts:
+            random.shuffle(prompt_list)
+
         # バッチ処理の関数
         def process_batch(batch: List[BatchData], highres_fix, highres_1st=False):
             batch_size = len(batch)
@@ -3321,6 +3332,11 @@ def setup_parser() -> argparse.ArgumentParser:
         action="store_true",
         help="use same seed for all prompts in iteration if no seed specified / 乱数seedの指定がないとき繰り返し内はすべて同じseedを使う（プロンプト間の差異の比較用）",
     )
+    parser.add_argument(
+        "--shuffle_prompts",
+        action="store_true",
+        help="shuffle prompts in iteration / 繰り返し内のプロンプトをシャッフルする",
+    )
     parser.add_argument("--fp16", action="store_true", help="use fp16 / fp16を指定し省メモリ化する")
     parser.add_argument("--bf16", action="store_true", help="use bfloat16 / bfloat16を指定し省メモリ化する")
     parser.add_argument("--xformers", action="store_true", help="use xformers / xformersを使用し高速化する")